Risk Factors Associated With Nonfatal Opioid Overdose Leading to Intensive Care Unit Admission: A Cross-sectional Study. Mitra, A., Ahsan, H., Li, W., Liu, W., Kerns, R. D., Tsai, J., Becker, W., Smelson, D. A., & Yu, H. JMIR medical informatics, 9(11):e32851, November, 2021. doi abstract bibtex BACKGROUND: Opioid overdose (OD) and related deaths have significantly increased in the United States over the last 2 decades. Existing studies have mostly focused on demographic and clinical risk factors in noncritical care settings. Social and behavioral determinants of health (SBDH) are infrequently coded in the electronic health record (EHR) and usually buried in unstructured EHR notes, reflecting possible gaps in clinical care and observational research. Therefore, SBDH often receive less attention despite being important risk factors for OD. Natural language processing (NLP) can alleviate this problem. OBJECTIVE: The objectives of this study were two-fold: First, we examined the usefulness of NLP for SBDH extraction from unstructured EHR text, and second, for intensive care unit (ICU) admissions, we investigated risk factors including SBDH for nonfatal OD. METHODS: We performed a cross-sectional analysis of admission data from the EHR of patients in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. We used patient admission data and International Classification of Diseases, Ninth Revision (ICD-9) diagnoses to extract demographics, nonfatal OD, SBDH, and other clinical variables. In addition to obtaining SBDH information from the ICD codes, an NLP model was developed to extract 6 SBDH variables from EHR notes, namely, housing insecurity, unemployment, social isolation, alcohol use, smoking, and illicit drug use. We adopted a sequential forward selection process to select relevant clinical variables. Multivariable logistic regression analysis was used to evaluate the associations with nonfatal OD, and relative risks were quantified as covariate-adjusted odds ratios (aOR). RESULTS: The strongest association with nonfatal OD was found to be drug use disorder (aOR 8.17, 95% CI 5.44-12.27), followed by bipolar disorder (aOR 2.69, 95% CI 1.68-4.29). Among others, major depressive disorder (aOR 2.57, 95% CI 1.12-5.88), being on a Medicaid health insurance program (aOR 2.26, 95% CI 1.43-3.58), history of illicit drug use (aOR 2.09, 95% CI 1.15-3.79), and current use of illicit drugs (aOR 2.06, 95% CI 1.20-3.55) were strongly associated with increased risk of nonfatal OD. Conversely, Blacks (aOR 0.51, 95% CI 0.28-0.94), older age groups (40-64 years: aOR 0.65, 95% CI 0.44-0.96; \textgreater64 years: aOR 0.16, 95% CI 0.08-0.34) and those with tobacco use disorder (aOR 0.53, 95% CI 0.32-0.89) or alcohol use disorder (aOR 0.64, 95% CI 0.42-1.00) had decreased risk of nonfatal OD. Moreover, 99.82% of all SBDH information was identified by the NLP model, in contrast to only 0.18% identified by the ICD codes. CONCLUSIONS: This is the first study to analyze the risk factors for nonfatal OD in an ICU setting using NLP-extracted SBDH from EHR notes. We found several risk factors associated with nonfatal OD including SBDH. SBDH are richly described in EHR notes, supporting the importance of integrating NLP-derived SBDH into OD risk assessment. More studies in ICU settings can help health care systems better understand and respond to the opioid epidemic.
@article{mitra_risk_2021,
title = {Risk {Factors} {Associated} {With} {Nonfatal} {Opioid} {Overdose} {Leading} to {Intensive} {Care} {Unit} {Admission}: {A} {Cross}-sectional {Study}},
volume = {9},
issn = {2291-9694},
shorttitle = {Risk {Factors} {Associated} {With} {Nonfatal} {Opioid} {Overdose} {Leading} to {Intensive} {Care} {Unit} {Admission}},
doi = {10.2196/32851},
abstract = {BACKGROUND: Opioid overdose (OD) and related deaths have significantly increased in the United States over the last 2 decades. Existing studies have mostly focused on demographic and clinical risk factors in noncritical care settings. Social and behavioral determinants of health (SBDH) are infrequently coded in the electronic health record (EHR) and usually buried in unstructured EHR notes, reflecting possible gaps in clinical care and observational research. Therefore, SBDH often receive less attention despite being important risk factors for OD. Natural language processing (NLP) can alleviate this problem.
