目的:蜂窝织炎是皮肤相关住院的最常见原因,脓毒症患者的死亡率仍然很高。已经开发了一些分层模型,但他们在外部验证中的表现并不令人满意。这项研究旨在开发和比较不同的模型,以预测住院期间蜂窝织炎患者发生败血症。
方法:这是一项回顾性队列研究。
方法:本研究包括国际上两个独立的大型队列的开发和外部验证阶段。
方法:使用重症监护医学信息集市(MIMIC)-IV数据库中的6695例蜂窝织炎患者,使用不同的机器学习算法开发模型。从我们大学的YiduCloud数据库中选择最佳模型,然后在2506例蜂窝织炎患者中进行外部验证。在外部验证组中,通过曲线下面积(AUC)进一步比较所选模型的性能和鲁棒性,诊断准确性,灵敏度,特异性和诊断OR。
方法:本研究的主要结果是基于住院期间脓毒症-3.0标准的发展。
结果:两组患者特征有显著差异。在内部验证中,XGBoost是最好的模型,AUC为0.780,AdaBoost是最差的型号,AUC为0.585。在外部验证中,人工神经网络(ANN)模型的AUC最高,0.830,而逻辑回归(LR)模型的AUC最低,0.792.删除变量时,增强和ANN模型中的AUC值变化小于LR模型中的AUC值变化。
结论:Boosting和神经网络模型的性能略好于LR模型,并且在复杂的临床情况下更加稳健。结果可以为临床医生提供一种工具,以检测早期发展为败血症的蜂窝织炎住院患者。
OBJECTIVE: Cellulitis is the most common cause of skin-related hospitalisations, and the mortality of patients with sepsis remains high. Some stratification models have been developed, but their performance in external validation has been unsatisfactory. This study was designed to develop and compare different models for predicting patients with
cellulitis developing sepsis during hospitalisation.
METHODS: This is a retrospective cohort study.
METHODS: This study included both the development and the external-validation phases from two independent large cohorts internationally.
METHODS: A total of 6695 patients with
cellulitis in the Medical Information Mart for Intensive care (MIMIC)-IV database were used to develop models with different machine-learning algorithms. The best models were selected and then externally validated in 2506 patients with
cellulitis from the YiduCloud database of our university. The performances and robustness of selected models were further compared in the external-validation group by area under the curve (AUC), diagnostic accuracy, sensitivity, specificity and diagnostic OR.
METHODS: The primary outcome of interest in this study was the development based on the Sepsis-3.0 criteria during hospitalisation.
RESULTS: Patient characteristics were significantly different between the two groups. In internal validation, XGBoost was the best model, with an AUC of 0.780, and AdaBoost was the worst model, with an AUC of 0.585. In external validation, the AUC of the artificial neural network (ANN) model was the highest, 0.830, while the AUC of the logistic regression (LR) model was the lowest, 0.792. The AUC values changed less in the boosting and ANN models than in the LR model when variables were deleted.
CONCLUSIONS: Boosting and neural network models performed slightly better than the LR model and were more robust in complex clinical situations. The results could provide a tool for clinicians to detect hospitalised patients with
cellulitis developing sepsis early.