@inproceedings{chu_documentation_2027,
	address = {Cham},
	title = {Documentation {Gaps} are the {Primary} {Barrier} to {Reproducing} {Clinical} {ML} {Literature}: {Automated} {Replication} with {VERA}},
	isbn = {978-3-032-30710-1},
	shorttitle = {Documentation {Gaps} are the {Primary} {Barrier} to {Reproducing} {Clinical} {ML} {Literature}},
	doi = {10.1007/978-3-032-30710-1_49},
	abstract = {Machine learning models are increasingly embedded in clinical decision support systems (CDSS) for triage, risk stratification, and precision medicine, where patients’ lives depend on model reliability. Yet the scientific literature underpinning these tools is rarely verified independently, and recent evidence reveals that even FDA-authorized AI devices may lack adequate clinical validation. We present VERA (Verification Engine for Reproducible Analysis), an automated system that takes a research paper PDF and attempts end-to-end replication, enabling systematic trust verification of the clinical ML literature. VERA integrates hybrid PDF extraction, dual-path dataset resolution (UCI Repository and dynamic MIMIC-III cohort construction), deterministic execution, and quantitative scoring.},
	language = {en},
	booktitle = {Artificial {Intelligence} in {Medicine}},
	publisher = {Springer Nature Switzerland},
	author = {Chu, Andre and Pettine, Warren Woodrich},
	editor = {Andreev, Pavel and Van Woensel, William and Holmes, John and Sauré, Antoine},
	year = {2027},
	keywords = {Automatic replication, Clinical decision support systems, Machine Learning, Practice guidelines, Reproducibility, Triage},
	pages = {412--422},
}

Machine learning models are increasingly embedded in clinical decision support systems (CDSS) for triage, risk stratification, and precision medicine, where patients’ lives depend on model reliability. Yet the scientific literature underpinning these tools is rarely verified independently, and recent evidence reveals that even FDA-authorized AI devices may lack adequate clinical validation. We present VERA (Verification Engine for Reproducible Analysis), an automated system that takes a research paper PDF and attempts end-to-end replication, enabling systematic trust verification of the clinical ML literature. VERA integrates hybrid PDF extraction, dual-path dataset resolution (UCI Repository and dynamic MIMIC-III cohort construction), deterministic execution, and quantitative scoring.

@misc{haque_timesynth_2026,
	title = {Timesynth: {A} {Temporal} {Fidelity} {Framework} for {Health} {Signal} {Digital} {Twins}},
	shorttitle = {Timesynth},
	url = {http://arxiv.org/abs/2607.00431},
	doi = {10.48550/arXiv.2607.00431},
	abstract = {Forecasting models for health-signal digital twins must preserve the oscillatory, frequency, phase, and state-transition dynamics of physiological signals, yet the pointwise metrics used to benchmark them cannot detect when these fundamental properties are lost. We show that this blind spot misranks models: across 11 architectures, models with comparable pointwise error diverge by up to 53° in phase accuracy, equivalent to roughly 123 ms for a 1.2 Hz cardiac rhythm and invisible to standard metrics. To enable development of models that escape such failures, we introduce TimeSynth, a controlled benchmarking framework with two reusable components: a physiologically grounded generator producing signals with analytically known ground-truth dynamics from parametric models fitted to real electroencephalography, electrocardiography and photoplethysmogram signals, along with diagnostics quantifying amplitude, frequency, phase, and state-transition fidelity. Linear and full-sequence attention models systematically lose frequency and phase information despite acceptable amplitude error, whereas architectures with localized temporal structure better preserve dynamical fidelity and adapt to observable state transitions; none, however, reliably preserves stochastic switching. Because the dominant determinant of fidelity is architectural, model choice becomes a principled, use-case-driven decision rather than a search for a single winner. TimeSynth thus supplies the controlled preclinical stress test missing before models are coupled to patient data, with a reusable generator and diagnostics for fidelity-aware development.},
	urldate = {2026-07-28},
	publisher = {arXiv},
	author = {Haque, Md Rakibul and Elhabian, Shireen and Pettine, Warren Woodrich},
	month = jul,
	year = {2026},
	note = {arXiv:2607.00431 [cs.LG]},
	keywords = {Computer Science - Machine Learning},
}

Forecasting models for health-signal digital twins must preserve the oscillatory, frequency, phase, and state-transition dynamics of physiological signals, yet the pointwise metrics used to benchmark them cannot detect when these fundamental properties are lost. We show that this blind spot misranks models: across 11 architectures, models with comparable pointwise error diverge by up to 53° in phase accuracy, equivalent to roughly 123 ms for a 1.2 Hz cardiac rhythm and invisible to standard metrics. To enable development of models that escape such failures, we introduce TimeSynth, a controlled benchmarking framework with two reusable components: a physiologically grounded generator producing signals with analytically known ground-truth dynamics from parametric models fitted to real electroencephalography, electrocardiography and photoplethysmogram signals, along with diagnostics quantifying amplitude, frequency, phase, and state-transition fidelity. Linear and full-sequence attention models systematically lose frequency and phase information despite acceptable amplitude error, whereas architectures with localized temporal structure better preserve dynamical fidelity and adapt to observable state transitions; none, however, reliably preserves stochastic switching. Because the dominant determinant of fidelity is architectural, model choice becomes a principled, use-case-driven decision rather than a search for a single winner. TimeSynth thus supplies the controlled preclinical stress test missing before models are coupled to patient data, with a reusable generator and diagnostics for fidelity-aware development.

@misc{pozo_decision_2026,
	title = {Decision {Curve} {Analysis} for {Evaluating} {Machine} {Learning} {Models} for {Next}-{Day} {Transfer} {Out} of {ICU}},
	copyright = {© 2026, Posted by openRxiv. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
	url = {https://www.medrxiv.org/content/10.64898/2026.04.19.26351213v1},
	doi = {10.64898/2026.04.19.26351213},
	abstract = {Timely identification of intensive care unit (ICU) patients likely to exit the unit can support anticipatory workflows such as chart review, eligibility screening, and patient outreach prior to transfer. Most ICU discharge prediction studies report discrimination and calibration, but these metrics do not quantify the decision consequences of acting on predictions. Using adult ICU admissions from MIMIC-IV, we represented each ICU stay as a sequence of daily clinical summaries and trained logistic regression, random forest, and XGBoost models to predict next day ICU transfer. Models achieved ROC AUC of 0.80 to 0.84 with differing calibration. We evaluated decision utility using decision curve analysis (DCA), where positive predictions trigger proactive review. Across thresholds, model guided strategies outperformed review-all, review-none, and a simple clinical rule. To translate net benefit into implementable operations, we modeled a clinical trial recruitment workflow with an 8 hour daily time constraint, incorporating chart review and consent effort. At a feasible operating threshold (0.23), the model flagged 23 charts/day and yielded 1.23 enrollments/day under conservative eligibility and consent assumptions. These results demonstrate that DCA provides a transparent framework for determining when ICU transfer predictions are worth using and how thresholds should be selected to align with real world workflow constraints.},
	language = {en},
	urldate = {2026-04-21},
	publisher = {medRxiv},
	author = {Pozo, Margaret and Pape, Abigail and Locke, Brian and Pettine, Warren Woodrich},
	month = apr,
	year = {2026},
	note = {ISSN: 3067-2007
Pages: 2026.04.19.26351213},
}

Timely identification of intensive care unit (ICU) patients likely to exit the unit can support anticipatory workflows such as chart review, eligibility screening, and patient outreach prior to transfer. Most ICU discharge prediction studies report discrimination and calibration, but these metrics do not quantify the decision consequences of acting on predictions. Using adult ICU admissions from MIMIC-IV, we represented each ICU stay as a sequence of daily clinical summaries and trained logistic regression, random forest, and XGBoost models to predict next day ICU transfer. Models achieved ROC AUC of 0.80 to 0.84 with differing calibration. We evaluated decision utility using decision curve analysis (DCA), where positive predictions trigger proactive review. Across thresholds, model guided strategies outperformed review-all, review-none, and a simple clinical rule. To translate net benefit into implementable operations, we modeled a clinical trial recruitment workflow with an 8 hour daily time constraint, incorporating chart review and consent effort. At a feasible operating threshold (0.23), the model flagged 23 charts/day and yielded 1.23 enrollments/day under conservative eligibility and consent assumptions. These results demonstrate that DCA provides a transparent framework for determining when ICU transfer predictions are worth using and how thresholds should be selected to align with real world workflow constraints.

@article{nemteanu_scalable_2026,
	title = {Scalable {Agile} {Framework} for {Execution} in {AI} for {Medical} {AI} {Ethics} {Policy} {Design} in {Small}- and {Medium}-{Sized} {Enterprises}},
	volume = {28},
	url = {https://www.jmir.org/2026/1/e80028},
	doi = {10.2196/80028},
	abstract = {Artificial intelligence (AI) is transforming patient care, but it also raises ethical questions, such as bias and transparency. While a range of well-established frameworks exist to guide responsible AI practice, most were designed for academic or regulatory settings and can be hard to operationalize within fast-moving, resource-limited small and medium-sized enterprises (SMEs). We report on the collaborative design of the SAFE-AI (Scalable Agile Framework for Execution in AI), an approach that embeds ethical safeguards, including fairness, transparency, responsibility metrics, and continuous monitoring, directly into standard Agile development cycles. In keeping with established Agile principles, SAFE-AI provides “just enough structure” to integrate ethical oversight into existing workflows without prescribing extensive new governance layers. Similar to other Agile frameworks, such as Scrum, which is described as a “lightweight framework” designed to help teams solve complex problems through iterative learning and minimal process overhead, SAFE-AI aims to remain practical for organizations that may not have dedicated ethics or compliance staff. Rather than simplifying technical methods, SAFE-AI simplifies when and how ethical review is triggered and documented, making responsible AI practices feasible even in environments with limited ethics, governance, or compliance resources. SAFE-AI assumes the presence of qualified data scientists and engineers, and it does not replace the need for statistical or technical expertise but instead provides a lightweight structure for coordinating and documenting work that those experts already perform. We followed a design-science, practice-oriented approach over 20 weeks. After a discovery workshop, a cross-functional team was assembled that included SME employees, ethics researchers, and academic partners. The SME’s role was limited to informing design constraints and feasibility considerations during the co-design phase. No operational pilot or production deployment was conducted as part of this study. To reduce the risk of internal design bias and improve generalizability, we also consulted external stakeholders through structured feedback sessions, including clinicians, health care domain experts, and regulatory specialists. Their feedback was incorporated into each prototype-feedback cycle, ensuring that priorities reflected not only the SME’s immediate context but also broader clinical and regulatory perspectives. The co-design process produced a 4-phase SAFE-AI life cycle: discovery, assessment, development, and monitoring. SAFE-AI’s phase-specific checklists meld acceptance, fairness, and transparency metrics into each Agile sprint. A novel scenario-based probability analogy mapping method was added to translate model risk and uncertainty into plain-language narratives for nontechnical stakeholders, forming the framework’s core “responsibility metrics” layer. SAFE-AI is presented as a proposed framework showing that meaningful ethical safeguards can be embedded easily within common workflows used by SMEs that already use basic Agile or iterative development practices. Its checklist-driven phases and automatic review triggers provide a defensible way to track fairness, transparency, and responsibility throughout the model lifecycle.},
	language = {EN},
	number = {1},
	urldate = {2026-02-26},
	journal = {Journal of Medical Internet Research},
	publisher = {JMIR Publications Inc., Toronto, Canada},
	author = {Nemteanu, Ion and Jr, Adir Mancebo and Joe, Leslie and Lopez, Ryan and Lopez, Patricia and Pettine, Warren Woodrich},
	month = feb,
	year = {2026},
	pages = {e80028},
}

Artificial intelligence (AI) is transforming patient care, but it also raises ethical questions, such as bias and transparency. While a range of well-established frameworks exist to guide responsible AI practice, most were designed for academic or regulatory settings and can be hard to operationalize within fast-moving, resource-limited small and medium-sized enterprises (SMEs). We report on the collaborative design of the SAFE-AI (Scalable Agile Framework for Execution in AI), an approach that embeds ethical safeguards, including fairness, transparency, responsibility metrics, and continuous monitoring, directly into standard Agile development cycles. In keeping with established Agile principles, SAFE-AI provides “just enough structure” to integrate ethical oversight into existing workflows without prescribing extensive new governance layers. Similar to other Agile frameworks, such as Scrum, which is described as a “lightweight framework” designed to help teams solve complex problems through iterative learning and minimal process overhead, SAFE-AI aims to remain practical for organizations that may not have dedicated ethics or compliance staff. Rather than simplifying technical methods, SAFE-AI simplifies when and how ethical review is triggered and documented, making responsible AI practices feasible even in environments with limited ethics, governance, or compliance resources. SAFE-AI assumes the presence of qualified data scientists and engineers, and it does not replace the need for statistical or technical expertise but instead provides a lightweight structure for coordinating and documenting work that those experts already perform. We followed a design-science, practice-oriented approach over 20 weeks. After a discovery workshop, a cross-functional team was assembled that included SME employees, ethics researchers, and academic partners. The SME’s role was limited to informing design constraints and feasibility considerations during the co-design phase. No operational pilot or production deployment was conducted as part of this study. To reduce the risk of internal design bias and improve generalizability, we also consulted external stakeholders through structured feedback sessions, including clinicians, health care domain experts, and regulatory specialists. Their feedback was incorporated into each prototype-feedback cycle, ensuring that priorities reflected not only the SME’s immediate context but also broader clinical and regulatory perspectives. The co-design process produced a 4-phase SAFE-AI life cycle: discovery, assessment, development, and monitoring. SAFE-AI’s phase-specific checklists meld acceptance, fairness, and transparency metrics into each Agile sprint. A novel scenario-based probability analogy mapping method was added to translate model risk and uncertainty into plain-language narratives for nontechnical stakeholders, forming the framework’s core “responsibility metrics” layer. SAFE-AI is presented as a proposed framework showing that meaningful ethical safeguards can be embedded easily within common workflows used by SMEs that already use basic Agile or iterative development practices. Its checklist-driven phases and automatic review triggers provide a defensible way to track fairness, transparency, and responsibility throughout the model lifecycle.

