Clinical Prediction Models for Peripherally Inserted Central Catheter-Related Venous Thrombosis: A Systematic Review and Meta-analysis

Abstract

Background PICC-related venous thrombosis (PICC-RVT) is one of the most serious complications of PICC. Clinical prediction models (CPMs) are statistical models that incorporate a number of variables and forecast the likelihood of outcome events using a few risk factors.Objective To analyze the reported PICC-RVT risk prediction models' bias risks and to conduct out a meta-analysis.Methods PICC-RVT research reports were available in PubMed, Web of Science, CINAHL, Scopus, ProQuest, and Cochrane Library. Research data from articles fulfilling eligibility conditions were collected and analyzed using the Systematic Reviews of Prediction Modeling Studies checklist. The included studies were assessed for bias using PROBAST. A meta-analysis of the C statistics of the included studies was performed using R software.Results A total of 714 articles were acquired from the electronic database; 5 of these were included in our study. All models were evaluated to have "low risk" in the clinical application domain using PROBAST evaluation. Regarding bias risk, three models were labeled as "high risk," one as "uncertain," and one as "low risk." The C-statistics for each model, which were in the range of 0.636 to 0.802.Conclusions The model's discrimination and prediction are acceptable, and most of the models in our review lack external validation. Our findings suggest reporting predictive model creation or validation utilizing TRIPOD criteria.