医学
哮喘
队列
恶化
逻辑回归
哮喘恶化
置信区间
内科学
曲线下面积
梯度升压
随机森林
机器学习
计算机科学
作者
Jonathan Inselman,Molly M. Jeffery,Jacob T. Maddux,Regina Lam,Nilay D. Shah,Matthew A. Rank,Che Ngufor
标识
DOI:10.1016/j.anai.2022.11.025
摘要
Abstract
Background
Little is known regarding the prediction of the risks of asthma exacerbation after stopping asthma biologics. Objective
To develop and validate a predictive model for the risk of asthma exacerbations after stopping asthma biologics using machine learning models. Methods
We identified 3057 people with asthma who stopped asthma biologics in the OptumLabs Database Warehouse and considered a wide range of demographic and clinical risk factors to predict subsequent outcomes. The primary outcome used to assess success after stopping was having no exacerbations in the 6 months after stopping the biologic. Elastic-net logistic regression (GLMnet), random forest, and gradient boosting machine models were used with 10-fold cross-validation within a development (80%) cohort and validation cohort (20%). Results
The mean age of the total cohort was 47.1 (SD, 17.1) years, 1859 (60.8%) were women, 2261 (74.0%) were White, and 1475 (48.3%) were in the Southern region of the United States. The elastic-net logistic regression model yielded an area under the curve (AUC) of 0.75 (95% confidence interval [CI], 0.71-0.78) in the development and an AUC of 0.72 in the validation cohort. The random forest model yielded an AUC of 0.75 (95% CI, 0.68-0.79) in the development cohort and an AUC of 0.72 in the validation cohort. The gradient boosting machine model yielded an AUC of 0.76 (95% CI, 0.72-0.80) in the development cohort and an AUC of 0.74 in the validation cohort. Conclusion
Outcomes after stopping asthma biologics can be predicted with moderate accuracy using machine learning methods.
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