Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; College of Medicine, University of Cincinnati, Cincinnati, OH, USA.
College of Medicine, University of Cincinnati, Cincinnati, OH, USA; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Int J Med Inform. 2019 Sep;129:234-241. doi: 10.1016/j.ijmedinf.2019.06.007. Epub 2019 Jun 8.
Last-minute surgery cancellation represents a major wastage of resources and can cause significant inconvenience to patients. Our objectives in this study were: 1) To develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children's risk of day-of-surgery cancellation.
We extracted five-year datasets (2012-2017) from the Electronic Health Record at Cincinnati Children's Hospital Medical Center. By leveraging patient-specific information and contextual data, machine learning classifiers were developed to predict all patient-related cancellations and the most frequent four cancellation causes individually (patient illness, "no show," NPO violation and refusal to undergo surgery by either patient or family). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) using ten-fold cross-validation. The best performance for predicting all-cause surgery cancellation was generated by gradient-boosted logistic regression models, with AUC 0.781 (95% CI: [0.764,0.797]) and 0.740 (95% CI: [0.726,0.771]) for the two campuses. Of the four most frequent individual causes of cancellation, "no show" and NPO violation were predicted better than patient illness or patient/family refusal. Models showed good cross-campus generalizability (AUC: 0.725/0.735, when training on one site and testing on the other). To synthesize a human-oriented conceptualization of pediatric surgery cancellation, an iterative step-forward approach was applied to identify key predictors which may inform the design of future preventive interventions.
Our study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The approach offers the promise of targeted interventions to significantly decrease both healthcare costs and also families' negative experiences.
最后时刻的手术取消代表了资源的重大浪费，并且会给患者带来极大的不便。我们在这项研究中的目标是：1）利用机器学习技术，从单一机构的两个不同儿科手术部位的患者特定和环境数据中，开发预测最后时刻手术取消的模型；2）确定影响儿童手术当日取消风险的具体关键预测因素。
我们从辛辛那提儿童医院医疗中心的电子健康记录中提取了五年数据集（2012-2017 年）。通过利用患者特定信息和环境数据，开发了机器学习分类器，以单独预测所有与患者相关的取消和最常见的四个取消原因（患者疾病、“失约”、禁食时间违反和患者或家属拒绝手术）。通过十折交叉验证，使用接收者操作特征曲线下的面积（AUC）评估模型性能。预测所有原因手术取消的最佳性能是由梯度提升逻辑回归模型生成的，AUC 为 0.781（95%CI：[0.764,0.797]）和 0.740（95%CI：[0.726,0.771]），适用于两个校区。在四个最常见的取消个人原因中，“失约”和禁食时间违反的预测效果优于患者疾病或患者/家属拒绝。模型显示出良好的跨校区通用性（AUC：0.725/0.735，在一个站点上进行训练，在另一个站点上进行测试）。为了综合理解儿科手术取消的以人为中心概念化，应用迭代逐步向前方法来确定可能为未来预防干预措施提供信息的关键预测因素。
我们的研究表明，机器学习模型有能力预测有最后时刻手术取消风险的儿科患者，并提供有关取消原因的有用见解。该方法有望实现有针对性的干预措施，从而显著降低医疗成本和患者家庭的负面体验。
