Sharif Asmaa Fady, Hafez Ahmad, Fayed Manar Maher, Sobh Zahraa Khalifa
Forensic Medicine and Clinical Toxicology Department, Faculty of Medicine, Tanta University, Tanta, Egypt.
Clinical Medical Sciences Department, College of Medicine, Dar Al-Uloom University, Riyadh, Saudi Arabia.
Cardiovasc Toxicol. 2025 Aug 30. doi: 10.1007/s12012-025-10055-x.
Atypical antipsychotics have experienced a significant increase in use across various disorders, coinciding with a rise in acute intoxication. This retrospective study predicts prolonged QTc interval and the necessity for mechanical ventilation (MV) in patients with acute atypical antipsychotic poisoning using machine learning techniques. This retrospective study included 355 patients with a mean age of 26.1 ± 9.6 years. The overall prevalence of the investigated outcomes was 5.5% for prolonged QTc interval and 7.1% for MV. Eight classifiers were developed, including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and five tree-based models: Random Forest, XGBoost, LightGBM, CatBoost, and Gradient Boosting Models. Model validation was conducted through external validation using the testing dataset and an internal five-fold cross-validation after optimizing the hyperparameters. As a predictor of prolonged QTc interval, all tree-based models achieved perfect specificity, recall, precision, accuracy, and area under the curve (AUC) of 100% using the training dataset. Similar performance was reported in models predicting the necessity for MV. Upon validation, the tree-based models for predicting prolonged QTc intervals maintained good AUCs, ranging between 0.930 and 0.958 in the training dataset and between 0.927 and 0.949 in the testing dataset. In terms of accuracy, the tree-based models exhibited good values in both external and five-fold cross-validation, with all values above 0.901. The observed declines in recall and precision during the validation of the proposed models underscore the need for future studies to utilize larger validation cohorts, thereby enabling the generalization of the proposed models in relevant clinical settings.
非典型抗精神病药物在各种疾病中的使用量显著增加，与此同时急性中毒事件也有所上升。这项回顾性研究使用机器学习技术预测急性非典型抗精神病药物中毒患者的QTc间期延长情况以及机械通气（MV）的必要性。该回顾性研究纳入了355例患者，平均年龄为26.1±9.6岁。所研究结局的总体患病率为：QTc间期延长5.5%，机械通气7.1%。开发了8种分类器，包括逻辑回归、支持向量机、K近邻以及5种基于树的模型：随机森林、XGBoost、LightGBM、CatBoost和梯度提升模型。在对超参数进行优化后，通过使用测试数据集进行外部验证和内部五折交叉验证来进行模型验证。作为QTc间期延长的预测指标，所有基于树的模型在使用训练数据集时均实现了完美的特异性、召回率、精确率、准确率和曲线下面积（AUC），均为100%。在预测机械通气必要性的模型中也报告了类似的性能。经过验证，用于预测QTc间期延长的基于树的模型保持了良好的AUC，在训练数据集中介于0.930和0.958之间，在测试数据集中介于0.927和0.949之间。在准确率方面，基于树的模型在外部验证和五折交叉验证中均表现出良好的值，所有值均高于0.901。在所提出模型的验证过程中观察到的召回率和精确率下降凸显了未来研究需要使用更大的验证队列，从而使所提出的模型能够在相关临床环境中得到推广。