OBJECTIVE: The objectives of this study were two-fold: First, we examined the usefulness of NLP for SBDH extraction from unstructured EHR text, and second, for intensive care unit (ICU) admissions, we investigated risk factors including SBDH for nonfatal OD.
METHODS: We performed a cross-sectional analysis of admission data from the EHR of patients in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. We used patient admission data and International Classification of Diseases, Ninth Revision (ICD-9) diagnoses to extract demographics, nonfatal OD, SBDH, and other clinical variables. In addition to obtaining SBDH information from the ICD codes, an NLP model was developed to extract 6 SBDH variables from EHR notes, namely, housing insecurity, unemployment, social isolation, alcohol use, smoking, and illicit drug use. We adopted a sequential forward selection process to select relevant clinical variables. Multivariable logistic regression analysis was used to evaluate the associations with nonfatal OD, and relative risks were quantified as covariate-adjusted odds ratios (aOR).
RESULTS: The strongest association with nonfatal OD was found to be drug use disorder (aOR 8.17, 95\% CI 5.44-12.27), followed by bipolar disorder (aOR 2.69, 95\% CI 1.68-4.29). Among others, major depressive disorder (aOR 2.57, 95\% CI 1.12-5.88), being on a Medicaid health insurance program (aOR 2.26, 95\% CI 1.43-3.58), history of illicit drug use (aOR 2.09, 95\% CI 1.15-3.79), and current use of illicit drugs (aOR 2.06, 95\% CI 1.20-3.55) were strongly associated with increased risk of nonfatal OD. Conversely, Blacks (aOR 0.51, 95\% CI 0.28-0.94), older age groups (40-64 years: aOR 0.65, 95\% CI 0.44-0.96; {\textgreater}64 years: aOR 0.16, 95\% CI 0.08-0.34) and those with tobacco use disorder (aOR 0.53, 95\% CI 0.32-0.89) or alcohol use disorder (aOR 0.64, 95\% CI 0.42-1.00) had decreased risk of nonfatal OD. Moreover, 99.82\% of all SBDH information was identified by the NLP model, in contrast to only 0.18\% identified by the ICD codes.
CONCLUSIONS: This is the first study to analyze the risk factors for nonfatal OD in an ICU setting using NLP-extracted SBDH from EHR notes. We found several risk factors associated with nonfatal OD including SBDH. SBDH are richly described in EHR notes, supporting the importance of integrating NLP-derived SBDH into OD risk assessment. More studies in ICU settings can help health care systems better understand and respond to the opioid epidemic.},
language = {eng},
number = {11},
journal = {JMIR medical informatics},
author = {Mitra, Avijit and Ahsan, Hiba and Li, Wenjun and Liu, Weisong and Kerns, Robert D. and Tsai, Jack and Becker, William and Smelson, David A. and Yu, Hong},
month = nov,
year = {2021},
pmid = {34747714},
pmcid = {PMC8663596},
keywords = {electronic health records, intensive care unit, natural language processing, opioids, overdose, risk factors, social and behavioral determinants of health},
pages = {e32851},
}
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{"_id":"FmpNantRvdcfzSbSL","bibbaseid":"mitra-ahsan-li-liu-kerns-tsai-becker-smelson-etal-riskfactorsassociatedwithnonfatalopioidoverdoseleadingtointensivecareunitadmissionacrosssectionalstudy-2021","author_short":["Mitra, A.","Ahsan, H.","Li, W.","Liu, W.","Kerns, R. D.","Tsai, J.","Becker, W.","Smelson, D. A.","Yu, H."],"bibdata":{"bibtype":"article","type":"article","title":"Risk Factors Associated With Nonfatal Opioid Overdose Leading to Intensive Care Unit Admission: A Cross-sectional Study","volume":"9","issn":"2291-9694","shorttitle":"Risk Factors Associated With Nonfatal Opioid Overdose Leading to Intensive Care Unit Admission","doi":"10.2196/32851","abstract":"BACKGROUND: Opioid overdose (OD) and related deaths have significantly increased in the United States over the last 2 decades. Existing studies have mostly focused on demographic and clinical risk factors in noncritical care settings. Social and behavioral determinants of health (SBDH) are infrequently