Das Lala T, Abramson Erika L, Stone Anne E, Kondrich Janienne E, Kern Lisa M, Grinspan Zachary M
Department of Healthcare Policy & Research, Weill Cornell Medicine, New York, New York.
Department of Pediatrics, Weill Cornell Medicine, New York, New York.
Pediatr Pulmonol. 2017 Jul;52(7):880-890. doi: 10.1002/ppul.23735. Epub 2017 May 30.
For children with asthma, emergency department (ED) visits are common, expensive, and often avoidable. Though several factors are associated with ED use (demographics, comorbidities, insurance, medications), its predictability using electronic health record (EHR) data is understudied.
We used a retrospective cohort study design and EHR data from one center to examine the relationship of patient factors in 1 year (2013) and the likelihood of frequent ED use (≥2 visits) in the following year (2014), using bivariate and multivariable statistics. We applied and compared several machine-learning algorithms to predict frequent ED use, then selected a model based on accuracy, parsimony, and interpretability.
We identified 2691 children. In bivariate analyses, future frequent ED use was associated with demographics, co-morbidities, insurance status, medication history, and use of healthcare resources. Machine learning algorithms had very good AUC (area under the curve) values [0.66-0.87], though fair PPV (positive predictive value) [48-70%] and poor sensitivity [16-27%]. Our final multivariable logistic regression model contained two variables: insurance status and prior ED use. For publicly insured patients, the odds of frequent ED use were 3.1 [2.2-4.5] times that of privately insured patients. Publicly insured patients with 4+ ED visits and privately insured patients with 6+ ED visits in a year had ≥50% probability of frequent ED use the following year. The model had an AUC of 0.86, PPV of 56%, and sensitivity of 23%.
Among children with asthma, prior frequent ED use and insurance status strongly predict future ED use.
对于哮喘患儿而言，急诊科就诊很常见、费用高昂且往往是可避免的。尽管有几个因素与急诊科就诊相关（人口统计学特征、合并症、保险、药物治疗），但利用电子健康记录（EHR）数据对其进行预测的研究较少。
我们采用回顾性队列研究设计，并利用来自一个中心的EHR数据，通过双变量和多变量统计方法，研究2013年患者因素与次年（2014年）频繁急诊科就诊（≥2次就诊）可能性之间的关系。我们应用并比较了几种机器学习算法来预测频繁急诊科就诊情况，然后基于准确性、简约性和可解释性选择了一个模型。
我们识别出2691名儿童。在双变量分析中，未来频繁急诊科就诊与人口统计学特征、合并症、保险状况、用药史以及医疗资源使用情况相关。机器学习算法的曲线下面积（AUC）值非常好[0.66 - 0.87]，但阳性预测值（PPV）一般[48 - 70%]，敏感性较差[16 - 27%]。我们最终的多变量逻辑回归模型包含两个变量：保险状况和既往急诊科就诊情况。对于参加公共保险的患者，频繁急诊科就诊的几率是参加私人保险患者的3.1[2.2 - 4.5]倍。一年中有4次及以上急诊科就诊的参加公共保险患者和有6次及以上急诊科就诊的参加私人保险患者，次年频繁急诊科就诊的概率≥50%。该模型的AUC为0.86，PPV为56%，敏感性为23%。
在哮喘患儿中，既往频繁急诊科就诊和保险状况能有力地预测未来的急诊科就诊情况。
