Gibbs David, Ehwerhemuepha Louis, Moreno Tatiana, Guner Yigit, Yu Peter, Schomberg John, Wallace Elizabeth, Feaster William
CHOC Children's Hospital, Orange, CA, USA.
School of Computational and Data Science, Chapman University, Orange, CA, USA.
Pediatr Res. 2021 Aug;90(2):464-471. doi: 10.1038/s41390-020-01237-0. Epub 2020 Nov 12.
In this study, trauma-specific risk factors of prolonged length of stay (LOS) in pediatric trauma were examined. Statistical and machine learning models were used to proffer ways to improve the quality of care of patients at risk of prolonged length of stay and reduce cost.
Data from 27 hospitals were retrieved on 81,929 hospitalizations of pediatric patients with a primary diagnosis of trauma, and for which the LOS was >24 h. Nested mixed effects model was used for simplified statistical inference, while a stochastic gradient boosting model, considering high-order statistical interactions, was built for prediction.
Over 18.7% of the encounters had LOS >1 week. Burns and corrosion and suspected and confirmed child abuse are the strongest drivers of prolonged LOS. Several other trauma-specific and general pediatric clinical variables were also predictors of prolonged LOS. The stochastic gradient model obtained an area under the receiver operator characteristic curve of 0.912 (0.907, 0.917).
The high performance of the machine learning model coupled with statistical inference from the mixed effects model provide an opportunity for targeted interventions to improve quality of care of trauma patients likely to require long length of stay.
Targeted interventions on high-risk patients would improve the quality of care of pediatric trauma patients and reduce the length of stay. This comprehensive study includes data from multiple hospitals analyzed with advanced statistical and machine learning models. The statistical and machine learning models provide opportunities for targeted interventions and reduction in prolonged length of stay reducing the burden of hospitalization on families.
在本研究中，对小儿创伤患者住院时间延长的特定创伤风险因素进行了检查。使用统计和机器学习模型来提供改善有住院时间延长风险患者的护理质量并降低成本的方法。
检索了27家医院中81929例主要诊断为创伤且住院时间>24小时的儿科患者的住院数据。采用嵌套混合效应模型进行简化统计推断，同时构建考虑高阶统计相互作用的随机梯度提升模型进行预测。
超过18.7%的病例住院时间>1周。烧伤、腐蚀以及疑似和确诊的儿童虐待是住院时间延长的最强驱动因素。其他一些特定创伤和一般儿科临床变量也是住院时间延长的预测因素。随机梯度模型在受试者工作特征曲线下的面积为0.912（0.907，0.917）。
机器学习模型的高性能与混合效应模型的统计推断相结合，为有针对性的干预提供了机会，以改善可能需要长时间住院的创伤患者的护理质量。
对高危患者进行有针对性的干预将提高小儿创伤患者的护理质量并缩短住院时间。这项全面的研究包括来自多家医院的数据，并用先进的统计和机器学习模型进行了分析。统计和机器学习模型为有针对性的干预和减少住院时间延长提供了机会，减轻了家庭的住院负担。