@misc{haque_timesynth_2026-1,
	title = {{TimeSynth}: {A} {Framework} for {Uncovering} {Systematic} {Biases} in {Time} {Series} {Forecasting}},
	shorttitle = {{TimeSynth}},
	url = {https://arxiv.org/abs/2602.11413v1},
	doi = {10.48550/arXiv.2602.11413},
	abstract = {Time series forecasting is a fundamental tool with wide ranging applications, yet recent debates question whether complex nonlinear architectures truly outperform simple linear models. Prior claims of dominance of the linear model often stem from benchmarks that lack diverse temporal dynamics and employ biased evaluation protocols. We revisit this debate through TimeSynth, a structured framework that emulates key properties of real world time series,including non-stationarity, periodicity, trends, and phase modulation by creating synthesized signals whose parameters are derived from real-world time series. Evaluating four model families Linear, Multi Layer Perceptrons (MLP), Convolutional Neural Networks (CNNs), and Transformers, we find a systematic bias in linear models: they collapse to simple oscillation regardless of signal complexity. Nonlinear models avoid this collapse and gain clear advantages as signal complexity increases. Notably, Transformers and CNN based models exhibit slightly greater adaptability to complex modulated signals compared to MLPs. Beyond clean forecasting, the framework highlights robustness differences under distribution and noise shifts and removes biases of prior benchmarks by using independent instances for train, test, and validation for each signal family. Collectively, TimeSynth provides a principled foundation for understanding when different forecasting approaches succeed or fail, moving beyond oversimplified claims of model equivalence.},
	language = {en},
	urldate = {2026-02-15},
	publisher = {arXiv},
	author = {Haque, Md Rakibul and Goudar, Vishwa and Elhabian, Shireen and Pettine, Warren Woodrich},
	month = feb,
	year = {2026},
}

Time series forecasting is a fundamental tool with wide ranging applications, yet recent debates question whether complex nonlinear architectures truly outperform simple linear models. Prior claims of dominance of the linear model often stem from benchmarks that lack diverse temporal dynamics and employ biased evaluation protocols. We revisit this debate through TimeSynth, a structured framework that emulates key properties of real world time series,including non-stationarity, periodicity, trends, and phase modulation by creating synthesized signals whose parameters are derived from real-world time series. Evaluating four model families Linear, Multi Layer Perceptrons (MLP), Convolutional Neural Networks (CNNs), and Transformers, we find a systematic bias in linear models: they collapse to simple oscillation regardless of signal complexity. Nonlinear models avoid this collapse and gain clear advantages as signal complexity increases. Notably, Transformers and CNN based models exhibit slightly greater adaptability to complex modulated signals compared to MLPs. Beyond clean forecasting, the framework highlights robustness differences under distribution and noise shifts and removes biases of prior benchmarks by using independent instances for train, test, and validation for each signal family. Collectively, TimeSynth provides a principled foundation for understanding when different forecasting approaches succeed or fail, moving beyond oversimplified claims of model equivalence.

@misc{chu_mmint-labvera_2026,
	title = {mmint-lab/{VERA}: {VERA}: {Autonomous} {Replication} and {Validation} of {Machine} {Learning} {Experiments} from {Research} {Papers}},
	shorttitle = {mmint-lab/{VERA}},
	url = {https://zenodo.org/records/18165553},
	doi = {10.5281/zenodo.18165553},
	abstract = {v1.0.0 - Initial Release



First public release of VERA (Verification Engine for Reproducible Analysis)


VERA is an autonomous agent that extracts, replicates, and validates machine learning experiments from research papers without manual intervention. Simply provide a PDF, and VERA handles extraction, execution, and comprehensive comparison reporting in {\textasciitilde}35 seconds.



Highlights



100\% extraction accuracy across 4 validated research papers (32 models total)

82\% average Results Match score with paper-reported benchmarks

Zero manual intervention required during execution

Full transparency with reviewable standalone Python scripts ({\textasciitilde}250 lines per paper)

Web dashboard + CLI interface included




Features

Core Capabilities



Autonomous PDF extraction with hybrid rule-based + semantic NLP

UCI dataset auto-resolution (20+ datasets)

14+ ML models (SVM, Random Forest, XGBoost, LightGBM, CatBoost, etc.)

Ensemble model support (e.g., "AdaBoost+LR")

Hyperparameter extraction (30+ patterns)

4-component scoring: EQ, CQ, RM, E2E

Interactive visualizations (heatmaps, delta charts, slopegraphs)


Interfaces



CLI: python -m vera --pdf paper.pdf --output\_dir results/

Web UI: React dashboard with FastAPI backend


Output Artifacts



COMPREHENSIVE\_REPORT.html - Interactive report

replication\_standalone.py - Reviewable code

results.json - Structured benchmarks

blueprint.md - Extraction details

figures/ - Publication-quality visualizations




Quick Start

Requirements



Python 3.11+

4GB RAM

Internet connection


Installation

\# Create environment
conda create -n vera python=3.12 -y
conda activate vera

\# Install dependencies
pip install -r requirements.txt

\# Optional: Install boosting libraries for better RM scores
pip install xgboost lightgbm catboost


Run Demo

\# CLI
python -m vera --pdf test\_papers/HeartDiseaseProj.pdf --output\_dir demo/heart\_disease
open demo/heart\_disease/article/COMPREHENSIVE\_REPORT.html

\# Web Interface
python -m vera web  \# Terminal 1
cd frontend \&\& npm install \&\& npm run dev  \# Terminal 2
\# Open http://localhost:5173




Validation Results

{\textbar} Metric {\textbar} Value {\textbar}
{\textbar}--------{\textbar}-------{\textbar}
{\textbar} Papers Tested {\textbar} 4 {\textbar}
{\textbar} Models Extracted {\textbar} 32 {\textbar}
{\textbar} Extraction Accuracy {\textbar} 100\% {\textbar}
{\textbar} Code Success Rate {\textbar} 100\% {\textbar}
{\textbar} Avg RM Score {\textbar} 82\% {\textbar}
{\textbar} Avg Execution Time {\textbar} {\textasciitilde}35s {\textbar}

Demo Papers Included



Heart Disease (10 models, 82\% RM)

Breast Cancer (12 models, 84\% RM)

Cardiovascular Disease (9 models, 77\% RM)

Diabetes SVM (1 model, 84\% RM)




Known Limitations



UCI datasets only (no Kaggle, HuggingFace, custom data)

Tabular ML only (no PyTorch/TensorFlow deep learning)

Machine-readable PDFs only (no OCR)

Binary classification focus (limited multi-class)




Documentation



README.md - Quick start guide

BLOG.md - Vision and detailed walkthrough

TECHNICAL\_BLOG.md - Technical deep-dive

demo/ - 4 pre-run demo outputs




What's Next

See ROADMAP.md for planned enhancements:



LLM extraction fallback

Deep learning support

OCR for scanned papers

Batch processing

Extended dataset coverage




Authors

Andre Chu and Warren Pettine
Medical Machine Intelligence Lab, Department of Psychiatry, University of Utah

Contact: andre.chu@utah.edu



License

Academic research tool. Not for clinical use.



Acknowledgments

This is a proof-of-concept demonstrating autonomous replication feasibility. It should complement, not replace, human review and expertise.},
	urldate = {2026-01-06},
	publisher = {Zenodo},
	author = {Chu, Andre},
	month = jan,
	year = {2026},
}

v1.0.0 - Initial Release First public release of VERA (Verification Engine for Reproducible Analysis) VERA is an autonomous agent that extracts, replicates, and validates machine learning experiments from research papers without manual intervention. Simply provide a PDF, and VERA handles extraction, execution, and comprehensive comparison reporting in ~35 seconds. Highlights 100% extraction accuracy across 4 validated research papers (32 models total) 82% average Results Match score with paper-reported benchmarks Zero manual intervention required during execution Full transparency with reviewable standalone Python scripts (~250 lines per paper) Web dashboard + CLI interface included Features Core Capabilities Autonomous PDF extraction with hybrid rule-based + semantic NLP UCI dataset auto-resolution (20+ datasets) 14+ ML models (SVM, Random Forest, XGBoost, LightGBM, CatBoost, etc.) Ensemble model support (e.g., "AdaBoost+LR") Hyperparameter extraction (30+ patterns) 4-component scoring: EQ, CQ, RM, E2E Interactive visualizations (heatmaps, delta charts, slopegraphs) Interfaces CLI: python -m vera –pdf paper.pdf –output_dir results/ Web UI: React dashboard with FastAPI backend Output Artifacts COMPREHENSIVE_REPORT.html - Interactive report replication_standalone.py - Reviewable code results.json - Structured benchmarks blueprint.md - Extraction details figures/ - Publication-quality visualizations Quick Start Requirements Python 3.11+ 4GB RAM Internet connection Installation # Create environment conda create -n vera python=3.12 -y conda activate vera # Install dependencies pip install -r requirements.txt # Optional: Install boosting libraries for better RM scores pip install xgboost lightgbm catboost Run Demo # CLI python -m vera –pdf test_papers/HeartDiseaseProj.pdf –output_dir demo/heart_disease open demo/heart_disease/article/COMPREHENSIVE_REPORT.html # Web Interface python -m vera web # Terminal 1 cd frontend && npm install && npm run dev # Terminal 2 # Open http://localhost:5173 Validation Results \textbar Metric \textbar Value \textbar \textbar——–\textbar——-\textbar \textbar Papers Tested \textbar 4 \textbar \textbar Models Extracted \textbar 32 \textbar \textbar Extraction Accuracy \textbar 100% \textbar \textbar Code Success Rate \textbar 100% \textbar \textbar Avg RM Score \textbar 82% \textbar \textbar Avg Execution Time \textbar ~35s \textbar Demo Papers Included Heart Disease (10 models, 82% RM) Breast Cancer (12 models, 84% RM) Cardiovascular Disease (9 models, 77% RM) Diabetes SVM (1 model, 84% RM) Known Limitations UCI datasets only (no Kaggle, HuggingFace, custom data) Tabular ML only (no PyTorch/TensorFlow deep learning) Machine-readable PDFs only (no OCR) Binary classification focus (limited multi-class) Documentation README.md - Quick start guide BLOG.md - Vision and detailed walkthrough TECHNICAL_BLOG.md - Technical deep-dive demo/ - 4 pre-run demo outputs What's Next See ROADMAP.md for planned enhancements: LLM extraction fallback Deep learning support OCR for scanned papers Batch processing Extended dataset coverage Authors Andre Chu and Warren Pettine Medical Machine Intelligence Lab, Department of Psychiatry, University of Utah Contact: andre.chu@utah.edu License Academic research tool. Not for clinical use. Acknowledgments This is a proof-of-concept demonstrating autonomous replication feasibility. It should complement, not replace, human review and expertise.

