Li Jialu, Ding Yi, Hao Yiwei, Gao Chengyu, Xiao Jinjing, Liu Ying, Zhao Yining, Li Qinlan, Xing Lulu, Liang Hongyuan, Ni Liang, Wang Fang, Wang Sa, Yang Di, Gao Guiju, Xiao Jiang, Zhao Hongxin
Clinical Center for HIV/AIDS, Beijing Ditan Hospital, Capital Medical University, Jingshun East Street, Beijing, 100015, China.
Division of Medical Record and Statistics, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Virol J. 2025 Aug 5;22(1):267. doi: 10.1186/s12985-025-02900-w.
Antiretroviral therapy (ART) has transformed HIV from a rapidly progressive and fatal disease to a chronic disease with limited impact on life expectancy. However, people living with HIV(PLWHs) faced high critical illness risk due to the increased prevalence of various comorbidities and are admitted to the Intensive Care Unit(ICU). This study aimed to use machine learning to predict ICU admission risk in PLWHs. 1530 HIV patients (199 admitted to ICU) from Beijing Ditan Hospital, Capital Medical University were enrolled in the study. Classification models were built based on logistic regression(LOG), random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), artificial neural network(ANN), and extreme gradient boosting(XGB). The risk of ICU admission was predicted using the Brier score, area under the receiver operating characteristic curve (ROC-AUC), and area under the precision-recall curve(PR-ROC) for internal validation and ranked by Shapley plot. The ANN model performed best in internal validation (Brier score = 0.034, ROC-AUC = 0.961, PR-AUC = 0.895) to predict the risk of ICU admission for PLWHs. 11 important features were identified to predict predict ICU admission risk by the Shapley plot: respiratory failure, multiple opportunistic infections in the respiratory system, AIDS defining cancers, baseline viral load, PCP, baseline CD4 cell count, and unexplained infections. An intelligent healthcare prediction system could be developed based on the medical records of PLWHs, and the ANN model performed best in effectively predicting the risk of ICU admission, which helped physicians make timely clinical interventions, alleviate patients suffering, and reduce healthcare cost.
抗逆转录病毒疗法（ART）已将艾滋病从一种快速进展的致命疾病转变为一种对预期寿命影响有限的慢性疾病。然而，由于各种合并症的患病率增加，艾滋病病毒感染者（PLWHs）面临着较高的重症风险，并被收入重症监护病房（ICU）。本研究旨在使用机器学习预测PLWHs的ICU入院风险。来自首都医科大学附属北京地坛医院的1530例HIV患者（199例入住ICU）被纳入研究。基于逻辑回归（LOG）、随机森林（RF）、k近邻（KNN）、支持向量机（SVM）、人工神经网络（ANN）和极端梯度提升（XGB）构建分类模型。使用Brier评分、受试者操作特征曲线下面积（ROC-AUC）和精确召回率曲线下面积（PR-ROC）预测ICU入院风险以进行内部验证，并通过Shapley图进行排名。ANN模型在内部验证中表现最佳（Brier评分为0.034，ROC-AUC为0.961，PR-AUC为0.895），用于预测PLWHs的ICU入院风险。通过Shapley图确定了11个预测ICU入院风险的重要特征：呼吸衰竭、呼吸系统多种机会性感染、艾滋病定义的癌症、基线病毒载量、肺孢子菌肺炎（PCP）、基线CD4细胞计数和不明原因感染。可以基于PLWHs的病历开发一个智能医疗预测系统，并且ANN模型在有效预测ICU入院风险方面表现最佳，这有助于医生及时进行临床干预，减轻患者痛苦，并降低医疗成本。
