Li Xu, Jiang Zhongsheng, Mo Shenglin, Huang Xiaohong, Chen Tao, Zhang Peng, Li Linghua, Huang Bin, Lu Yanqiu, Wu Ying, Hu Jiaguang
Liuzhou Key Laboratory of Infection Disease and Immunology, Liuzhou People's Hospital, Liuzhou, Guangxi, China.
Division of Infectious Diseases, Liuzhou People's Hospital, Liuzhou, Guangxi, China.
Epidemiol Infect. 2024 Dec 5;152:e153. doi: 10.1017/S0950268824001456.
Our study aimed to develop and validate a nomogram to assess talaromycosis risk in hospitalized HIV-positive patients. Prediction models were built using data from a multicentre retrospective cohort study in China. On the basis of the inclusion and exclusion criteria, we collected data from 1564 hospitalized HIV-positive patients in four hospitals from 2010 to 2019. Inpatients were randomly assigned to the training or validation group at a 7:3 ratio. To identify the potential risk factors for talaromycosis in HIV-infected patients, univariate and multivariate logistic regression analyses were conducted. Through multivariate logistic regression, we determined ten variables that were independent risk factors for talaromycosis in HIV-infected individuals. A nomogram was developed following the findings of the multivariate logistic regression analysis. For user convenience, a web-based nomogram calculator was also created. The nomogram demonstrated excellent discrimination in both the training and validation groups [area under the ROC curve (AUC) = 0.883 vs. 0.889] and good calibration. The results of the clinical impact curve (CIC) analysis and decision curve analysis (DCA) confirmed the clinical utility of the model. Clinicians will benefit from this simple, practical, and quantitative strategy to predict talaromycosis risk in HIV-infected patients and can implement appropriate interventions accordingly.
我们的研究旨在开发并验证一种列线图，以评估住院HIV阳性患者发生足分支菌病的风险。预测模型是利用中国一项多中心回顾性队列研究的数据构建的。根据纳入和排除标准，我们收集了2010年至2019年期间四家医院1564例住院HIV阳性患者的数据。住院患者以7:3的比例随机分配到训练组或验证组。为了确定HIV感染患者发生足分支菌病的潜在风险因素，进行了单因素和多因素逻辑回归分析。通过多因素逻辑回归，我们确定了10个变量，这些变量是HIV感染个体发生足分支菌病的独立风险因素。根据多因素逻辑回归分析的结果开发了一种列线图。为方便用户，还创建了一个基于网络的列线图计算器。该列线图在训练组和验证组中均表现出出色的区分度[ROC曲线下面积（AUC）=0.883对0.889]和良好的校准度。临床影响曲线（CIC）分析和决策曲线分析（DCA）的结果证实了该模型的临床实用性。临床医生将从这种简单、实用且定量的策略中受益，该策略可预测HIV感染患者发生足分支菌病的风险，并可据此实施适当的干预措施。