@article{locke_consistency_2025,
	title = {The {Consistency} of {Hypercapnic} {Respiratory} {Failure} {Case} {Definitions} in {Electronic} {Health} {Record} {Data}},
	issn = {1931-3543},
	doi = {10.1016/j.chest.2025.08.002},
	abstract = {BACKGROUND: Several studies suggest a high prevalence and disease burden associated with hypercapnic respiratory failure of any cause in emergency department (ED) and inpatient settings. However, these studies use different case definitions. The consistency and validity of these case definitions have not been assessed.
RESEARCH QUESTIONS: Do the various case definitions from health record-based studies of adults with hypercapnic respiratory failure identify the same, or similar, patients?
METHODS: We identified case definitions for hypercapnic respiratory failure from the peer-reviewed literature. We constructed an emulation dataset enriched for the presence of hypercapnic respiratory failure from TriNetX, which aggregates health records from 76 hospitals in the United States. Adult ED and inpatient encounters occurring in 2022 were eligible for inclusion. We assessed consistency among case definitions by applying them to the emulation dataset on the day of ED or hospital admission. We calculated Cohen's kappa (κ) and compared the patient characteristics and short-term outcomes for the cohorts defined by each case definition. We evaluated the accuracy of hypercapnic respiratory failure diagnosis codes for capturing hypercapnia demonstrated on first-day arterial blood gas samples.
RESULTS: Ten case definitions for hypercapnic respiratory failure were identified and amenable to emulation. The emulation dataset contained 515,286 encounters. Case definitions showed limited agreement (median κ = 0.35, interquartile range 0.21-0.56) for hypercapnic respiratory failure identification. Key demographic characteristics, comorbidities, and outcomes such as receiving ventilatory support (range 29-100\%) and 30-day mortality (range 18-35\%) varied substantially between the resulting cohorts. Diagnosis codes were insensitive for capturing first-day arterial blood gas-demonstrated hypercapnia (sensitivity 23.5\%, CI 23.1 to 23.8\%).
INTERPRETATION: Case definitions from studies of hypercapnic respiratory failure identify patients with widely different attributes and outcomes, thereby hampering interpretation of associated study findings. Standardization of case definitions is necessary to improve the rigor and generalizability of research on hypercapnic respiratory failure.},
	language = {eng},
	journal = {Chest},
	author = {Locke, Brian W. and Richards, W. Wayne and Gouripeddi, Ramkiran and Brown, Jeanette P. and Finkelstein, Joseph and Sundar, Krishna M. and Peltan, Ithan D. and Brown, Samuel M.},
	month = aug,
	year = {2025},
	keywords = {Case Definition, Hypercapnic Respiratory Failure, Hypercarbia, Hypoventilation, Research Methods},
	pages = {S0012--3692(25)05126--8},
}

BACKGROUND: Several studies suggest a high prevalence and disease burden associated with hypercapnic respiratory failure of any cause in emergency department (ED) and inpatient settings. However, these studies use different case definitions. The consistency and validity of these case definitions have not been assessed. RESEARCH QUESTIONS: Do the various case definitions from health record-based studies of adults with hypercapnic respiratory failure identify the same, or similar, patients? METHODS: We identified case definitions for hypercapnic respiratory failure from the peer-reviewed literature. We constructed an emulation dataset enriched for the presence of hypercapnic respiratory failure from TriNetX, which aggregates health records from 76 hospitals in the United States. Adult ED and inpatient encounters occurring in 2022 were eligible for inclusion. We assessed consistency among case definitions by applying them to the emulation dataset on the day of ED or hospital admission. We calculated Cohen's kappa (κ) and compared the patient characteristics and short-term outcomes for the cohorts defined by each case definition. We evaluated the accuracy of hypercapnic respiratory failure diagnosis codes for capturing hypercapnia demonstrated on first-day arterial blood gas samples. RESULTS: Ten case definitions for hypercapnic respiratory failure were identified and amenable to emulation. The emulation dataset contained 515,286 encounters. Case definitions showed limited agreement (median κ = 0.35, interquartile range 0.21-0.56) for hypercapnic respiratory failure identification. Key demographic characteristics, comorbidities, and outcomes such as receiving ventilatory support (range 29-100%) and 30-day mortality (range 18-35%) varied substantially between the resulting cohorts. Diagnosis codes were insensitive for capturing first-day arterial blood gas-demonstrated hypercapnia (sensitivity 23.5%, CI 23.1 to 23.8%). INTERPRETATION: Case definitions from studies of hypercapnic respiratory failure identify patients with widely different attributes and outcomes, thereby hampering interpretation of associated study findings. Standardization of case definitions is necessary to improve the rigor and generalizability of research on hypercapnic respiratory failure.

@misc{nemteanu_practical_2025,
	title = {A {Practical} {SAFE}-{AI} {Framework} for {Small} and {Medium}-{Sized} {Enterprises} {Developing} {Medical} {Artificial} {Intelligence} {Ethics} {Policies}},
	url = {http://arxiv.org/abs/2507.01304},
	doi = {10.48550/arXiv.2507.01304},
	abstract = {Artificial intelligence (AI) offers incredible possibilities for patient care, but raises significant ethical issues, such as the potential for bias. Powerful ethical frameworks exist to minimize these issues, but are often developed for academic or regulatory environments and tend to be comprehensive but overly prescriptive, making them difficult to operationalize within fast-paced, resource-constrained environments. We introduce the Scalable Agile Framework for Execution in AI (SAFE-AI) designed to balance ethical rigor with business priorities by embedding ethical oversight into standard Agile-based product development workflows. The framework emphasizes the early establishment of testable acceptance criteria, fairness metrics, and transparency metrics to manage model uncertainty, while also promoting continuous monitoring and re-evaluation of these metrics across the AI lifecycle. A core component of this framework are responsibility metrics using scenario-based probability analogy mapping designed to enhance transparency and stakeholder trust. This ensures that retraining or tuning activities are subject to lightweight but meaningful ethical review. By focusing on the minimum necessary requirements for responsible development, our framework offers a scalable, business-aligned approach to ethical AI suitable for organizations without dedicated ethics teams.},
	urldate = {2025-07-08},
	publisher = {arXiv},
	author = {Nemteanu, Ion and Jr, Adir Mancebo and Joe, Leslie and Lopez, Ryan and Lopez, Patricia and Pettine, Warren Woodrich},
	month = jul,
	year = {2025},
	note = {arXiv:2507.01304 [cs]},
	keywords = {Computer Science - Computers and Society},
}

Artificial intelligence (AI) offers incredible possibilities for patient care, but raises significant ethical issues, such as the potential for bias. Powerful ethical frameworks exist to minimize these issues, but are often developed for academic or regulatory environments and tend to be comprehensive but overly prescriptive, making them difficult to operationalize within fast-paced, resource-constrained environments. We introduce the Scalable Agile Framework for Execution in AI (SAFE-AI) designed to balance ethical rigor with business priorities by embedding ethical oversight into standard Agile-based product development workflows. The framework emphasizes the early establishment of testable acceptance criteria, fairness metrics, and transparency metrics to manage model uncertainty, while also promoting continuous monitoring and re-evaluation of these metrics across the AI lifecycle. A core component of this framework are responsibility metrics using scenario-based probability analogy mapping designed to enhance transparency and stakeholder trust. This ensures that retraining or tuning activities are subject to lightweight but meaningful ethical review. By focusing on the minimum necessary requirements for responsible development, our framework offers a scalable, business-aligned approach to ethical AI suitable for organizations without dedicated ethics teams.

@article{carr_use_2025,
	title = {Use of {High}-{Dose} {Hydroxocobalamin} for {Septic} {Shock}},
	volume = {0},
	issn = {0012-3692},
	url = {https://journal.chestnet.org/article/S0012-3692(25)00129-1/abstract},
	doi = {10.1016/j.chest.2025.01.016},
	abstract = {Septic shock, a global health burden with a case fatality rate exceeding 40\%, is initiated
and maintained by circulating nitric oxide and hydrogen sulfide that are emergent
targets of therapeutic interest.1 High-dose (5-gm) hydroxocobalamin (vitamin B12)
(HD-HOC) scavenges circulating nitric oxide and hydrogen sulfide and may attenuate
the inflammatory response in early septic shock.2 Since 2014, several case reports
and case series have reported that HD-HOC is associated with reduced vasopressor requirements
in adults with refractory vasodilatory shock (including septic shock).},
	language = {English},
	number = {0},
	urldate = {2025-04-25},
	journal = {CHEST},
	publisher = {Elsevier},
	author = {Carr, Jason R. and Locke, Brian and Patel, Jayshil J. and Peltan, Ithan D. and Brown, Samuel M. and Bosch, Nicholas A. and Law, Anica C.},
	month = jan,
	year = {2025},
}

Septic shock, a global health burden with a case fatality rate exceeding 40%, is initiated and maintained by circulating nitric oxide and hydrogen sulfide that are emergent targets of therapeutic interest.1 High-dose (5-gm) hydroxocobalamin (vitamin B12) (HD-HOC) scavenges circulating nitric oxide and hydrogen sulfide and may attenuate the inflammatory response in early septic shock.2 Since 2014, several case reports and case series have reported that HD-HOC is associated with reduced vasopressor requirements in adults with refractory vasodilatory shock (including septic shock).

@article{anderson-bell_seeing_2025,
	title = {Seeing the random forest for the {Zzz}’s: machine learning’s new role in sleep science},
	copyright = {© 2025 American Academy of Sleep Medicine},
	shorttitle = {Seeing the random forest for the {Zzz}’s},
	url = {https://jcsm.aasm.org/doi/10.5664/jcsm.11674},
	doi = {10.5664/jcsm.11674},
	abstract = {The official peer-reviewed journal of the American Academy of Sleep Medicine.},
	language = {EN},
	urldate = {2025-04-25},
	journal = {Journal of Clinical Sleep Medicine},
	publisher = {American Academy of Sleep Medicine},
	author = {Anderson-Bell, Dustin and Locke, Brian W.},
	month = mar,
	year = {2025},
}

The official peer-reviewed journal of the American Academy of Sleep Medicine.

@misc{pettine_dont_2025,
	title = {Don't follow your gut when designing a medical {AI} product - ask these questions},
	url = {https://www.linkedin.com/pulse/dont-follow-your-gut-when-designing-medical-ai-product-warren-pettine-pdiqc/?trackingId=P2lAd4rhtSjgQJsbseEoMg%3D%3D},
	journal = {LinkedIn},
	author = {Pettine, Warren},
	month = jan,
	year = {2025},
}

@inproceedings{christenson_ai-enhanced_2024,
	title = {{AI}-{ENHANCED} {EMERGENCY} {SEVERITY} {INDEX} {FOR} {COMPREHENSIVE} {ED} {TRIAGE}},
	url = {https://figshare.com/articles/poster/AI-ENHANCED_EMERGENCY_SEVERITY_INDEX_FOR_COMPREHENSIVE_ED_TRIAGE/27755895/1},
	doi = {10.6084/m9.figshare.27755895.v1},
	abstract = {Background: Emergency Department (ED) triage systems, such as the Emergency Severity Index (ESI), are crucial for patient prioritization but have limitations in predicting outcomes and optimizing resource allocation. AI-enhanced triage systems offer potential improvements in accuracy, efficiency, and patient care.Objectives: To develop an optimized AI-enhanced ESI score using the MIMIC-IV ED dataset, integrating patient history, real-time vital signs, model predictions, and traditional nurse-assigned ESI to create a more comprehensive, dynamic, and accurate triage tool.Methods: We analyzed over 400,000 ED visits from the MIMIC-IV dataset. Using a customized autoML pipeline, we engineered features from triage variables, patient history, and temporal vital sign patterns. Multiple machine learning models, including XGBoost and MLPs, were trained to predict various outcomes. These predictions, along with the nurse-assigned ESI and real-time patient data, were then combined to create an optimized AI ESI score. Performance was evaluated using AUC, calibration metrics, and compared to traditional ESI scoring.Results: Individual models demonstrated high performance in predicting key outcomes: hospitalization (AUC: 0.85), critical outcomes (ICU admission/mortality within 12 hours, AUC: 0.88), and ED reattendance (AUC: 0.75). These models, triage variables, and vital sign patterns were combined into a single AI-enhanced ESI score, which showed stronger correlation with critical outcomes compared to the nurse-assigned ESI. This combined score allowed for more precise patient ranking than the traditional 5-level ESI system, effectively refining the nurse-assigned ESI.Conclusion: This AI-enhanced ESI system demonstrates significant improvements over traditional methods by integrating patient information with model predictions into a single, comprehensive, and dynamic triage score. It has the potential to optimize resource allocation, improve patient flow, and enhance overall patient care in EDs. Future work will focus on prospective validation, seamless integration into clinical workflows, and assessment of its impact on patient outcomes and ED efficiency.},
	language = {en},
	urldate = {2025-01-14},
	publisher = {figshare},
	author = {Christenson, Matthias P. and Locke, Brian and Koirala, Pranav and Pettine, Warren Woodrich},
	month = nov,
	year = {2024},
}

Background: Emergency Department (ED) triage systems, such as the Emergency Severity Index (ESI), are crucial for patient prioritization but have limitations in predicting outcomes and optimizing resource allocation. AI-enhanced triage systems offer potential improvements in accuracy, efficiency, and patient care.Objectives: To develop an optimized AI-enhanced ESI score using the MIMIC-IV ED dataset, integrating patient history, real-time vital signs, model predictions, and traditional nurse-assigned ESI to create a more comprehensive, dynamic, and accurate triage tool.Methods: We analyzed over 400,000 ED visits from the MIMIC-IV dataset. Using a customized autoML pipeline, we engineered features from triage variables, patient history, and temporal vital sign patterns. Multiple machine learning models, including XGBoost and MLPs, were trained to predict various outcomes. These predictions, along with the nurse-assigned ESI and real-time patient data, were then combined to create an optimized AI ESI score. Performance was evaluated using AUC, calibration metrics, and compared to traditional ESI scoring.Results: Individual models demonstrated high performance in predicting key outcomes: hospitalization (AUC: 0.85), critical outcomes (ICU admission/mortality within 12 hours, AUC: 0.88), and ED reattendance (AUC: 0.75). These models, triage variables, and vital sign patterns were combined into a single AI-enhanced ESI score, which showed stronger correlation with critical outcomes compared to the nurse-assigned ESI. This combined score allowed for more precise patient ranking than the traditional 5-level ESI system, effectively refining the nurse-assigned ESI.Conclusion: This AI-enhanced ESI system demonstrates significant improvements over traditional methods by integrating patient information with model predictions into a single, comprehensive, and dynamic triage score. It has the potential to optimize resource allocation, improve patient flow, and enhance overall patient care in EDs. Future work will focus on prospective validation, seamless integration into clinical workflows, and assessment of its impact on patient outcomes and ED efficiency.

