Individualised risk prediction model for exacerbations in patients with severe asthma: Protocol for a multicentre real-world risk modelling study

Tae Yoon Lee; Mohsen Sadatsafavi; Chandra Prakash Yadav; David B. Price; Richard Beasley; Christer Janson; Mariko Siyue Koh; Rupsa Roy; Wenjia Chen

doi:10.1136/bmjopen-2022-070459

Individualised risk prediction model for exacerbations in patients with severe asthma: Protocol for a multicentre real-world risk modelling study

Tae Yoon Lee, Mohsen Sadatsafavi, Chandra Prakash Yadav, David B. Price, Richard Beasley, Christer Janson, Mariko Siyue Koh, Rupsa Roy, Wenjia Chen^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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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).

Original language	English
Article number	e070459
Number of pages	7
Journal	BMJ Open
Volume	13
Issue number	3
DOIs	https://doi.org/10.1136/bmjopen-2022-070459
Publication status	Published - 9 Mar 2023

Bibliographical note

Funding Information:
This work was supported by Singapore Ministry of Education Tier 1 Grant (22-4820-A0001-0).

Acknowledgments
We thank Professor Helen Reddle (The Woolcock Institute of Medical Research and The University of Sydney, Sydney, Australia) for her substantial contribution to the study design, statistical analysis plan and access to the NOVELTY data.

Keywords

asthma
preventive medicine
risk management
Multicenter Studies as Topic
Humans
Cost of Illness
Asthma/epidemiology
Longitudinal Studies

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Cite this

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title = "Individualised risk prediction model for exacerbations in patients with severe asthma: Protocol for a multicentre real-world risk modelling study",

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).",

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note = "Funding Information: This work was supported by Singapore Ministry of Education Tier 1 Grant (22-4820-A0001-0). Acknowledgments We thank Professor Helen Reddle (The Woolcock Institute of Medical Research and The University of Sydney, Sydney, Australia) for her substantial contribution to the study design, statistical analysis plan and access to the NOVELTY data.",

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AU - Sadatsafavi, Mohsen

AU - Yadav, Chandra Prakash

AU - Price, David B.

AU - Beasley, Richard

AU - Janson, Christer

AU - Koh, Mariko Siyue

AU - Roy, Rupsa

AU - Chen, Wenjia

N1 - Funding Information: This work was supported by Singapore Ministry of Education Tier 1 Grant (22-4820-A0001-0). Acknowledgments We thank Professor Helen Reddle (The Woolcock Institute of Medical Research and The University of Sydney, Sydney, Australia) for her substantial contribution to the study design, statistical analysis plan and access to the NOVELTY data.

PY - 2023/3/9

Y1 - 2023/3/9

N2 - 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).

AB - 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).

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Individualised risk prediction model for exacerbations in patients with severe asthma: Protocol for a multicentre real-world risk modelling study

Abstract

Bibliographical note

Keywords

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