Chen Yiwei, Pan Kejun, Lu Xiaobo, Maimaiti Erxiding, Wubuli Maimaitiaili
Department of Epidemiology and Health Statistics, School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China.
Department of Infectious Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Front Public Health. 2025 Mar 12;13:1544351. doi: 10.3389/fpubh.2025.1544351. eCollection 2025.
Early assessment and intervention of Acquired Immune Deficiency Syndrome (AIDS) patients at high risk of mortality is critical. This study aims to develop an optimally performing mortality risk prediction model for AIDS patients with comorbid AIDS-related diseases or symptoms to facilitate early intervention.
The study included 478 first-time hospital-admitted AIDS patients with related diseases or symptoms. Eight predictors were screened using lasso regression, followed by building eight models and using SHAP values (Shapley's additive explanatory values) to identify key features in the best models. The accuracy and discriminatory power of model predictions were assessed using variable importance plots, receiver operating characteristic curves, calibration curves, and confusion matrices. Clinical benefits were evaluated through decision-curve analyses, and validation was performed with an external set of 48 patients.
Lasso regression identified eight predictors, including hemoglobin, infection pathway, Sulfamethoxazole-Trimethoprim, expectoration, headache, persistent diarrhea, Pneumocystis jirovecii pneumonia, and bacterial pneumonia. The optimal model, XGBoost, yielded an Area Under Curve (AUC) of 0.832, a sensitivity of 0.703, and a specificity of 0.799 in the training set. In the test set, the AUC was 0.729, the sensitivity was 0.717, and the specificity was 0.636. In the external validation set, the AUC was 0.873, the sensitivity was 0.852, and the specificity was 0.762. Furthermore, the calibration curves showed a high degree of fit, and the DCA curves demonstrated the overall high clinical utility of the model.
In this study, an XGBoost-based mortality risk prediction model is proposed, which can effectively predict the mortality risk of patients with co-morbid AIDS-related diseases or symptomatic AIDS, providing a new reference for clinical decision-making.
对有高死亡风险的获得性免疫缺陷综合征（艾滋病）患者进行早期评估和干预至关重要。本研究旨在为合并艾滋病相关疾病或症状的艾滋病患者开发一种性能最优的死亡风险预测模型，以促进早期干预。
该研究纳入了478例首次因相关疾病或症状入院的艾滋病患者。使用套索回归筛选出8个预测因子，随后构建8个模型，并使用SHAP值（夏普利加性解释值）来识别最佳模型中的关键特征。使用变量重要性图、受试者工作特征曲线、校准曲线和混淆矩阵评估模型预测的准确性和区分能力。通过决策曲线分析评估临床效益，并对48例患者的外部数据集进行验证。
套索回归确定了8个预测因子，包括血红蛋白、感染途径、复方新诺明、咳痰、头痛、持续性腹泻、耶氏肺孢子菌肺炎和细菌性肺炎。最优模型XGBoost在训练集中的曲线下面积（AUC）为0.832，灵敏度为0.703，特异度为0.799。在测试集中，AUC为0.729，灵敏度为0.717，特异度为0.636。在外部验证集中，AUC为0.873，灵敏度为0.852，特异度为0.762。此外，校准曲线显示拟合度高，决策曲线分析表明该模型具有较高的总体临床实用性。
本研究提出了一种基于XGBoost的死亡风险预测模型，该模型能够有效预测合并艾滋病相关疾病或有症状艾滋病患者的死亡风险，为临床决策提供新的参考。