@article{locke_role_2024,
	title = {The {Role} of {Obstructive} {Sleep} {Apnea} in {Hypercapnic} {Respiratory} {Failure} {Identified} in {Critical} {Care}, {Inpatient}, and {Outpatient} {Settings}},
	volume = {19},
	issn = {1556-407X, 1556-4088},
	url = {https://www.sleep.theclinics.com/article/S1556-407X(24)00022-5/abstract},
	doi = {10.1016/j.jsmc.2024.02.012},
	language = {English},
	number = {2},
	urldate = {2024-12-13},
	journal = {Sleep Medicine Clinics},
	publisher = {Elsevier},
	author = {Locke, Brian W. and Brown, Jeanette P. and Sundar, Krishna M.},
	month = jun,
	year = {2024},
	keywords = {Hypercapnia, Hypercapnic respiratory failure, Hypoventilation, Non-invasive ventilation, Positive airway pressure, Respiratory insufficiency, Sleep apnea},
	pages = {339--356},
}

@inproceedings{locke_diagnostic_2024,
	address = {Cham},
	title = {Diagnostic {Modeling} to {Identify} {Unrecognized} {Inpatient} {Hypercapnia} {Using} {Health} {Record} {Data}},
	isbn = {978-3-031-66538-7},
	doi = {10.1007/978-3-031-66538-7_4},
	abstract = {Hypercapnic respiratory failure (an accumulation of carbon dioxide, CO2, in the blood) is often missed in clinical practice. Arterial blood gas is the standard diagnostic test, but it is painful and not routine. When clinicians fail to make the diagnosis, it is often because an arterial blood gas was not obtained. This ‘partial verification’ of CO2 levels presents a challenge for machine learning algorithms. We assessed the accuracy of two machine learning methods using demographics and routine lab work to estimate the likelihood that a patient has hypercapnic respiratory failure at hospital admission. Hospitalized patients who received an arterial blood gas sample constituted the training (n = 111,015) and geographic validation (n = 20,834) sets. Acceptance of “silver standard” diagnostic criteria and weighting observations by their modeled likelihood of receiving arterial blood gas sampling were used to assess the stability of findings in the presence of partial verification. Both regularized logistic regression and random-forest-based models resulted in acceptable performance (area under the curve: 0.763 and 0.758 respectively), with minimal changes in the auxiliary analyses. This work suggests that routinely available health record data can stratify the likelihood of hypercapnic respiratory failure among hospitalized adults, and findings may generalize to patients who have not received arterial blood gas sampling in clinical practice.},
	language = {en},
	booktitle = {Artificial {Intelligence} in {Medicine}},
	publisher = {Springer Nature Switzerland},
	author = {Locke, Brian W. and Richards, W. Wayne and Brown, Jeanette P. and Cui, Wanting and Finkelstein, Joseph and Sundar, Krishna M. and Gouripeddi, Ramkiran},
	editor = {Finkelstein, Joseph and Moskovitch, Robert and Parimbelli, Enea},
	year = {2024},
	pages = {36--45},
}

Hypercapnic respiratory failure (an accumulation of carbon dioxide, CO2, in the blood) is often missed in clinical practice. Arterial blood gas is the standard diagnostic test, but it is painful and not routine. When clinicians fail to make the diagnosis, it is often because an arterial blood gas was not obtained. This ‘partial verification’ of CO2 levels presents a challenge for machine learning algorithms. We assessed the accuracy of two machine learning methods using demographics and routine lab work to estimate the likelihood that a patient has hypercapnic respiratory failure at hospital admission. Hospitalized patients who received an arterial blood gas sample constituted the training (n = 111,015) and geographic validation (n = 20,834) sets. Acceptance of “silver standard” diagnostic criteria and weighting observations by their modeled likelihood of receiving arterial blood gas sampling were used to assess the stability of findings in the presence of partial verification. Both regularized logistic regression and random-forest-based models resulted in acceptable performance (area under the curve: 0.763 and 0.758 respectively), with minimal changes in the auxiliary analyses. This work suggests that routinely available health record data can stratify the likelihood of hypercapnic respiratory failure among hospitalized adults, and findings may generalize to patients who have not received arterial blood gas sampling in clinical practice.

@article{anderson-bell_hypercapnic_2024,
	title = {{HYPERCAPNIC} {RESPIRATORY} {FAILURE} {IS} {INFREQUENTLY} {REPORTED} {AS} {AN} {ADVERSE} {EVENT} {IN} {CLINICAL} {TRIALS}},
	volume = {166},
	issn = {0012-3692},
	url = {https://journal.chestnet.org/article/S0012-3692(24)04179-5/abstract},
	doi = {10.1016/j.chest.2024.06.3377},
	abstract = {SESSION TITLE: Pulmonary Physiology Abstracts Posters},
	language = {English},
	number = {4},
	urldate = {2024-12-13},
	journal = {CHEST},
	publisher = {Elsevier},
	author = {Anderson-Bell, Dustin and Locke, Brian W.},
	month = oct,
	year = {2024},
	pages = {A5688},
}

SESSION TITLE: Pulmonary Physiology Abstracts Posters

@article{locke_low_2024,
	title = {A {LOW} {RESEARCH}-{BURDEN} {STUDY} {TO} {ASSESS} {FEASIBILITY} {AND} {INTEREST} {IN} {IMMERSIVE} {VIRTUAL} {REALITY} {IN} {THE} {INTENSIVE} {CARE} {UNIT}},
	volume = {166},
	url = {https://journal.chestnet.org/article/S0012-3692(24)00951-6/abstract},
	number = {4},
	urldate = {2024-12-13},
	journal = {CHEST},
	publisher = {Elsevier},
	author = {LOCKE, BRIAN W. and RIVERA, CHRISTIAN REATEGUI and TSAI, TE-YI and GABRIEL, AILEEN and FINKELSTEIN, JOSEPH},
	year = {2024},
	pages = {A155--A156},
}

@article{aberegg_hickams_2024,
	title = {Hickam’s {Dictum}: {An} {Analysis} of {Multiple} {Diagnoses}},
	issn = {1525-1497},
	shorttitle = {Hickam’s {Dictum}},
	url = {https://doi.org/10.1007/s11606-024-09120-y},
	doi = {10.1007/s11606-024-09120-y},
	abstract = {Hickam’s dictum (“a patient can have as many diseases as he damn well pleases”) has been touted as a counterargument to Ockham’s razor, which enjoins clinicians to seek a single, simple, or unifying diagnosis. Yet the phenomenon of multiple diagnoses has not been formally analyzed.},
	language = {en},
	urldate = {2024-12-13},
	journal = {Journal of General Internal Medicine},
	author = {Aberegg, Scott K. and Poole, Brian R. and Locke, Brian W.},
	month = oct,
	year = {2024},
	keywords = {Ockham’s razor, Reichenbach’s common cause principle, causation, chief complaint, coincidence, incidence, incidentaloma, prevalence, probability, unifying diagnosis},
}

Hickam’s dictum (“a patient can have as many diseases as he damn well pleases”) has been touted as a counterargument to Ockham’s razor, which enjoins clinicians to seek a single, simple, or unifying diagnosis. Yet the phenomenon of multiple diagnoses has not been formally analyzed.

@inproceedings{locke_determinants_2024,
	title = {Determinants of the {Performance} of {Serum} {Bicarbonate} for {Identification} of {Hypercapnia} {From} {Health} {Record} {Data}},
	url = {https://www.atsjournals.org/doi/10.1164/ajrccm-conference.2024.209.1_MeetingAbstracts.A4916},
	doi = {10.1164/ajrccm-conference.2024.209.1_MeetingAbstracts.A4916},
	language = {en},
	urldate = {2024-12-13},
	booktitle = {B108. {BREATHE} {EASY}: {NAVIGATING} {THE} {WORLD} {OF} {VENTILATORY} {DISORDERS}},
	publisher = {American Thoracic Society},
	author = {Locke, B.W. and Richards, W. and Brown, J.P. and Gouripeddi, R. and Sundar, K.M.},
	month = may,
	year = {2024},
	pages = {A4916--A4916},
}

@article{locke_immersive_2024,
	title = {Immersive {Virtual} {Reality} {Use} in {Medical} {Intensive} {Care}: {Mixed} {Methods} {Feasibility} {Study}},
	volume = {12},
	copyright = {This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published JMIR Serious Games, is properly cited. The complete bibliographic information, a link to the original publication on https://games.jmir.org/, as well as this copyright and license information must be included.},
	shorttitle = {Immersive {Virtual} {Reality} {Use} in {Medical} {Intensive} {Care}},
	url = {https://games.jmir.org/2024/1/e62842},
	doi = {10.2196/62842},
	abstract = {Background: Immersive virtual reality (VR) is a promising therapy to improve the experience of patients with critical illness and may help avoid postdischarge functional impairments. However, the determinants of interest and usability may vary locally and reports of uptake in the literature are variable.
Objective: The aim of this mixed methods feasibility study was to assess the acceptability and potential utility of immersive VR in critically ill patients at a single institution.
Methods: Adults without delirium who were admitted to 1 of 2 intensive care units were offered the opportunity to participate in 5-15 minutes of immersive VR delivered by a VR headset. Patient vital signs, heart rate variability, mood, and pain were assessed before and after the VR experience. Pre-post comparisons were performed using paired 2-sided t tests. A semistructured interview was administered after the VR experience. Patient descriptions of the experience, issues, and potential uses were summarized with thematic analysis.
Results: Of the 35 patients offered the chance to participate, 20 (57\%) agreed to partake in the immersive VR experience, with no difference in participation rate by age. Improvements were observed in overall mood (mean difference 1.8 points, 95\% CI 0.6-3.0; P=.002), anxiety (difference of 1.7 points, 95\% CI 0.8-2.7; P=.001), and pain (difference of 1.3 points, 95\% CI 0.5-2.1; P=.003) assessed on 1-10 scales. The heart rate changed by a mean of –1.1 (95\% CI –0.3 to –1.9; P=.008) beats per minute (bpm) from a baseline of 86.1 (SD 11.8) bpm and heart rate variability, assessed by the stress index (SI), changed by a mean of –5.0 (95\% CI –1.5 to –8.5; P=.004) seconds–2 from a baseline SI of 40.0 (SD 23) seconds–2. Patients commented on the potential for the therapy to address pain, lessen anxiety, and facilitate calmness. Technical challenges were minimal and there were no adverse effects observed.
Conclusions: Patient acceptance of immersive VR was high in a mostly medical intensive care population with little prior VR experience. Patients commented on the potential of immersive VR to ameliorate cognitive and emotional symptoms. Investigators can consider integrating minimally modified commercial VR headsets into the existing intensive care unit workflow to further assess VR’s efficacy for a variety of endpoints.},
	language = {EN},
	number = {1},
	urldate = {2024-12-13},
	journal = {JMIR Serious Games},
	publisher = {JMIR Publications Inc., Toronto, Canada},
	author = {Locke, Brian W. and Tsai, Te-yi and Reategui-Rivera, C. Mahony and Gabriel, Aileen S. and Smiley, Aref and Finkelstein, Joseph},
	month = aug,
	year = {2024},
	note = {Company: JMIR Serious Games
Distributor: JMIR Serious Games
Institution: JMIR Serious Games
Label: JMIR Serious Games},
	pages = {e62842},
}

Background: Immersive virtual reality (VR) is a promising therapy to improve the experience of patients with critical illness and may help avoid postdischarge functional impairments. However, the determinants of interest and usability may vary locally and reports of uptake in the literature are variable. Objective: The aim of this mixed methods feasibility study was to assess the acceptability and potential utility of immersive VR in critically ill patients at a single institution. Methods: Adults without delirium who were admitted to 1 of 2 intensive care units were offered the opportunity to participate in 5-15 minutes of immersive VR delivered by a VR headset. Patient vital signs, heart rate variability, mood, and pain were assessed before and after the VR experience. Pre-post comparisons were performed using paired 2-sided t tests. A semistructured interview was administered after the VR experience. Patient descriptions of the experience, issues, and potential uses were summarized with thematic analysis. Results: Of the 35 patients offered the chance to participate, 20 (57%) agreed to partake in the immersive VR experience, with no difference in participation rate by age. Improvements were observed in overall mood (mean difference 1.8 points, 95% CI 0.6-3.0; P=.002), anxiety (difference of 1.7 points, 95% CI 0.8-2.7; P=.001), and pain (difference of 1.3 points, 95% CI 0.5-2.1; P=.003) assessed on 1-10 scales. The heart rate changed by a mean of –1.1 (95% CI –0.3 to –1.9; P=.008) beats per minute (bpm) from a baseline of 86.1 (SD 11.8) bpm and heart rate variability, assessed by the stress index (SI), changed by a mean of –5.0 (95% CI –1.5 to –8.5; P=.004) seconds–2 from a baseline SI of 40.0 (SD 23) seconds–2. Patients commented on the potential for the therapy to address pain, lessen anxiety, and facilitate calmness. Technical challenges were minimal and there were no adverse effects observed. Conclusions: Patient acceptance of immersive VR was high in a mostly medical intensive care population with little prior VR experience. Patients commented on the potential of immersive VR to ameliorate cognitive and emotional symptoms. Investigators can consider integrating minimally modified commercial VR headsets into the existing intensive care unit workflow to further assess VR’s efficacy for a variety of endpoints.

