Predicting asthma control deterioration in children.
Predicting asthma control deterioration in children.
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DOI：
10.1186/s12911-015-0208-9
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发表时间：
2015-10-14
影响因子：
3.5
通讯作者：
Nkoy FL
中科院分区：
文献类型：
--
作者：
Luo G;Stone BL;Fassl B;Maloney CG;Gesteland PH;Yerram SR;Nkoy FL
小儿哮喘影响着 710 万美国儿童，每年产生的直接医疗费用总额约为 93 亿美元。儿童哮喘控制不理想，导致哮喘频繁恶化、费用过高和生活质量下降。在个体患者层面成功预测哮喘控制恶化的风险将增强自我管理并能够进行早期干预以减少哮喘恶化。我们开发并测试了第一组模型，用于在发生前一周预测儿童哮喘控制恶化。我们之前报道了哮喘症状追踪器的验证，这是一种每周哮喘自我监测工具。在两年的时间里，我们使用该工具收集了 210 名儿童总共 2912 次每周的哮喘控制评估。我们将哮喘控制数据集与患者属性和环境变量相结合，开发机器学习模型来预测儿童哮喘控制提前一周恶化。我们的最佳模型的准确度为 71.8%，灵敏度为 73.8%，特异性为 71.4%，受试者工作特征曲线下面积为 0.757。我们还确定了模型的潜在改进，以刺激未来对该主题的研究。我们最好的模型提前一周成功预测了儿童的哮喘控制水平。如果具有足够的准确性，该模型可以集成到电子哮喘自我监测系统中，以提供实时决策支持和针对潜在的哮喘控制恶化的个性化早期预警。
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. 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. 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. 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.