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Predicting asthma control deterioration in children

机译：预测儿童哮喘控制恶化

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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.
机译：背景儿科哮喘影响了710万美国儿童，每年直接医疗总费用约为93亿美元。儿童哮喘控制效果欠佳，导致哮喘发作频繁，费用高昂和生活质量下降。成功预测个体患者哮喘控制恶化的风险将增强自我管理，并使早期干预措施减少哮喘加重。我们开发并测试了第一套模型，用于预测儿童哮喘发作前一周的恶化情况。方法我们先前报道了哮喘症状追踪器（哮喘每周自我监测工具）的有效性。在两年的时间里，我们使用此工具收集了210名儿童的每周总计2912例哮喘控制评估。我们将哮喘控制数据集与患者属性和环境变量结合在一起，开发了机器学习模型，以预测孩子一周后哮喘控制的恶化情况。结果我们最好的模型达到了71.8％的准确度，73.8％的灵敏度，71.4％的特异性，以及接收器工作特性曲线下的面积0.757。我们还确定了对模型的潜在改进，以刺激对该主题的进一步研究。结论我们的最佳模型成功地预测了孩子一周后的哮喘控制水平。该模型具有足够的准确性，可以集成到电子哮喘自我监控系统中，以提供实时决策支持和潜在的哮喘控制恶化的个性化预警。

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