@article{locke_association_2024,
	title = {The association of weight loss from anti‐obesity medications or bariatric surgery and apnea‐hypopnea index in obstructive sleep apnea},
	volume = {25},
	issn = {1467-7881, 1467-789X},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/obr.13697},
	doi = {10.1111/obr.13697},
	abstract = {Summary
            
              Introduction
              Weight loss is recommended for individuals with obstructive sleep apnea (OSA) and overweight or obesity, but there is limited evidence to guide the selection of weight management strategies for patients who do not lose sufficient weight with diet and lifestyle changes. We evaluated the relationship between weight loss caused by pharmacologic or surgical interventions and subsequent improvement in OSA by the apnea‐hypopnea index (AHI).
            
            
              Methods
              PubMed, Cochrane CENTRAL, and EMBASE were searched for randomized trials comparing pharmacologic or surgical obesity interventions to usual care, placebo, or no treatment in adults with OSA. The association between percentage weight loss and AHI change between randomization and last follow‐up was evaluated using meta‐regression. PROSPERO: CRD42022378853.
            
            
              Results
              
                Ten eligible trials (
                n
                 = 854 patients) were included. Four (
                n
                 = 211) assessed bariatric surgery, and 6 (
                n
                 = 643) assessed pharmacologic interventions over a median follow‐up of 13 months (interquartile range 6–26 months). The linear best estimate of the change in AHI is 0.45 events per hour (95\% Confidence Interval 0.18 to 0.73 events per hour) for every 1\% body weight lost.
              
            
            
              Conclusions
              Weight loss caused by medication or surgery caused a proportionate improvement of the AHI. Providers could consider extrapolating from this relationship when advising patients of the expected effects of other pharmacologic or surgical interventions without direct evidence in OSA.},
	language = {en},
	number = {4},
	urldate = {2024-12-13},
	journal = {Obesity Reviews},
	author = {Locke, Brian W. and Gomez‐Lumbreras, Ainhoa and Tan, Chia Jie and Nonthasawadsri, Teerawat and Veettil, Sajesh K. and Patikorn, Chanthawat and Chaiyakunapruk, Nathorn},
	month = apr,
	year = {2024},
	pages = {e13697},
}

Summary Introduction Weight loss is recommended for individuals with obstructive sleep apnea (OSA) and overweight or obesity, but there is limited evidence to guide the selection of weight management strategies for patients who do not lose sufficient weight with diet and lifestyle changes. We evaluated the relationship between weight loss caused by pharmacologic or surgical interventions and subsequent improvement in OSA by the apnea‐hypopnea index (AHI). Methods PubMed, Cochrane CENTRAL, and EMBASE were searched for randomized trials comparing pharmacologic or surgical obesity interventions to usual care, placebo, or no treatment in adults with OSA. The association between percentage weight loss and AHI change between randomization and last follow‐up was evaluated using meta‐regression. PROSPERO: CRD42022378853. Results Ten eligible trials ( n  = 854 patients) were included. Four ( n  = 211) assessed bariatric surgery, and 6 ( n  = 643) assessed pharmacologic interventions over a median follow‐up of 13 months (interquartile range 6–26 months). The linear best estimate of the change in AHI is 0.45 events per hour (95% Confidence Interval 0.18 to 0.73 events per hour) for every 1% body weight lost. Conclusions Weight loss caused by medication or surgery caused a proportionate improvement of the AHI. Providers could consider extrapolating from this relationship when advising patients of the expected effects of other pharmacologic or surgical interventions without direct evidence in OSA.

@misc{pettine_need_2024,
	title = {The need for data harmonization, multimodal integration and multi-objective loss functions to identify longitudinal changes in aging},
	url = {https://preprints.jmir.org/preprint/68679},
	abstract = {Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet},
	language = {en},
	urldate = {2024-12-04},
	author = {Pettine, Warren and Christenson, Matthias},
	month = dec,
	year = {2024},
}

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

@article{christenson_hue_2024,
	title = {Hue selectivity from recurrent circuitry in {Drosophila}},
	volume = {27},
	copyright = {2024 The Author(s)},
	issn = {1546-1726},
	url = {https://www.nature.com/articles/s41593-024-01640-4},
	doi = {10.1038/s41593-024-01640-4},
	abstract = {In the perception of color, wavelengths of light reflected off objects are transformed into the derived quantities of brightness, saturation and hue. Neurons responding selectively to hue have been reported in primate cortex, but it is unknown how their narrow tuning in color space is produced by upstream circuit mechanisms. We report the discovery of neurons in the Drosophila optic lobe with hue-selective properties, which enables circuit-level analysis of color processing. From our analysis of an electron microscopy volume of a whole Drosophila brain, we construct a connectomics-constrained circuit model that accounts for this hue selectivity. Our model predicts that recurrent connections in the circuit are critical for generating hue selectivity. Experiments using genetic manipulations to perturb recurrence in adult flies confirm this prediction. Our findings reveal a circuit basis for hue selectivity in color vision.},
	language = {en},
	number = {6},
	urldate = {2024-12-11},
	journal = {Nature Neuroscience},
	publisher = {Nature Publishing Group},
	author = {Christenson, Matthias P. and Sanz Diez, Alvaro and Heath, Sarah L. and Saavedra-Weisenhaus, Maia and Adachi, Atsuko and Nern, Aljoscha and Abbott, L. F. and Behnia, Rudy},
	month = jun,
	year = {2024},
	keywords = {Neural circuits, Sensory processing},
	pages = {1137--1147},
}

In the perception of color, wavelengths of light reflected off objects are transformed into the derived quantities of brightness, saturation and hue. Neurons responding selectively to hue have been reported in primate cortex, but it is unknown how their narrow tuning in color space is produced by upstream circuit mechanisms. We report the discovery of neurons in the Drosophila optic lobe with hue-selective properties, which enables circuit-level analysis of color processing. From our analysis of an electron microscopy volume of a whole Drosophila brain, we construct a connectomics-constrained circuit model that accounts for this hue selectivity. Our model predicts that recurrent connections in the circuit are critical for generating hue selectivity. Experiments using genetic manipulations to perturb recurrence in adult flies confirm this prediction. Our findings reveal a circuit basis for hue selectivity in color vision.

@misc{christenson_assessing_2024,
	title = {Assessing {Foundation} {Models}' {Transferability} to {Physiological} {Signals} in {Precision} {Medicine}},
	url = {http://arxiv.org/abs/2412.03427},
	doi = {10.48550/arXiv.2412.03427},
	abstract = {The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario differentiation. Finally, the pipeline validates these representations through specific downstream medical tasks. Initial testing of our pipeline on the Moirai time series foundation model revealed significant limitations in physiological signal processing, including feature entanglement, temporal dynamics distortion, and reduced scenario discrimination. These findings suggest that current foundation models may require substantial architectural modifications or targeted fine-tuning before deployment in clinical settings.},
	urldate = {2024-12-09},
	publisher = {arXiv},
	author = {Christenson, Matthias and Geary, Cove and Locke, Brian and Koirala, Pranav and Pettine, Warren Woodrich},
	month = dec,
	year = {2024},
	note = {arXiv:2412.03427},
	keywords = {Computer Science - Machine Learning},
}

The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario differentiation. Finally, the pipeline validates these representations through specific downstream medical tasks. Initial testing of our pipeline on the Moirai time series foundation model revealed significant limitations in physiological signal processing, including feature entanglement, temporal dynamics distortion, and reduced scenario discrimination. These findings suggest that current foundation models may require substantial architectural modifications or targeted fine-tuning before deployment in clinical settings.

@techreport{pettine_attention_2024,
	title = {Attention and learning strategies reveal distinct profiles of psychiatric traits},
	url = {https://osf.io/preprints/psyarxiv/52mkw},
	language = {en-us},
	institution = {PsyArXiv},
	author = {Pettine, Warren Woodrich and Tseng, Angela and Yang, Amy and Docherty, Anna R. and Redish, A. David and Murray, John D. and Jacob, Suma},
	year = {2024},
	keywords = {Cognitive Psychology, Concepts and Categories, Learning, Social and Behavioral Sciences, category learning, computational model, reinforcement learning},
}

@article{locke_uncomfortable_2023,
	title = {Uncomfortable {Truths} {About} {Data}, {Justice}, and {Idiopathic} {Pulmonary} {Fibrosis} in the {Veterans} {Health} {Administration}},
	volume = {164},
	issn = {0012-3692},
	url = {https://journal.chestnet.org/article/S0012-3692(23)00465-8/abstract},
	doi = {10.1016/j.chest.2023.03.037},
	abstract = {The 2014 approvals of pirfenidone and nintedanib for use in idiopathic pulmonary fibrosis
(IPF) by the US Food and Drug Administration were notable in the acceptance of an
unproven surrogate end point: decrease in the rate of FVC decline. They offered the
following rationale1: (1) IPF carries a dire prognosis, with a current mean survival
of 4 years2 and no disease-modifying treatments; (2) the preferred clinical efficacy
end point (death) would require a prohibitively large, slow, and expensive trial;
and (3) both the natural disease course and cumulative trial data suggest that reduced
spirometric decline ultimately would translate to improvements in mortality rates
and other patient-important end points.},
	language = {English},
	number = {2},
	urldate = {2024-12-13},
	journal = {CHEST},
	publisher = {Elsevier},
	author = {Locke, Brian W. and Callahan, Sean J.},
	month = aug,
	year = {2023},
	pages = {280--282},
}

The 2014 approvals of pirfenidone and nintedanib for use in idiopathic pulmonary fibrosis (IPF) by the US Food and Drug Administration were notable in the acceptance of an unproven surrogate end point: decrease in the rate of FVC decline. They offered the following rationale1: (1) IPF carries a dire prognosis, with a current mean survival of 4 years2 and no disease-modifying treatments; (2) the preferred clinical efficacy end point (death) would require a prohibitively large, slow, and expensive trial; and (3) both the natural disease course and cumulative trial data suggest that reduced spirometric decline ultimately would translate to improvements in mortality rates and other patient-important end points.