coded in the electronic health record (EHR) and usually buried in unstructured EHR notes, reflecting possible gaps in clinical care and observational research. Therefore, SBDH often receive less attention despite being important risk factors for OD. Natural language processing (NLP) can alleviate this problem. OBJECTIVE: The objectives of this study were two-fold: First, we examined the usefulness of NLP for SBDH extraction from unstructured EHR text, and second, for intensive care unit (ICU) admissions, we investigated risk factors including SBDH for nonfatal OD. METHODS: We performed a cross-sectional analysis of admission data from the EHR of patients in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. We used patient admission data and International Classification of Diseases, Ninth Revision (ICD-9) diagnoses to extract demographics, nonfatal OD, SBDH, and other clinical variables. In addition to obtaining SBDH information from the ICD codes, an NLP model was developed to extract 6 SBDH variables from EHR notes, namely, housing insecurity, unemployment, social isolation, alcohol use, smoking, and illicit drug use. We adopted a sequential forward selection process to select relevant clinical variables. Multivariable logistic regression analysis was used to evaluate the associations with nonfatal OD, and relative risks were quantified as covariate-adjusted odds ratios (aOR). RESULTS: The strongest association with nonfatal OD was found to be drug use disorder (aOR 8.17, 95% CI 5.44-12.27), followed by bipolar disorder (aOR 2.69, 95% CI 1.68-4.29). Among others, major depressive disorder (aOR 2.57, 95% CI 1.12-5.88), being on a Medicaid health insurance program (aOR 2.26, 95% CI 1.43-3.58), history of illicit drug use (aOR 2.09, 95% CI 1.15-3.79), and current use of illicit drugs (aOR 2.06, 95% CI 1.20-3.55) were strongly associated with increased risk of nonfatal OD. Conversely, Blacks (aOR 0.51, 95% CI 0.28-0.94), older age groups (40-64 years: aOR 0.65, 95% CI 0.44-0.96; \\textgreater64 years: aOR 0.16, 95% CI 0.08-0.34) and those with tobacco use disorder (aOR 0.53, 95% CI 0.32-0.89) or alcohol use disorder (aOR 0.64, 95% CI 0.42-1.00) had decreased risk of nonfatal OD. Moreover, 99.82% of all SBDH information was identified by the NLP model, in contrast to only 0.18% identified by the ICD codes. CONCLUSIONS: This is the first study to analyze the risk factors for nonfatal OD in an ICU setting using NLP-extracted SBDH from EHR notes. We found several risk factors associated with nonfatal OD including SBDH. SBDH are richly described in EHR notes, supporting the importance of integrating NLP-derived SBDH into OD risk assessment. More studies in ICU settings can help health care systems better understand and respond to the opioid epidemic.","language":"eng","number":"11","journal":"JMIR medical informatics","author":[{"propositions":[],"lastnames":["Mitra"],"firstnames":["Avijit"],"suffixes":[]},{"propositions":[],"lastnames":["Ahsan"],"firstnames":["Hiba"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Wenjun"],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Weisong"],"suffixes":[]},{"propositions":[],"lastnames":["Kerns"],"firstnames":["Robert","D."],"suffixes":[]},{"propositions":[],"lastnames":["Tsai"],"firstnames":["Jack"],"suffixes":[]},{"propositions":[],"lastnames":["Becker"],"firstnames":["William"],"suffixes":[]},{"propositions":[],"lastnames":["Smelson"],"firstnames":["David","A."],"suffixes":[]},{"propositions":[],"lastnames":["Yu"],"firstnames":["Hong"],"suffixes":[]}],"month":"November","year":"2021","pmid":"34747714","pmcid":"PMC8663596","keywords":"electronic health records, intensive care unit, natural language processing, opioids, overdose, risk factors, social and behavioral determinants of health","pages":"e32851","bibtex":"@article{mitra_risk_2021,\n\ttitle = {Risk {Factors} {Associated} {With} {Nonfatal} {Opioid} {Overdose} {Leading} to {Intensive} {Care} {Unit} {Admission}: {A} {Cross}-sectional {Study}},\n\tvolume = {9},\n\tissn = {2291-9694},\n\tshorttitle = {Risk {Factors} {Associated} {With} {Nonfatal} {Opioid} {Overdose} {Leading} to {Intensive} {Care} {Unit} {Admission}},\n\tdoi = {10.2196/32851},\n\tabstract = {BACKGROUND: Opioid overdose (OD) and related deaths have significantly increased in the United States over the last 2 decades. Existing studies have mostly focused on demographic and clinical risk factors in noncritical care settings. Social and behavioral determinants of health (SBDH) are infrequently coded in the electronic health record (EHR) and usually buried in unstructured EHR notes, reflecting possible gaps in clinical care and observational research. Therefore, SBDH often receive less attention despite being important risk factors for OD. Natural language processing (NLP) can alleviate this problem.\nOBJECTIVE: The objectives of this study were two-fold: First, we examined the usefulness of NLP for SBDH extraction from unstructured EHR text, and second, for intensive care unit (ICU) admissions, we investigated risk factors including SBDH for nonfatal OD.\nMETHODS: We performed a cross-sectional analysis of admission data from the EHR of patients in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. We used patient admission data and International Classification of Diseases, Ninth Revision (ICD-9) diagnoses to extract demographics, nonfatal OD, SBDH, and other clinical variables. In addition to obtaining SBDH information from the ICD codes, an NLP model was developed to extract 6 SBDH variables from EHR notes, namely, housing insecurity, unemployment, social isolation, alcohol use, smoking, and illicit drug use. We adopted a sequential forward selection process to select relevant clinical variables. Multivariable logistic regression analysis was used to evaluate the associations with nonfatal OD, and relative risks were quantified as covariate-adjusted odds ratios (aOR).\nRESULTS: The strongest association with nonfatal OD was found to be drug use disorder (aOR 8.17, 95\\% CI 5.44-12.27), followed by bipolar disorder (aOR 2.69, 95\\% CI 1.68-4.29). Among others, major depressive disorder (aOR 2.57, 95\\% CI 1.12-5.88), being on a Medicaid health insurance program (aOR 2.26, 95\\% CI 1.43-3.58), history of illicit drug use (aOR 2.09, 95\\% CI 1.15-3.79), and current use of illicit drugs (aOR 2.06, 95\\% CI 1.20-3.55) were strongly associated with increased risk of nonfatal OD. Conversely, Blacks (aOR 0.51, 95\\% CI 0.28-0.94), older age groups (40-64 years: aOR 0.65, 95\\% CI 0.44-0.96; {\\textgreater}64 years: aOR 0.16, 95\\% CI 0.08-0.34) and those with tobacco use disorder (aOR 0.53, 95\\% CI 0.32-0.89) or alcohol use disorder (aOR 0.64, 95\\% CI 0.42-1.00) had decreased risk of nonfatal OD. Moreover, 99.82\\% of all SBDH information was identified by the NLP model, in contrast to only 0.18\\% identified by the ICD codes.\nCONCLUSIONS: This is the first study to analyze the risk factors for nonfatal OD in an ICU setting using NLP-extracted SBDH from EHR notes. We found several risk factors associated with nonfatal OD including SBDH. SBDH are richly described in EHR notes, supporting the importance of integrating NLP-derived SBDH into OD risk assessment. More studies in ICU settings can help health care systems better understand and respond to the opioid epidemic.},\n\tlanguage = {eng},\n\tnumber = {11},\n\tjournal = {JMIR medical informatics},\n\tauthor = {Mitra, Avijit and Ahsan, Hiba and Li, Wenjun and Liu, Weisong and Kerns, Robert D. and Tsai, Jack and Becker, William and Smelson, David A. and Yu, Hong},\n\tmonth = nov,\n\tyear = {2021},\n\tpmid = {34747714},\n\tpmcid = {PMC8663596},\n\tkeywords = {electronic health records, intensive care unit, natural language processing, opioids, overdose, risk factors, social and behavioral determinants of health},\n\tpages = {e32851},\n}\n\n","author_short":["Mitra, A.","Ahsan, H.","Li, W.","Liu, W.","Kerns, R. D.","Tsai, J.","Becker, W.","Smelson, D. 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