A nomogram-based clinical prediction model for adverse clinical outcomes in non-HIV Pneumocystis jirovecii pneumonia patients

作者：Dong Wang, Lujia Guan, Qian Yin, Xiaoxia Hou, Xi Zhan, Zhaohui Tong · 发表于：BMC Pulmonary Medicine · 年份：2025 · DOI：10.1186/s12890-025-03700-2 · 被引用次数：2 · 研究领域：Pneumocystis jirovecii pneumonia detection and treatment、Pneumonia and Respiratory Infections、HIV-related health complications and treatments

Abstract Background Non-human immunodeficiency virus (HIV) immunocompromised patients with Pneumocystis jirovecii pneumonia (PJP) face rapid progression and high mortality, necessitating a predictive model to identify patients at risk of adverse clinical outcomes for timely interventions and improved stratification. Methods Patients admitted between January 2011 and June 2024 at Beijing Chao-Yang Hospital were retrospectively analyzed. Collected data included patients’ demographics, smoking status, comorbidities, immunosuppressive diseases, blood laboratory tests, in-hospital treatment, and adverse clinical outcomes. Predictor selection was performed using the least absolute shrinkage and selection operator (LASSO) and logistic regression, with selected features incorporated into a nomogram. Internal validation was conducted using a 500-bootstrap resampling method to ensure model robustness. Model performance was assessed via the area under the receiver operating curve (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curve (CIC). Results Among the 431 patients, 243 (56.4%) experienced adverse clinical outcomes. LASSO regression screened 21 variables, selecting 9 predictors with non-zero coefficients through 10-fold cross-validation at lambda.1se = 0.0453 (log(lambda.1se) = -3.092). Multivariate logistic regression identified 7 independent risk factors for adverse clinical outcomes: smoking status, cytomegalovirus infection, diabetes, neutrophil-lym...