@article{locke_pulmonary_2023,
	title = {{PULMONARY} {ARTERY} {SIZE} {ON} {CT} {AND} {MORTALITY} {RISK}: {A} {DOSE}-{RESPONSE} {ANALYSIS}},
	volume = {164},
	issn = {0012-3692},
	shorttitle = {{PULMONARY} {ARTERY} {SIZE} {ON} {CT} {AND} {MORTALITY} {RISK}},
	url = {https://journal.chestnet.org/article/S0012-3692(23)04909-7/abstract},
	doi = {10.1016/j.chest.2023.07.3871},
	abstract = {SESSION TITLE: Pulmonary Vascular Disease Posters 5},
	language = {English},
	number = {4},
	urldate = {2024-12-13},
	journal = {CHEST},
	publisher = {Elsevier},
	author = {Locke, Brian W. and Scarpato, Brittany and Bledsoe, Joseph and Knox, Daniel and Conner, Karen and Stoddard, Gregory and Cirulis, Meghan M. and Elliott, C. Gre and Dodson, Mark W.},
	month = oct,
	year = {2023},
	pages = {A6004--A6005},
}

SESSION TITLE: Pulmonary Vascular Disease Posters 5

@article{fenger_just_2023,
	title = {{JUST} {KEEP} {TRYING}: {PRIOR} {ATTEMPTS} {AT} {WEANING} {DO} {NOT} {DETERMINE} {EVENTUAL} {LIBERATION} {FROM} {TRACHESOTOMY} {AND} {MECHANICAL} {VENTILATION} {IN} {HIGH}-{LEVEL} {SPINAL} {CORD} {PATIENTS}},
	volume = {164},
	issn = {0012-3692},
	shorttitle = {{JUST} {KEEP} {TRYING}},
	url = {https://journal.chestnet.org/article/S0012-3692(23)05014-6/abstract},
	doi = {10.1016/j.chest.2023.07.3976},
	abstract = {SESSION TITLE: Respiratory Care Posters},
	language = {English},
	number = {4},
	urldate = {2024-12-13},
	journal = {CHEST},
	publisher = {Elsevier},
	author = {Fenger, Casey and Barker, James and Creveling, Polly and Tang, William and Flis, Alexandra and Park, Kevin and Rosenbluth, Jeffrey and Locke, Brian W. and Brown, Jeanette P.},
	month = oct,
	year = {2023},
	pages = {A6180--A6181},
}

SESSION TITLE: Respiratory Care Posters

@article{locke_what_2023,
	chapter = {Editorial},
	title = {What {Are} {We} {Aiming} for in {Chronic} {Hypercapnic} {Respiratory} {Failure}?},
	volume = {68},
	copyright = {Copyright © 2023 by Daedalus Enterprises},
	issn = {0020-1324, 1943-3654},
	url = {https://rc.rcjournal.com/content/68/12/1775},
	doi = {10.4187/respcare.11573},
	abstract = {The wisdom of organizing illness into clinical syndromes—sepsis, ARDS, and the like—has been debated for at least 50 years, perhaps longer.1,2 The utility comes from a syndrome’s ability to facilitate the recognition or understanding of an important common element among the people who receive the label.3 The “lumpers,” being in favor of syndromes as a paradigm, note the promise of more reliable clinical recognition, efficient enrollment into studies, and optimization of care processes. The “splitters,” taking the opposite position, emphasize the peril of grouping patients with different pathophysiology, different natural histories of disease, and differing responses to treatment. If the average outcome poorly represents how individuals will respond, both individual care and the efficiency of trials suffer.

Hypercapnic respiratory failure is the syndrome that occurs when alveolar ventilation is insufficient to match metabolic demand. Is the “syndrome paradigm” the right way for us to improve how we treat these patients? Or would efforts be better spent focusing on individual diseases? Comparison to where other syndrome-based research has excelled or struggled might provide guidance.

The heterogeneity of patients labeled as having ARDS or sepsis is one proposal for why few trials studying therapies for those conditions have shown replicable benefits.4 A diverse range of pathologies can lead to hypercapnia. Permutations of physiologic derangements limiting the maximal sustainable ventilation (unfavorable respiratory system loads, muscle weakness, mechanical disadvantage, unstable ventilatory control) and those leading to a large ventilation requirement (ventilatory inefficiency, elevated metabolic rate) contribute to hypercapnia in differing degrees in different diseases.5 Will most patients with hypercapnic respiratory failure respond similarly to a proposed management strategy? The demonstrated benefit of noninvasive ventilation (NIV) in COPD is contingent on selecting only specific patients (that remain hypercapnic after 2–4 weeks of stability) and applying a specific strategy (high … 

Correspondence: Brian W Locke MD. E-mail: brian.locke\{at\}hsc.utah.edu},
	language = {en},
	number = {12},
	urldate = {2024-12-13},
	journal = {Respiratory Care},
	publisher = {Respiratory Care},
	author = {Locke, Brian W. and Brown, Jeanette},
	month = dec,
	year = {2023},
	pages = {1775--1778},
}

The wisdom of organizing illness into clinical syndromes—sepsis, ARDS, and the like—has been debated for at least 50 years, perhaps longer.1,2 The utility comes from a syndrome’s ability to facilitate the recognition or understanding of an important common element among the people who receive the label.3 The “lumpers,” being in favor of syndromes as a paradigm, note the promise of more reliable clinical recognition, efficient enrollment into studies, and optimization of care processes. The “splitters,” taking the opposite position, emphasize the peril of grouping patients with different pathophysiology, different natural histories of disease, and differing responses to treatment. If the average outcome poorly represents how individuals will respond, both individual care and the efficiency of trials suffer. Hypercapnic respiratory failure is the syndrome that occurs when alveolar ventilation is insufficient to match metabolic demand. Is the “syndrome paradigm” the right way for us to improve how we treat these patients? Or would efforts be better spent focusing on individual diseases? Comparison to where other syndrome-based research has excelled or struggled might provide guidance. The heterogeneity of patients labeled as having ARDS or sepsis is one proposal for why few trials studying therapies for those conditions have shown replicable benefits.4 A diverse range of pathologies can lead to hypercapnia. Permutations of physiologic derangements limiting the maximal sustainable ventilation (unfavorable respiratory system loads, muscle weakness, mechanical disadvantage, unstable ventilatory control) and those leading to a large ventilation requirement (ventilatory inefficiency, elevated metabolic rate) contribute to hypercapnia in differing degrees in different diseases.5 Will most patients with hypercapnic respiratory failure respond similarly to a proposed management strategy? The demonstrated benefit of noninvasive ventilation (NIV) in COPD is contingent on selecting only specific patients (that remain hypercapnic after 2–4 weeks of stability) and applying a specific strategy (high … Correspondence: Brian W Locke MD. E-mail: brian.locke\at\hsc.utah.edu

@inproceedings{locke_test_2023,
	title = {Test {Performance} of {Serum} {Bicarbonate} in {Identifying} {Hypercapnia} {Across} {Settings} and {Diseases}},
	url = {https://www.atsjournals.org/doi/10.1164/ajrccm-conference.2023.207.1_MeetingAbstracts.A6495},
	doi = {10.1164/ajrccm-conference.2023.207.1_MeetingAbstracts.A6495},
	language = {en},
	urldate = {2024-12-13},
	booktitle = {D30. {INTEGRATING} {OSA} {AND} {COMORBIDITIES} {FOR} {EFFECTIVE} {THERAPIES}},
	publisher = {American Thoracic Society},
	author = {Locke, B.W. and Gouripeddi, R. and Richards, W. and Brown, J.P. and Sundar, K.M.},
	month = may,
	year = {2023},
	pages = {A6495--A6495},
}

@inproceedings{taylor_trajectory_2023,
	title = {Trajectory of {Bicarbonate} {With} {CPAP} {Usage} {Across} the {Spectrum} of {Obesity} {Related} {Hypoventilation}},
	url = {https://www.atsjournals.org/doi/10.1164/ajrccm-conference.2023.207.1_MeetingAbstracts.A1424},
	doi = {10.1164/ajrccm-conference.2023.207.1_MeetingAbstracts.A1424},
	language = {en},
	urldate = {2024-12-13},
	booktitle = {A36. {SLEEP}-{RELATED} {HEALTH} {SERVICES} {RESEARCH}},
	publisher = {American Thoracic Society},
	author = {Taylor, A. and Locke, B.W. and Howe, H. and Workman, K. and Sundar, K.M.},
	month = may,
	year = {2023},
	pages = {A1424--A1424},
}

@article{locke_predictors_2023,
	title = {Predictors of {Initial} {CPAP} {Prescription} and {Subsequent} {Course} with {CPAP} in {Patients} with {Central} {Sleep} {Apneas} at a {Single} {Center}},
	volume = {201},
	issn = {1432-1750},
	url = {https://doi.org/10.1007/s00408-023-00657-z},
	doi = {10.1007/s00408-023-00657-z},
	abstract = {Guidelines recommend considering an initial trial of continuous positive airway pressure (CPAP) to treat central sleep apnea (CSA). However, practice patterns vary widely. This study investigated predictors for an initial trial of CPAP in patients with central apneas and whether those factors predict adequate treatment response in patients receiving an initial CPAP trial.},
	language = {en},
	number = {6},
	urldate = {2024-12-13},
	journal = {Lung},
	author = {Locke, Brian W. and Sellman, Jeffrey and McFarland, Jonathan and Uribe, Francisco and Workman, Kimberly and Sundar, Krishna M.},
	month = dec,
	year = {2023},
	keywords = {Clinical decision-making, Continuous positive airway pressure, Sleep apnea syndromes, Sleep apnea, central},
	pages = {625--634},
}

Guidelines recommend considering an initial trial of continuous positive airway pressure (CPAP) to treat central sleep apnea (CSA). However, practice patterns vary widely. This study investigated predictors for an initial trial of CPAP in patients with central apneas and whether those factors predict adequate treatment response in patients receiving an initial CPAP trial.

@article{scarpato_association_2023,
	title = {The association between pulmonary artery enlargement and mortality in an {Emergency} {Department} population undergoing computed tomography pulmonary angiography},
	volume = {13},
	copyright = {© 2023 The Authors. Pulmonary Circulation published by John Wiley \& Sons Ltd on behalf of Pulmonary Vascular Research Institute.},
	issn = {2045-8940},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pul2.12225},
	doi = {10.1002/pul2.12225},
	abstract = {Findings of an enlarged pulmonary artery diameter (PAd) and increased pulmonary artery to ascending aorta ratio (PA:AA) on contrast-enhanced computed tomography pulmonary angiography (CTPA) are associated with increased mortality in particular groups of patients with cardiopulmonary disease. However, the frequency and prognostic significance of these incidental findings has not been studied in unselected patients evaluated in the Emergency Department (ED). This study aims to determine the prevalence and associated prognosis of enlarged pulmonary artery measurements in an ED cohort. We measured PA and AA diameters on 990 CTPA studies performed in the ED. An enlarged PA diameter was defined as {\textgreater}27 mm in females and {\textgreater}29 mm in males, while an increased PA:AA was defined as {\textgreater}0.9. Poisson regression was performed to calculate prevalence ratios for relevant comorbidities, and multivariable Cox regression was performed to calculate hazard ratios (HR) for mortality of patients with enlarged pulmonary artery measurements. An enlarged PAd was observed in 27.9\% of 990 patients and was more commonly observed in older patients and in patients with obesity or heart failure. Conversely, PA:AA was increased in 34.2\% of subjects, and was more common in younger patients and those with peripheral vascular disease or obesity. After controlling for age, sex, and comorbidities, both enlarged PAd (HR 1.29, 95\% CI 1.00–1.68, p = 0.05) and PA:AA (HR 1.70, 95\% CI 1.31–2.22 p {\textless} 0.01) were independently associated with mortality. In sum, enlarged PAd and increased PA:AA are common in patients undergoing CTPAs in the ED setting and both are independently associated with mortality.},
	language = {en},
	number = {2},
	urldate = {2024-12-13},
	journal = {Pulmonary Circulation},
	author = {Scarpato, Brittany M. and Locke, Brian W. and Bledsoe, Joseph and Knox, Daniel B. and Conner, Karen and Stoddard, Gregory J. and Cirulis, Meghan M. and Elliott, Charles Gregory and Dodson, Mark W.},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pul2.12225},
	keywords = {contrast media, diagnostic techniques and monitoring, epidemiology, imaging, mortality},
	pages = {e12225},
}

Findings of an enlarged pulmonary artery diameter (PAd) and increased pulmonary artery to ascending aorta ratio (PA:AA) on contrast-enhanced computed tomography pulmonary angiography (CTPA) are associated with increased mortality in particular groups of patients with cardiopulmonary disease. However, the frequency and prognostic significance of these incidental findings has not been studied in unselected patients evaluated in the Emergency Department (ED). This study aims to determine the prevalence and associated prognosis of enlarged pulmonary artery measurements in an ED cohort. We measured PA and AA diameters on 990 CTPA studies performed in the ED. An enlarged PA diameter was defined as \textgreater27 mm in females and \textgreater29 mm in males, while an increased PA:AA was defined as \textgreater0.9. Poisson regression was performed to calculate prevalence ratios for relevant comorbidities, and multivariable Cox regression was performed to calculate hazard ratios (HR) for mortality of patients with enlarged pulmonary artery measurements. An enlarged PAd was observed in 27.9% of 990 patients and was more commonly observed in older patients and in patients with obesity or heart failure. Conversely, PA:AA was increased in 34.2% of subjects, and was more common in younger patients and those with peripheral vascular disease or obesity. After controlling for age, sex, and comorbidities, both enlarged PAd (HR 1.29, 95% CI 1.00–1.68, p = 0.05) and PA:AA (HR 1.70, 95% CI 1.31–2.22 p \textless 0.01) were independently associated with mortality. In sum, enlarged PAd and increased PA:AA are common in patients undergoing CTPAs in the ED setting and both are independently associated with mortality.

