Individualised risk prediction model for exacerbations in patients with severe asthma: protocol for a multicentre real-world risk modelling study. Lee, T. Y., Sadatsafavi, M., Yadav, C. P., Price, D. B., Beasley, R., Janson, C., Koh, M. S., Roy, R., & Chen, W. BMJ open, 13(3):e070459, March, 2023. doi abstract bibtex INTRODUCTION: Severe asthma is associated with a disproportionally high disease burden, including the risk of severe exacerbations. Accurate prediction of the risk of severe exacerbations may enable clinicians to tailor treatment plans to an individual patient. This study aims to develop and validate a novel risk prediction model for severe exacerbations in patients with severe asthma, and to examine the potential clinical utility of this tool. METHODS AND ANALYSIS: The target population is patients aged 18 years or older with severe asthma. Based on the data from the International Severe Asthma Registry (n=8925), a prediction model will be developed using a penalised, zero-inflated count model that predicts the rate or risk of exacerbation in the next 12 months. The risk prediction tool will be externally validated among patients with physician-assessed severe asthma in an international observational cohort, the NOVEL observational longiTudinal studY (n=1652). Validation will include examining model calibration (ie, the agreement between observed and predicted rates), model discrimination (ie, the extent to which the model can distinguish between high-risk and low-risk individuals) and the clinical utility at a range of risk thresholds. ETHICS AND DISSEMINATION: This study has obtained ethics approval from the Institutional Review Board of National University of Singapore (NUS-IRB-2021-877), the Anonymised Data Ethics and Protocol Transparency Committee (ADEPT1924) and the University of British Columbia (H22-01737). Results will be published in an international peer-reviewed journal. TRIAL REGISTRATION NUMBER: European Union electronic Register of Post-Authorisation Studies, EU PAS Register (EUPAS46088).
@article{lee_individualised_2023,
title = {Individualised risk prediction model for exacerbations in patients with severe asthma: protocol for a multicentre real-world risk modelling study},
volume = {13},
issn = {2044-6055},
shorttitle = {Individualised risk prediction model for exacerbations in patients with severe asthma},
doi = {10.1136/bmjopen-2022-070459},
abstract = {INTRODUCTION: Severe asthma is associated with a disproportionally high disease burden, including the risk of severe exacerbations. Accurate prediction of the risk of severe exacerbations may enable clinicians to tailor treatment plans to an individual patient. This study aims to develop and validate a novel risk prediction model for severe exacerbations in patients with severe asthma, and to examine the potential clinical utility of this tool.
METHODS AND ANALYSIS: The target population is patients aged 18 years or older with severe asthma. Based on the data from the International Severe Asthma Registry (n=8925), a prediction model will be developed using a penalised, zero-inflated count model that predicts the rate or risk of exacerbation in the next 12 months. The risk prediction tool will be externally validated among patients with physician-assessed severe asthma in an international observational cohort, the NOVEL observational longiTudinal studY (n=1652). Validation will include examining model calibration (ie, the agreement between observed and predicted rates), model discrimination (ie, the extent to which the model can distinguish between high-risk and low-risk individuals) and the clinical utility at a range of risk thresholds.
ETHICS AND DISSEMINATION: This study has obtained ethics approval from the Institutional Review Board of National University of Singapore (NUS-IRB-2021-877), the Anonymised Data Ethics and Protocol Transparency Committee (ADEPT1924) and the University of British Columbia (H22-01737). Results will be published in an international peer-reviewed journal.
TRIAL REGISTRATION NUMBER: European Union electronic Register of Post-Authorisation Studies, EU PAS Register (EUPAS46088).},
language = {eng},
number = {3},
journal = {BMJ open},
author = {Lee, Tae Yoon and Sadatsafavi, Mohsen and Yadav, Chandra Prakash and Price, David B. and Beasley, Richard and Janson, Christer and Koh, Mariko Siyue and Roy, Rupsa and Chen, Wenjia},
month = mar,
year = {2023},
pmid = {36894199},
pmcid = {PMC10008482},
keywords = {Asthma, Cost of Illness, Humans, Longitudinal Studies, Multicenter Studies as Topic, asthma, preventive medicine, risk management},
pages = {e070459},
}
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Accurate prediction of the risk of severe exacerbations may enable clinicians to tailor treatment plans to an individual patient. This study aims to develop and validate a novel risk prediction model for severe exacerbations in patients with severe asthma, and to examine the potential clinical utility of this tool. METHODS AND ANALYSIS: The target population is patients aged 18 years or older with severe asthma. Based on the data from the International Severe Asthma Registry (n=8925), a prediction model will be developed using a penalised, zero-inflated count model that predicts the rate or risk of exacerbation in the next 12 months. The risk prediction tool will be externally validated among patients with physician-assessed severe asthma in an international observational cohort, the NOVEL observational longiTudinal studY (n=1652). Validation will include examining model calibration (ie, the agreement between observed and predicted rates), model discrimination (ie, the extent to which the model can distinguish between high-risk and low-risk individuals) and the clinical utility at a range of risk thresholds. ETHICS AND DISSEMINATION: This study has obtained ethics approval from the Institutional Review Board of National University of Singapore (NUS-IRB-2021-877), the Anonymised Data Ethics and Protocol Transparency Committee (ADEPT1924) and the University of British Columbia (H22-01737). Results will be published in an international peer-reviewed journal. 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