Risk models for sepsis-associated acute kidney injury demonstrate a pooled C-statistic of 0.817Risk models show promise in predicting sepsis and kidney injury

ObjectivesThis study aims to systematically evaluate the predictive performance of risk models for sepsis-associated acute kidney injury (SA-AKI). Furthermore, we explore the specific factors that influence how effectively these models perform in clinical settings.MethodsWe systematically searched PubMed, The Cochrane Library, Web of Science, and Embase to identify cohort studies published up to 30 March 2026. These studies focused on the development and validation of SA-AKI prediction models. To ensure the quality of the evidence, the risk of bias was assessed using the Prediction model study Risk Of Bias ASsessment Tool (PROBAST). Data synthesis involved a random-effects model, which we used to pool C-statistics and their 95% confidence intervals (CIs). Finally, sources of heterogeneity were explored through subgroup analysis.ResultsA total of 15 studies involving 46,490 patients were included in this meta-analysis. The pooled C-statistic was 0.817 (95% CI: 0.781–0.847), indicating a moderate-to-good discriminative ability for SA-AKI. However, substantial heterogeneity was observed (I2 = 92.8%, τ2 = 0.143). Subgroup analyses further elucidated the drivers of this variation. We found that while models developed in Asian regions showed a higher pooled C-statistic than those from North America (0.845 vs. 0.777), this difference was not statistically significant (P for interaction = 0.076). Similarly, studies with a low risk of bias yielded superior predictive performance compared to those at high risk (0.847 vs. 0.762; P for interaction = 0.010). Notably, model performance was not significantly influenced by the type of validation (internal vs. external) or the language of publication based on the interaction test. However, the external validation subgroup displayed an exceptionally wide 95% CI, underscoring a high degree of uncertainty in this pooled estimate due to the limited number of contributing studies. Sensitivity analysis using the leave-one-out method demonstrated the robustness of the pooled estimate, as the omitted results remained stable within a narrow range (0.805–0.826). Finally, visual inspection of the funnel plot and Egger’s test suggested no potential publication bias (P = 0.1891).ConclusionCurrent prediction models for SA-AKI demonstrate moderate-to-good predictive performance; however, high heterogeneity was observed across the included studies. Future research should prioritize external validation and emphasize the reduction of bias risk. Furthermore, we recommend that investigators explore robust, population-specific models to enhance clinical utility and generalizability.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD420261347810.