Kidney Med. 2025 Dec 11;8(2):101200. doi: 10.1016/j.xkme.2025.101200. eCollection 2026 Feb.
ABSTRACT
RATIONALE & OBJECTIVE: Patients with atrial fibrillation (AF) and chronic kidney disease (CKD) are at high risk for ischemic stroke (IS) and bleeding. The applicability of prediction models in this population remains debated. This study aimed to (1) identify external validations of CHADS-VASc, CHADS, HAS-BLED, and HEMORRHAGES model scores in patients with AF undergoing dialysis or with CKD, (2) provide pooled estimates, and (3) assess their risk of bias (ROB).
STUDY DESIGN: Systematic review and meta-analysis.
SETTING & PARTICIPANTS: We searched Web of Science, PubMed, MEDLINE, Embase, Emcare, PMC, Cochrane Library, and Academic Search Premier for studies externally validating IS and bleeding prediction models in patients with AF undergoing dialysis or with CKD.
EXPOSURES: AF and CKD or dialysis.
OUTCOMES: IS and bleeding.
ANALYTICAL APPROACH: Eligible studies were reviewed, discrimination was pooled using random-effects meta-analysis, calibration was calculated and plotted, and the ROB score was assessed using the prediction model ROB assessment tool.
RESULTS: The CHADS-VASc score was validated in 35 studies, the CHADS and HAS-BLED scores in 19 each, and the HEMORRHAGES score in 1. Among 627,199 patients, 28,493 (4.5%) experienced IS and 25,695 (4.1%) bleeding. Only 12 studies presented c-statistic scores. In patients with AF and CKD, the CHADS model score showed nominally better discrimination predicting IS (pooled c-statistic score of 0.70) than the CHADS-VASc model score (0.64). In patients with AF undergoing dialysis, the CHADS-VASc and CHADS model scores showed similar discrimination predicting IS (both 0.70), and the HAS-BLED and HEMORRHAGES model scores showed similar c-statistic scores predicting bleeding (0.55 and 0.56, respectively). Calibration was good in the most relevant high-risk group.
LIMITATIONS: All studies were at high ROB scores, contained within- and between-study heterogeneity, and often merged scoring categories or populations, limiting comparability.
CONCLUSIONS: Although modest, the discrimination of prediction models in patients with AF undergoing dialysis or with CKD is similar to patients with AF without CKD. Despite the described limitations, these models can be used in clinical practice for patients with CKD and patients undergoing dialysis.
PMID:41630994 | PMC:PMC12861232 | DOI:10.1016/j.xkme.2025.101200