Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis - Xiaoyan Quan; Huarong Xiong; Xiaoyu Liu; Pan Song; Dan Wang; Qin Chen; Xiaoli Hu; Meihong Shi (Scientific Reports)
Abstract Peripheral arterial disease (PAD) affects approximately 236.62 million individuals globally, exposing them to significantly increased risks of major limb events such as death and amputation. Concurrently, the number of diagnostic prediction models for PAD patients is steadily rising; however, these studies exhibit varying results, and their quality and applicability in clinical practice and future research remain unclear. To systematically assess the methodological quality of studies on PAD diagnostic prediction models. PubMed, Embase, Web of Science and Cochrane Database of Systematic Reviews were searched to identify studies which aiming to develop or validate a diagnostic prediction model of PAD. The retrieval time limit is from the establishment of the database to June 1, 2025. Two researchers independently screened and extracted data from eligible studies and evaluated the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST). A total of 24 studies on PAD diagnostic prediction models were included, most of which exhibited high risk of bias, predominantly in the domains of study population and statistical analysis. The meta-analyzed Area Under the Receiver Operating Characteristic Curve (AUC) was 0.79 [0.74, 0.84], indicating favorable model performance. The reported number of predictor variables ranged from 2 to 20, with common predictors including age, gender, hypertension, diabetes, smoking, and BMI. This study demonstrates that PAD diagnostic prediction models exhibit good predictive performance, albeit accompanied by a high risk of bias and substantial heterogeneity across studies. Future research on modeling should emphasize comprehensive methodological enhancements in model design, construction, evaluation, and validation, with full disclosure of crucial model information. It should also utilize network computing for presenting model outcomes and conduct large-scale, multi-center external validation of existing models to promote their clinical application. Trial registration: This study protocol has been registered with PROSPERO (registration number: CRD42024557144).