He Rui, Zhang Kebiao, Li Hong, Gu Manping
Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Department of Emergency, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, 400016, China.
BMC Endocr Disord. 2025 Mar 27;25(1):86. doi: 10.1186/s12902-025-01873-9.
Hyperglycemic crisis is one of the most common and severe complications of diabetes mellitus, associated with a high motarlity rate. Emergency admissions due to hyperglycemic crisis remain prevalent and challenging. This study aimed to develop and validate predictive models for in-hospital mortality risk among patients with hyperglycemic crisis admitted to the emergency department using various machine learning (ML) methods.
A multi-center retrospective study was conducted across six large general adult hospitals in Chongqing, western China. Patients diagnosed with hyperglycemic crisis were identified using an electronic medical record (EMR) database. Demographics, comorbidities, clinical characteristics, laboratory results, complications, and therapeutic interventions were extracted from the medical records to construct the prognostic prediction model. Seven machine learning algorithms, including support vector machines (SVM), random forest (RF), recursive partitioning and regression trees (RPART), extreme gradient boosting with dart booster (XGBoost), multivariate adaptive regression splines (MARS), neural network (NNET), and adaptive boost (AdaBoost) were compared with logistic regression (LR) for predicting the risk of in-hospital mortality in patients with hyperglycemic crisis. Stratified random sampling was used to split the data into training (80%) and validation (20%) sets. Ten-fold cross validation was performed on the training set to optimize model hyperparameters. The sensitivity, specificity, positive and negative predictive values, area under the curve (AUC) and accuracy of all models were computed for comparative analysis.
A total of 1668 patients were eligible for the present study. The in-hospital mortality rate was 7.3% (121/1668). In the training set, feature importance scores were calculated for each of the eight models, and the top 10 significant features were identified. In the validation set, all models demonstrated good predictive capability, with areas under the curve value exceeding 0.9 with a F1 score between 0.632 and 0.81, except the MARS model. Six machine learning algorithm models outperformed the referred logistic regression algorithm except the MARS model. Among the selected models, RPART, RF, and SVM achieved the best performance in the selected models (AUC values were 0.970, 0.968 and 0.968, F1 score were 0.652, 0.762, 0.762 respectively). Feature importance analysis identified novel predictors including mechanical ventilation, age, Charlson Comorbidity Index, blood gas index, first 24-hour insulin dosage, and first 24-hour fluid intake.
Most machine learning algorithms exhibited excellent performance predicting in-hospital mortality among patients with hyperglycemic crisis except the MARS model, and the best one was RPART model. These algorithms identified overlapping but different, up to 10 predictors. Early identification of high-risk patients using these models could support clinical decision-making and potentially improve the prognosis of hyperglycemic crisis patients.
Not applicable.
高血糖危象是糖尿病最常见且最严重的并发症之一，死亡率很高。因高血糖危象而进行的急诊入院情况仍然很普遍且具有挑战性。本研究旨在使用各种机器学习（ML）方法开发并验证急诊科收治的高血糖危象患者院内死亡风险的预测模型。
在中国西部重庆的六家大型综合性成人医院开展了一项多中心回顾性研究。利用电子病历（EMR）数据库识别出诊断为高血糖危象的患者。从病历中提取人口统计学、合并症、临床特征、实验室检查结果、并发症和治疗干预措施，以构建预后预测模型。将七种机器学习算法，包括支持向量机（SVM）、随机森林（RF）、递归划分与回归树（RPART）、带dart增强器的极端梯度提升（XGBoost）、多元自适应回归样条（MARS）、神经网络（NNET）和自适应增强（AdaBoost）与逻辑回归（LR）进行比较，以预测高血糖危象患者的院内死亡风险。采用分层随机抽样将数据分为训练集（80%）和验证集（20%）。对训练集进行十折交叉验证以优化模型超参数。计算所有模型的敏感性、特异性、阳性和阴性预测值、曲线下面积（AUC）和准确性，进行比较分析。
共有1668例患者符合本研究条件。院内死亡率为7.3%（121/1668）。在训练集中，计算了八个模型各自的特征重要性得分，并确定了前10个显著特征。在验证集中，除MARS模型外，所有模型均显示出良好的预测能力，曲线下面积值超过0.9，F1分数在0.632至0.81之间。除MARS模型外，六种机器学习算法模型的表现优于参考的逻辑回归算法。在所选模型中，RPART、RF和SVM在所选模型中表现最佳（AUC值分别为0.970、0.968和0.968，F1分数分别为0.652、0.762、0.762）。特征重要性分析确定了新的预测因素，包括机械通气、年龄、Charlson合并症指数、血气指数、首个24小时胰岛素剂量和首个24小时液体摄入量。
除MARS模型外，大多数机器学习算法在预测高血糖危象患者院内死亡方面表现出优异性能，最佳的是RPART模型。这些算法识别出了重叠但不同的多达10个预测因素。使用这些模型早期识别高危患者可支持临床决策，并可能改善高血糖危象患者的预后。
不适用。
