Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine, Uppsala University, Uppsala, Sweden.
Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine, Uppsala University, Uppsala, Sweden.
Respir Med. 2021 Aug-Sep;185:106483. doi: 10.1016/j.rmed.2021.106483. Epub 2021 May 26.
The ability to predict impending asthma exacerbations may allow better utilization of healthcare resources, prevention of hospitalization and improve patient outcomes. We aimed to develop models using machine learning to predict risk of exacerbations.
Data from 29,396 asthma patients was collected from electronic medical records and national registers covering clinical and epidemiological factors (e.g. comorbidities, health care contacts), between 2000 and 2013. Machine-learning classifiers were used to create models to predict exacerbations within the next 15 days. Model selection was done using the mean cross validation score of area under precision-recall curve (AUPRC).
The most important predictors of exacerbation were comorbidity burden and previous exacerbations. Model validation on test data yielded an AUPRC = 0.007 (95% CI: ± 0.0002), indicating that historic clinical information alone may not be sufficient to predict a near future risk of asthma exacerbation.
Supplementation with additional data on environmental triggers, (e.g. weather, pollen count, air quality) and from wearables, might be necessary to improve performance of the short-term predictive model to develop a more clinically useful tool.
预测哮喘恶化的能力可能会更好地利用医疗资源、预防住院和改善患者预后。我们旨在使用机器学习开发预测恶化风险的模型。
从 2000 年至 2013 年，从电子病历和国家登记处收集了 29396 例哮喘患者的数据，涵盖了临床和流行病学因素（如合并症、医疗保健接触）。使用机器学习分类器创建了预测未来 15 天内恶化的模型。使用精度-召回曲线下面积的平均交叉验证评分（AUPRC）进行模型选择。
恶化的最重要预测因素是合并症负担和既往恶化。在测试数据上进行的模型验证得到了 AUPRC=0.007（95%CI：±0.0002），表明仅基于历史临床信息可能不足以预测哮喘恶化的近期风险。
可能需要补充有关环境触发因素（例如天气、花粉计数、空气质量）和可穿戴设备的数据，以提高短期预测模型的性能，从而开发更具临床意义的工具。
