预测儿童哮喘控制恶化
2015/12/03
摘要
背景：在美国有7100000儿童患有小儿哮喘，这些患儿每年直接的医疗保健花费总额为近93亿美元。儿童哮喘控制欠佳导致了频繁出现的哮喘发作，过度的花费以及生活质量的降低。在患者个体水平成功预测哮喘控制中的恶化风险有助于增强哮喘自我管理并且通过早期干预减少哮喘发作。我们建立并测试了第一套在哮喘发作前一周预测儿童哮喘控制恶化的模型。
方法：我们之前报道了哮喘症状追踪器的获批，这是一种可以每周进行哮喘自我监测的工具。在两年的时间里，我们使用这个工具收集了210个儿童的2912例每周的哮喘控制评估。我们将哮喘控制的数据集与患者属性和环境变量相结合从而建立机器学习模型以提前一周预测一个儿童哮喘控制恶化情况。
结果：我们最好的模型可以达到71.8%的准确度、73.8%的敏感度、71.4%的特异性以及接收器操作特征曲线下的面积为0.757.我们还辨别了模型潜在的改善来激发关于这个课题的进一步研究。
结论：我们最好的模型提前一周成功地预测了一个儿童哮喘控制的水平。这个模型具有较高的准确性，可以整合到电子哮喘自我监测系统中去，从而为潜在的哮喘控制恶化提供实时的决策支持以及个体化的早期预警。
（杨冬 审校）
BMC Med Inform Decis Mak. 2015 Oct 14;15(1):84. doi: 10.1186/s12911-015-0208-9.
Predicting asthma control deterioration in children.
Luo G1, Stone BL2, Fassl B3, Maloney CG4, Gesteland PH5, Yerram SR6, Nkoy FL7.
Abstract
BACKGROUND:Pediatric asthma affects 7.1 million American children incurring an annual total direct healthcare cost around 9.3 billion dollars. Asthma control in children is suboptimal, leading to frequent asthma exacerbations, excess costs, and decreased quality of life. Successful prediction of risk for asthma control deterioration at the individual patient level would enhance self-management and enable early interventions to reduce asthma exacerbations. We developed and tested the first set of models for predicting a child's asthma control deterioration one week prior to occurrence.
METHODS:We previously reported validation of the Asthma Symptom Tracker, a weekly asthma self-monitoring tool. Over a period of two years, we used this tool to collect a total of 2912 weekly assessments of asthma control on 210 children. We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child's asthma control deterioration one week ahead.
RESULTS:Our best model achieved an accuracy of 71.8 %, a sensitivity of 73.8 %, a specificity of 71.4 %, and an area under the receiver operating characteristic curve of 0.757. We also identified potential improvements to our models to stimulate future research on this topic.
CONCLUSIONS:Our best model successfully predicted a child's asthma control level one week ahead. With adequate accuracy, the model could be integrated into electronic asthma self-monitoring systems to provide real-time decision support and personalized early warnings of potential asthma control deteriorations.
BMC Med Inform Decis Mak. 2015 Oct 14;15(1):84. doi: 10.1186/s12911-015-0208-9.
