Xiao Hao, Chen Cheng, Lin Fangbo
Rehabilitation Medicine Department, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University (The First Hospital of Changsha), Changsha, People's Republic of China.
Tuberculosis Department, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University (The First Hospital of Changsha), Changsha, People's Republic of China.
Sci Rep. 2025 Apr 11;15(1):12507. doi: 10.1038/s41598-025-97739-0.
To develop and validate a nomogram model for differentiating cryptococcal meningitis (CM) from tuberculous meningitis (TBM) in HIV-infected patients, given the diagnostic challenges due to shared clinical manifestations and limitations of existing methods. A retrospective analysis extracted 207 HIV cases (112 CM, 95 TBM). Candidate predictor variables covering general information, blood biochemical, and cerebrospinal fluid(CSF) examination indicators were collected. Least absolute shrinkage and selection operator (LASSO) regression and ten-fold cross-validation identified key predictors, which were used to construct and validate the nomogram model. Model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) values were used to interpret the characteristics of the model's predictor variables. Five predictors (extracranial tuberculosis, extracranial fungi, erythrocyte sedimentation rate, albumin, and CSF pressure) were included in the final nomogram. The model achieved AUC of 0.830 (95% CI: 0.758-0.902) in the training set and 0.811 (95% CI: 0.719-0.904) in the testing set, with good calibration and clinical validity shown by calibration curves and DCA. The developed nomogram model effectively distinguishes CM from TBM in HIV-infected patients. It aids clinicians in diagnosis decisions.
鉴于HIV感染患者中隐球菌性脑膜炎（CM）和结核性脑膜炎（TBM）临床表现相似以及现有诊断方法存在局限性所带来的诊断挑战，旨在开发并验证一种用于区分HIV感染患者中隐球菌性脑膜炎和结核性脑膜炎的列线图模型。一项回顾性分析纳入了207例HIV病例（112例CM，95例TBM）。收集了涵盖一般信息、血液生化和脑脊液（CSF）检查指标的候选预测变量。通过最小绝对收缩和选择算子（LASSO）回归及十折交叉验证确定关键预测因素，并用于构建和验证列线图模型。通过受试者操作特征（ROC）曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估模型性能。使用SHapley加性解释（SHAP）值来解释模型预测变量的特征。最终列线图纳入了五个预测因素（颅外结核、颅外真菌、红细胞沉降率、白蛋白和脑脊液压力）。该模型在训练集中的AUC为0.830（95%CI：0.758 - 0.902），在测试集中为0.811（95%CI：0.719 - 0.904），校准曲线和DCA显示出良好的校准和临床有效性。所开发的列线图模型能有效区分HIV感染患者中的CM和TBM，有助于临床医生做出诊断决策。
