Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis - Xiaoyu Wang; Xiaoyu Wang; Zhenzhen Feng; Zhenzhen Feng; Zhenzhen Feng; Lu Wang; Lu Wang; Wenrui Liu; Wenrui Liu; Jiansheng Li; Jiansheng Li; Jiansheng Li (Frontiers in Medicine, 2025)
BackgroundThe number of risk prediction models for mortality in patients with severe pneumonia (SP) is increasing, while the quality and clinical applicability of these models remain unclear. This study aimed to systematically review published research on risk prediction models for mortality in patients with SP.MethodsPubMed, Embase, Cochrane Library, and Web of Science were searched from inception to August 31, 2024. Data from selected studies were extracted, including study design, participants, diagnostic criteria, sample size, predictors, model development, and performance. The prediction model risk of bias assessment tool was used to assess the risk of bias and applicability. A meta-analysis of the area under the curve (AUC) values from validated models was conducted using Stata 17.0 software.ResultsA total of 22 prediction models from 18 studies were included in this review, including 15 logistic regression models, two cox proportional regression hazards models, two classification and regression trees, one light gradient boosting machine, and one multilayer perceptron. The reported AUC values ranged from 0.713 to 0.952. Seventeen studies were found to have a high risk of bias, primarily due to inappropriate data sources and poor reporting of the analysis domain. The pooled AUC value of five validated models was 0.85 (95% confidence interval: 0.81–0.88), indicating a fair level of discrimination.ConclusionAlthough the included studies reported that the risk prediction models for mortality in patients with SP exhibited a certain level of discriminative ability, most of these models were found to have a high risk of bias. Future studies should focus on developing new models with larger sample sizes, rigorous study designs, and multicenter external validation.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42024589877, identifier: CRD42024589877.