@article{locke_verity_2023,
	title = {The {Verity} of a {Unifying} {Diagnosis}},
	volume = {43},
	issn = {0272-989X},
	url = {https://doi.org/10.1177/0272989X231192521},
	doi = {10.1177/0272989X231192521},
	language = {en},
	number = {7-8},
	urldate = {2024-12-13},
	journal = {Medical Decision Making},
	publisher = {SAGE Publications Inc STM},
	author = {Locke, Brian W. and Aberegg, Scott K.},
	month = oct,
	year = {2023},
	pages = {755--757},
}

@misc{pettine_altered_2023,
	address = {Tampa, Florida},
	title = {Altered goal-directed attention and test-retest performance adults typed for autism, {ADHD} and depression},
	author = {Pettine, Warren W. and Redish, A. David and Tseng, Angela and Murray, John D. and Jacob, Suma},
	year = {2023},
}

@inproceedings{locke_changes_2022,
	title = {Changes in {Bicarbonate} in {Patients} at {Risk} for {Obesity} {Hypoventilation} {Undergoing} {Bariatric} {Surgery}},
	url = {https://www.atsjournals.org/doi/10.1164/ajrccm-conference.2022.205.1_MeetingAbstracts.A4996},
	doi = {10.1164/ajrccm-conference.2022.205.1_MeetingAbstracts.A4996},
	language = {en},
	urldate = {2024-12-13},
	booktitle = {C110. {BRIDGING} {THE} {GAPS}: {SLEEP}, {NIV}, {PULMONARY} {DISEASE}, {AND} {COMORBIDITIES}},
	publisher = {American Thoracic Society},
	author = {Locke, B.W. and Addison, C. and Mishra, S. and Sundar, K.M.},
	month = may,
	year = {2022},
	pages = {A4996--A4996},
}

@inproceedings{locke_common_2022,
	title = {Common {Methods} of {Identifying} {Hypercapnic} {Respiratory} {Failure} {Produce} {Meaningfully} {Different} {Cohorts}},
	url = {https://www.atsjournals.org/doi/10.1164/ajrccm-conference.2022.205.1_MeetingAbstracts.A2889},
	doi = {10.1164/ajrccm-conference.2022.205.1_MeetingAbstracts.A2889},
	language = {en},
	urldate = {2024-12-13},
	booktitle = {B43. {ICU} {MANAGEMENT}},
	publisher = {American Thoracic Society},
	author = {Locke, B.W. and Sundar, K.M. and Brown, J.P. and Gouripeddi, R.},
	month = may,
	year = {2022},
	pages = {A2889--A2889},
}

@article{locke_osa_2022,
	title = {{OSA} and {Chronic} {Respiratory} {Disease}: {Mechanisms} and {Epidemiology}},
	volume = {19},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {1660-4601},
	shorttitle = {{OSA} and {Chronic} {Respiratory} {Disease}},
	url = {https://www.mdpi.com/1660-4601/19/9/5473},
	doi = {10.3390/ijerph19095473},
	abstract = {Obstructive sleep apnea (OSA) is a highly prevalent disorder that has profound implications on the outcomes of patients with chronic lung disease. The hallmark of OSA is a collapse of the oropharynx resulting in a transient reduction in airflow, large intrathoracic pressure swings, and intermittent hypoxia and hypercapnia. The subsequent cytokine-mediated inflammatory cascade, coupled with tractional lung injury, damages the lungs and may worsen several conditions, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and pulmonary hypertension. Further complicating this is the sleep fragmentation and deterioration of sleep quality that occurs because of OSA, which can compound the fatigue and physical exhaustion often experienced by patients due to their chronic lung disease. For patients with many pulmonary disorders, the available evidence suggests that the prompt recognition and treatment of sleep-disordered breathing improves their quality of life and may also alter the course of their illness. However, more robust studies are needed to truly understand this relationship and the impacts of confounding comorbidities such as obesity and gastroesophageal reflux disease. Clinicians taking care of patients with chronic pulmonary disease should screen and treat patients for OSA, given the complex bidirectional relationship OSA has with chronic lung disease.},
	language = {en},
	number = {9},
	urldate = {2024-12-13},
	journal = {International Journal of Environmental Research and Public Health},
	publisher = {Multidisciplinary Digital Publishing Institute},
	author = {Locke, Brian W. and Lee, Janet J. and Sundar, Krishna M.},
	month = jan,
	year = {2022},
	note = {Number: 9},
	keywords = {hypoventilation, hypoxia, lung inflammation, obstructive, obstructive pulmonary disease, outcomes assessments, sleep apnea},
	pages = {5473},
}

Obstructive sleep apnea (OSA) is a highly prevalent disorder that has profound implications on the outcomes of patients with chronic lung disease. The hallmark of OSA is a collapse of the oropharynx resulting in a transient reduction in airflow, large intrathoracic pressure swings, and intermittent hypoxia and hypercapnia. The subsequent cytokine-mediated inflammatory cascade, coupled with tractional lung injury, damages the lungs and may worsen several conditions, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and pulmonary hypertension. Further complicating this is the sleep fragmentation and deterioration of sleep quality that occurs because of OSA, which can compound the fatigue and physical exhaustion often experienced by patients due to their chronic lung disease. For patients with many pulmonary disorders, the available evidence suggests that the prompt recognition and treatment of sleep-disordered breathing improves their quality of life and may also alter the course of their illness. However, more robust studies are needed to truly understand this relationship and the impacts of confounding comorbidities such as obesity and gastroesophageal reflux disease. Clinicians taking care of patients with chronic pulmonary disease should screen and treat patients for OSA, given the complex bidirectional relationship OSA has with chronic lung disease.

@article{christenson_exploiting_2022,
	title = {Exploiting colour space geometry for visual stimulus design across animals},
	volume = {377},
	url = {https://royalsocietypublishing.org/doi/full/10.1098/rstb.2021.0280},
	doi = {10.1098/rstb.2021.0280},
	abstract = {Colour vision represents a vital aspect of perception that ultimately enables a wide variety of species to thrive in the natural world. However, unified methods for constructing chromatic visual stimuli in a laboratory setting are lacking. Here, we present stimulus design methods and an accompanying programming package to efficiently probe the colour space of any species in which the photoreceptor spectral sensitivities are known. Our hardware-agnostic approach incorporates photoreceptor models within the framework of the principle of univariance. This enables experimenters to identify the most effective way to combine multiple light sources to create desired distributions of light, and thus easily construct relevant stimuli for mapping the colour space of an organism. We include methodology to handle uncertainty of photoreceptor spectral sensitivity as well as to optimally reconstruct hyperspectral images given recent hardware advances. Our methods support broad applications in colour vision science and provide a framework for uniform stimulus designs across experimental systems.
This article is part of the theme issue ‘Understanding colour vision: molecular, physiological, neuronal and behavioural studies in arthropods’.},
	number = {1862},
	urldate = {2024-12-11},
	journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
	publisher = {Royal Society},
	author = {Christenson, Matthias P. and Mousavi, S. Navid and Oriol, Elie and Heath, Sarah L. and Behnia, Rudy},
	month = sep,
	year = {2022},
	keywords = {Python, colour management, colour space, colour vision, nonlinear optimization, univariance},
	pages = {20210280},
}

Colour vision represents a vital aspect of perception that ultimately enables a wide variety of species to thrive in the natural world. However, unified methods for constructing chromatic visual stimuli in a laboratory setting are lacking. Here, we present stimulus design methods and an accompanying programming package to efficiently probe the colour space of any species in which the photoreceptor spectral sensitivities are known. Our hardware-agnostic approach incorporates photoreceptor models within the framework of the principle of univariance. This enables experimenters to identify the most effective way to combine multiple light sources to create desired distributions of light, and thus easily construct relevant stimuli for mapping the colour space of an organism. We include methodology to handle uncertainty of photoreceptor spectral sensitivity as well as to optimally reconstruct hyperspectral images given recent hardware advances. Our methods support broad applications in colour vision science and provide a framework for uniform stimulus designs across experimental systems. This article is part of the theme issue ‘Understanding colour vision: molecular, physiological, neuronal and behavioural studies in arthropods’.

@misc{kohn_state_2021,
	title = {State and stimulus dependence reconcile motion computation and the {Drosophila} connectome},
	copyright = {© 2021, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
	url = {https://www.biorxiv.org/content/10.1101/2021.04.17.440267v1},
	doi = {10.1101/2021.04.17.440267},
	abstract = {Sensory systems dynamically optimize their processing properties in order to process a wide range of environmental and behavioral conditions. However, attempts to infer the function of these systems via modeling often treat system components as having static processing properties. This is particularly evident in the Drosophila motion detection circuit, where the core algorithm for motion detection is still debated, and where inputs to motion detecting neurons remain underdescribed. Using whole-cell patch clamp electrophysiology, we measured the state- and stimulus-dependent filtering properties of inputs to the OFF motion-detecting T5 cell in Drosophila. Simply summing these inputs within the framework of a connectomic-constrained model of the circuit demonstrates that changes in the shape of input temporal filters are sufficient to explain conflicting theories of T5 function. Therefore, with our measurements and our model, we reconcile motion computation with the anatomy of the circuit.},
	language = {en},
	urldate = {2024-12-11},
	publisher = {bioRxiv},
	author = {Kohn, Jessica R. and Portes, Jacob P. and Christenson, Matthias P. and Abbott, L. F. and Behnia, Rudy},
	month = apr,
	year = {2021},
	note = {Pages: 2021.04.17.440267
Section: New Results},
}

Sensory systems dynamically optimize their processing properties in order to process a wide range of environmental and behavioral conditions. However, attempts to infer the function of these systems via modeling often treat system components as having static processing properties. This is particularly evident in the Drosophila motion detection circuit, where the core algorithm for motion detection is still debated, and where inputs to motion detecting neurons remain underdescribed. Using whole-cell patch clamp electrophysiology, we measured the state- and stimulus-dependent filtering properties of inputs to the OFF motion-detecting T5 cell in Drosophila. Simply summing these inputs within the framework of a connectomic-constrained model of the circuit demonstrates that changes in the shape of input temporal filters are sufficient to explain conflicting theories of T5 function. Therefore, with our measurements and our model, we reconcile motion computation with the anatomy of the circuit.

@article{kohn_flexible_2021,
	title = {Flexible filtering by neural inputs supports motion computation across states and stimuli},
	volume = {31},
	issn = {0960-9822},
	url = {https://www.cell.com/current-biology/abstract/S0960-9822(21)01317-8},
	doi = {10.1016/j.cub.2021.09.061},
	language = {English},
	number = {23},
	urldate = {2024-12-11},
	journal = {Current Biology},
	publisher = {Elsevier},
	author = {Kohn, Jessica R. and Portes, Jacob P. and Christenson, Matthias P. and Abbott, L. F. and Behnia, Rudy},
	month = dec,
	year = {2021},
	keywords = {Drosophila melanogaster, ON/OFF pathways, connectome, direction selectivity, motion vision, neuromodulation, octopamine, state dependence, temporal filter},
	pages = {5249--5260.e5},
}

@incollection{locke_effect_2020,
	series = {American {Thoracic} {Society} {International} {Conference} {Abstracts}},
	title = {The {Effect} of {OSA} {Severity} and {CPAP} {Adherence} on {Weight} {Regain} {After} {Bariatric} {Surgery}},
	url = {https://www.atsjournals.org/doi/abs/10.1164/ajrccm-conference.2020.201.1_MeetingAbstracts.A2439},
	doi = {10.1164/ajrccm-conference.2020.201.1_MeetingAbstracts.A2439},
	urldate = {2024-12-13},
	booktitle = {A69. {SRN}: {CLINICAL} {AND} {PHYSIOLOGICAL} {INSIGHTS} {INTO} {THE} {MANAGEMENT} {OF} {SDB}},
	publisher = {American Thoracic Society},
	author = {Locke, B.w. and Kim, J. and Bennion, J. and Prasad, C. and Volckmann, E. and Sundar, K.},
	month = may,
	year = {2020},
	pages = {A2439--A2439},
}

@incollection{sellman_spectrum_2020,
	series = {American {Thoracic} {Society} {International} {Conference} {Abstracts}},
	title = {Spectrum of {Central} {Sleep} {Apnea} at an {Academic} {Sleep} {Center} {Between} 2016-2018 and {Treatment} {Responses} to {CPAP} vs. {Adaptive} {Servo} {Ventilation}},
	url = {https://www.atsjournals.org/doi/abs/10.1164/ajrccm-conference.2020.201.1_MeetingAbstracts.A2430},
	doi = {10.1164/ajrccm-conference.2020.201.1_MeetingAbstracts.A2430},
	urldate = {2024-12-13},
	booktitle = {A69. {SRN}: {CLINICAL} {AND} {PHYSIOLOGICAL} {INSIGHTS} {INTO} {THE} {MANAGEMENT} {OF} {SDB}},
	publisher = {American Thoracic Society},
	author = {Sellman, J. and Locke, B.w. and McFarland, J. and Uribe, F. and Sundar, K.},
	month = may,
	year = {2020},
	pages = {A2430--A2430},
}

