CHOC Children's, Orange, CA, 92868, USA.
Schmid College of Science & Technology, Chapman University, Orange, CA, USA.
J Orthop Surg Res. 2020 Aug 14;15(1):331. doi: 10.1186/s13018-020-01863-8.
Emergency department (ED) return visits within 72 h may be a sign of poor quality of care and entail unnecessary use of healthcare resources. In this study, we compare the performance of two leading statistical and machine learning classification algorithms, and we use the best performing approach to identify novel risk factors of ED return visits.
We analyzed 3.2 million ED encounters with at least one diagnosis under "injury, poisoning and certain other consequences of external causes" and "external causes of morbidity." These encounters included patients 18 years or older from across 128 emergency room facilities in the USA. For each encounter, we calculated the 72-h ED return status and retrieved 57 features from demographics, diagnoses, procedures, and medications administered during the process of administration of medical care. We implemented a mixed-effects model to assess the effects of the covariates while accounting for the hierarchical structure of the data. Additionally, we investigated the predictive accuracy of the extreme gradient boosting tree ensemble approach and compared the performance of the two methods.
The mixed-effects model indicates that certain blunt force and non-blunt trauma inflates the risk of a return visit. Notably, patients with trauma to the head and patients with burns and corrosions have elevated risks. This is in addition to 11 other classes of both blunt force and non-blunt force traumas. In addition, prior healthcare resource utilization, patients who have had one or more prior return visits within the last 6 months, prior ED visits, and the number of hospitalizations within the 6 months are associated with increased risk of returning to the ED after discharge. On the one hand, the area under the receiver characteristic curve (AUROC) of the mixed-effects model was 0.710 (0.707, 0.712). On the other hand, the gradient boosting tree ensemble had a lower AUROC of 0.698 CI (0.696, 0.700) on the independent test model.
The proposed mixed-effects model achieved the highest known AUC and resulted in the identification of novel risk factors. The model outperformed one of the leading machine learning ensemble classifiers, the extreme gradient boosting tree in terms of model performance. The risk factors we identified can assist emergency departments to decrease the number of unplanned return visits within 72 h.
在 72 小时内急诊科（ED）复诊可能是护理质量差的标志，并导致不必要的医疗资源使用。在这项研究中，我们比较了两种领先的统计和机器学习分类算法的性能，并使用表现最佳的方法来识别 ED 复诊的新的危险因素。
我们分析了来自美国 128 个急诊室设施的至少有一个“损伤、中毒和某些其他外部原因的后果”和“发病率的外部原因”诊断的 320 万次 ED 就诊。对于每一次就诊，我们计算了 72 小时 ED 复诊的情况，并从人口统计学、诊断、程序和医疗过程中使用的药物中提取了 57 个特征。我们实施了混合效应模型，以评估协变量的影响，同时考虑到数据的层次结构。此外，我们还研究了极端梯度提升树集成方法的预测准确性，并比较了两种方法的性能。
混合效应模型表明，某些钝器和非钝器创伤会增加复诊的风险。值得注意的是，头部创伤和烧伤和腐蚀的患者风险增加。这除了其他 11 类钝器和非钝器创伤之外。此外，先前的医疗资源利用、在过去 6 个月内有一次或多次复诊的患者、先前的 ED 就诊以及在 6 个月内的住院次数与出院后返回 ED 的风险增加有关。一方面，混合效应模型的接收者特征曲线下面积（AUROC）为 0.710（0.707，0.712）。另一方面，梯度提升树集成在独立测试模型上的 AUROC 较低，为 0.698 CI（0.696，0.700）。
提出的混合效应模型达到了已知的最高 AUC，并确定了新的危险因素。该模型在模型性能方面优于两种领先的机器学习集成分类器之一，即极端梯度提升树。我们确定的危险因素可以帮助急诊部门减少 72 小时内计划外复诊的次数。
