Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
BMJ Open. 2023 Mar 9;13(3):e070459. doi: 10.1136/bmjopen-2022-070459.
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.
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.
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.
European Union electronic Register of Post-Authorisation Studies, EU PAS Register (EUPAS46088).
重度哮喘与不成比例的高疾病负担相关，包括严重加重的风险。准确预测严重加重的风险可能使临床医生能够根据个体患者的情况定制治疗计划。本研究旨在开发和验证一种用于预测重度哮喘患者严重加重风险的新型预测模型，并检验该工具的潜在临床实用性。
目标人群为年龄在 18 岁或以上的重度哮喘患者。基于国际重度哮喘登记处（n=8925）的数据，使用惩罚零膨胀计数模型来开发预测模型，该模型预测未来 12 个月内加重的发生率或风险。该风险预测工具将在国际观察性队列 NOVEL 观察性纵向研究（n=1652）中，对经过医生评估的重度哮喘患者进行外部验证。验证将包括检查模型校准（即观察到的和预测的发生率之间的一致性）、模型区分度（即模型区分高风险和低风险个体的程度）以及在一系列风险阈值下的临床实用性。
本研究已获得新加坡国立大学机构审查委员会（NUS-IRB-2021-877）、匿名数据伦理和方案透明度委员会（ADEPT1924）和不列颠哥伦比亚大学（H22-01737）的伦理批准。结果将在国际同行评议期刊上发表。
欧盟药品上市后监测研究电子注册处，EUPAS Register（EUPAS46088）。