@article{anton_creating_2020,
	title = {Creating a {COVID}-19 {Action} {Plan} for {GME} {Programs}},
	volume = {12},
	issn = {1949-8349},
	url = {https://doi.org/10.4300/JGME-D-20-00206.1},
	doi = {10.4300/JGME-D-20-00206.1},
	number = {4},
	urldate = {2024-12-13},
	journal = {Journal of Graduate Medical Education},
	author = {Anton, Marja and Wright, Josephine and Braithwaite, Matthew and Sturgeon, Greg and Locke, Brian and Milne, Caroline and Crosby, Aaron},
	month = aug,
	year = {2020},
	pages = {399--402},
}

@article{heath_circuit_2020,
	title = {Circuit {Mechanisms} {Underlying} {Chromatic} {Encoding} in {Drosophila} {Photoreceptors}},
	volume = {30},
	issn = {0960-9822},
	url = {https://www.cell.com/current-biology/abstract/S0960-9822(19)31577-5},
	doi = {10.1016/j.cub.2019.11.075},
	language = {English},
	number = {2},
	urldate = {2024-12-11},
	journal = {Current Biology},
	publisher = {Elsevier},
	author = {Heath, Sarah L. and Christenson, Matthias P. and Oriol, Elie and Saavedra-Weisenhaus, Maia and Kohn, Jessica R. and Behnia, Rudy},
	month = jan,
	year = {2020},
	keywords = {Drosophila melanogaster, color model, color opponency, color vision, convergent evolution, horizontal cell, medulla, neural circuit, photon capture, photoreceptor},
	pages = {264--275.e8},
}

@article{locke_enhancing_2017,
	title = {Enhancing medical student quality improvement education through immersion},
	volume = {51},
	issn = {1365-2923},
	doi = {10.1111/medu.13313},
	language = {eng},
	number = {5},
	journal = {Medical Education},
	author = {Locke, Brian and Kneeland, Patrick},
	month = may,
	year = {2017},
	keywords = {Clinical Competence, Competency-Based Education, Curriculum, Education, Medical, Undergraduate, Humans, Quality Improvement, Students, Medical},
	pages = {549--550},
}

@article{brandenburg_mountain_2017,
	title = {Mountain medical kits: epidemiology-based recommendations and analysis of medical supplies carried by mountain climbers in {Colorado}},
	volume = {24},
	shorttitle = {Mountain medical kits},
	url = {https://academic.oup.com/jtm/article-abstract/24/2/taw088/2930765},
	number = {2},
	urldate = {2024-12-13},
	journal = {Journal of travel medicine},
	publisher = {Oxford University Press},
	author = {Brandenburg, William E. and Locke, Brian W.},
	year = {2017},
	pages = {taw088},
}

@article{livingstone_symbol_2014,
	title = {Symbol addition by monkeys provides evidence for normalized quantity coding},
	volume = {111},
	url = {http://www.pnas.org/content/111/18/6822.short},
	doi = {10.1073/pnas.1404208111},
	number = {18},
	urldate = {2015-01-07},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Livingstone, Margaret S. and Pettine, Warren W. and Srihasam, Krishna and Moore, Brandon and Morocz, Istvan A. and Lee, Daeyeol},
	year = {2014},
	keywords = {macaque, normalization, number sense, value coding},
	pages = {6822--6827},
}

@article{nemteanu_scalable_nodate,
	title = {Scalable {Agile} {Framework} for {Execution} in {AI} ({SAFE}‑{AI}) for {Medical} {AI} {Ethics} {Policy} {Design} in {Small} and {Medium}‑{Sized} {Enterprises}},
	url = {https://preprints.jmir.org/preprint/80028},
	doi = {10.2196/preprints.80028},
	abstract = {Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet},
	language = {en},
	urldate = {2026-01-16},
	journal = {JMIR Preprints},
	author = {Nemteanu, Ion and Mancebo Jr, Adir and Joe, Leslie and Lopez, Ryan and Lopez, Patricia and Pettine, Warren},
}

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

@article{aberegg_full_nodate,
	title = {Full title: {Hickam}’s {Dictum}: {An} {Analysis} of {Multiple} {Diagnoses} {Running} title: {Analysis} of {Multiple} {Diagnoses}},
	shorttitle = {Full title},
	url = {https://www.newswise.com/pdf_docs/17293520117628_Hickam_9-19_R1_final-clean.pdf},
	urldate = {2024-12-13},
	author = {Aberegg, Scott K. and Poole, Brian R. and Locke, Brian W.},
}

@article{locke_electronic_nodate,
	title = {Electronic health record–derived outcomes in obstructive sleep apnea managed with positive airway pressure tracking systems},
	volume = {18},
	url = {https://jcsm.aasm.org/doi/full/10.5664/jcsm.9750},
	doi = {10.5664/jcsm.9750},
	abstract = {Study Objectives:To assess the effectiveness of continuous positive airway pressure (CPAP) management guided by CPAP machine downloads in newly diagnosed patients with obstructive sleep apnea (OSA) using electronic health record–derived health care utilization, biometric variables, and laboratory data.Methods:Electronic health record data of patients seen at the University of Utah Sleep Program from 2012–2015 were reviewed to identify patients with new diagnosis of OSA in whom CPAP adherence and residual apnea-hypopnea index as measured by a positive airway pressure adherence tracking device data for ≥ 1 year were available. Biometric data, laboratory data, and system-wide charges were compared in the 1 year before and after CPAP therapy. Subgroups were divided by whether patients met tracking criteria, mean nightly usage, and OSA severity.Results:976 consecutive, newly diagnosed participants with OSA (median age 55 years, 56.6\% male) met inclusion criteria. There was a mean decrease of systolic blood pressure (BP) of 1.2 mm Hg and diastolic BP of 1.0 mm Hg within a year of initiation of CPAP therapy. BP improvements in the subgroup meeting CPAP tracking targets were 1.36 mmHg (systolic) and 1.37 mmHg (diastolic). No significant change was noted in body mass index, glycated hemoglobin, or serum creatinine values within a year of starting CPAP therapy, and health care utilization increased (mean acute care visits 0.22 per year to 0.53 per year; mean charges of \$3,997 per year to \$8,986 per year).Conclusions:An improvement in BP was noted within a year of CPAP therapy in newly diagnosed patients with OSA, with no difference in the magnitude of improvement between those meeting tracking system adherence targets.Citation:Locke BW, Neill SE, Howe HE, Crotty MC, Kim J, Sundar KM. Electronic health record–derived outcomes in obstructive sleep apnea managed with positive airway pressure tracking systems. J Clin Sleep Med. 2022;18(3):885–894.},
	number = {3},
	urldate = {2024-12-13},
	journal = {Journal of Clinical Sleep Medicine},
	publisher = {American Academy of Sleep Medicine},
	author = {Locke, Brian W. and Neill, Sarah E. and Howe, Heather E. and Crotty, Michael C. and Kim, Jaewhan and Sundar, Krishna M.},
	keywords = {continuous positive airway pressure, obstructive sleep apnea, treatment outcome},
	pages = {885--894},
}

Study Objectives:To assess the effectiveness of continuous positive airway pressure (CPAP) management guided by CPAP machine downloads in newly diagnosed patients with obstructive sleep apnea (OSA) using electronic health record–derived health care utilization, biometric variables, and laboratory data.Methods:Electronic health record data of patients seen at the University of Utah Sleep Program from 2012–2015 were reviewed to identify patients with new diagnosis of OSA in whom CPAP adherence and residual apnea-hypopnea index as measured by a positive airway pressure adherence tracking device data for ≥ 1 year were available. Biometric data, laboratory data, and system-wide charges were compared in the 1 year before and after CPAP therapy. Subgroups were divided by whether patients met tracking criteria, mean nightly usage, and OSA severity.Results:976 consecutive, newly diagnosed participants with OSA (median age 55 years, 56.6% male) met inclusion criteria. There was a mean decrease of systolic blood pressure (BP) of 1.2 mm Hg and diastolic BP of 1.0 mm Hg within a year of initiation of CPAP therapy. BP improvements in the subgroup meeting CPAP tracking targets were 1.36 mmHg (systolic) and 1.37 mmHg (diastolic). No significant change was noted in body mass index, glycated hemoglobin, or serum creatinine values within a year of starting CPAP therapy, and health care utilization increased (mean acute care visits 0.22 per year to 0.53 per year; mean charges of $3,997 per year to $8,986 per year).Conclusions:An improvement in BP was noted within a year of CPAP therapy in newly diagnosed patients with OSA, with no difference in the magnitude of improvement between those meeting tracking system adherence targets.Citation:Locke BW, Neill SE, Howe HE, Crotty MC, Kim J, Sundar KM. Electronic health record–derived outcomes in obstructive sleep apnea managed with positive airway pressure tracking systems. J Clin Sleep Med. 2022;18(3):885–894.

@article{locke_severity_nodate,
	title = {Severity, comorbidities, and adherence to therapy in {Native} {Hawaiians}/{Pacific} {Islanders} with obstructive sleep apnea},
	volume = {19},
	url = {https://jcsm.aasm.org/doi/full/10.5664/jcsm.10472},
	doi = {10.5664/jcsm.10472},
	abstract = {Study Objectives:Considerable disparities in the prevalence, diagnosis, and management of obstructive sleep apnea (OSA) exist for minority groups in the United States. However, the impact of OSA on Native Hawaiian/Pacific Islanders (NHPIs) has not been evaluated.Methods:We performed a retrospective review of patient records of NHPIs who underwent sleep apnea testing between 2014 and 2021 at a single center in Utah to characterize the distribution of comorbidities, disease severity, and treatment adherence.Results:140 of 141 NHPI patients who underwent sleep testing had OSA. High rates of obesity (94\%) and other relevant comorbidities were found. OSA was mostly severe (57\%), particularly in males with higher obesity. Adherence to continuous positive airway pressure was low (41\% using for 4 or more hours on 70\% of nights), and medical factors predicted whether adherence targets were met with modest accuracy (area under the curve 0.699).Conclusions:NHPIs at a single sleep center had a high prevalence of comorbidities in association with OSA, a skewed distribution toward severe disease suggestive of barriers to care or unique disease characteristics, and low adherence to continuous positive airway pressure. These findings suggest a high burden of OSA in this population, and further work to characterize barriers to identifying and treating OSA in NHPIs can improve chronic disease outcomes in NHPIs.Citation:Locke BW, Sundar DJ, Ryujin D. Severity, comorbidities, and adherence to therapy in Native Hawaiians/Pacific Islanders with obstructive sleep apnea. J Clin Sleep Med. 2023;19(5):967–974.},
	number = {5},
	urldate = {2024-12-13},
	journal = {Journal of Clinical Sleep Medicine},
	publisher = {American Academy of Sleep Medicine},
	author = {Locke, Brian W. and Sundar, Divya J. and Ryujin, Darin},
	keywords = {Native Hawaiian/Pacific Islander (NHPI), continuous positive airway pressure, obstructive sleep apnea, racial disparity},
	pages = {967--974},
}

Study Objectives:Considerable disparities in the prevalence, diagnosis, and management of obstructive sleep apnea (OSA) exist for minority groups in the United States. However, the impact of OSA on Native Hawaiian/Pacific Islanders (NHPIs) has not been evaluated.Methods:We performed a retrospective review of patient records of NHPIs who underwent sleep apnea testing between 2014 and 2021 at a single center in Utah to characterize the distribution of comorbidities, disease severity, and treatment adherence.Results:140 of 141 NHPI patients who underwent sleep testing had OSA. High rates of obesity (94%) and other relevant comorbidities were found. OSA was mostly severe (57%), particularly in males with higher obesity. Adherence to continuous positive airway pressure was low (41% using for 4 or more hours on 70% of nights), and medical factors predicted whether adherence targets were met with modest accuracy (area under the curve 0.699).Conclusions:NHPIs at a single sleep center had a high prevalence of comorbidities in association with OSA, a skewed distribution toward severe disease suggestive of barriers to care or unique disease characteristics, and low adherence to continuous positive airway pressure. These findings suggest a high burden of OSA in this population, and further work to characterize barriers to identifying and treating OSA in NHPIs can improve chronic disease outcomes in NHPIs.Citation:Locke BW, Sundar DJ, Ryujin D. Severity, comorbidities, and adherence to therapy in Native Hawaiians/Pacific Islanders with obstructive sleep apnea. J Clin Sleep Med. 2023;19(5):967–974.