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Systematic Review and Meta-analysis of the Predictive Performance of Stroke and Bleeding Prediction Models in Atrial

Liselotte F S Langenhuijsen^1,2, Daniëlle C L Derksen¹, Jet Milders¹

¹Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

Kidney Medicine

|February 3, 2026

Insights

Patients with atrial fibrillation (AF) and chronic kidney disease (CKD) face high risks of stroke and bleeding. Prediction models like CHA2DS2-VASc and HAS-BLED show modest but usable discrimination in this population, despite study limitations.

Area of Science:

Cardiology
Nephrology
Clinical Epidemiology

Background:

Patients with atrial fibrillation (AF) and chronic kidney disease (CKD) have elevated risks for ischemic stroke (IS) and bleeding.
The clinical utility of existing prediction models in this high-risk cohort remains under investigation.

Purpose of the Study:

To systematically review and meta-analyze external validations of IS and bleeding prediction models (CHA2DS2-VASc, CHADS2, HAS-BLED, HEMORR2HAGES) in patients with AF and CKD or undergoing dialysis.
To provide pooled estimates of model performance and assess risk of bias.

Main Methods:

Systematic review and meta-analysis of published studies.
Inclusion of studies externally validating IS and bleeding prediction models in AF patients with CKD or on dialysis.
Pooled discrimination using random-effects meta-analysis and assessment of calibration and risk of bias.

Main Results:

Analysis of 627,199 patients across 35 studies for CHA2DS2-VASc, 19 for CHADS2 and HAS-BLED, and 1 for HEMORR2HAGES.
CHADS2 demonstrated nominally better IS prediction (c-statistic 0.70) than CHA2DS2-VASc (0.64) in AF patients with CKD.
In AF patients undergoing dialysis, CHA2DS2-VASc and CHADS2 showed similar IS prediction (0.70), while HAS-BLED and HEMORR2HAGES showed similar bleeding prediction (0.55-0.56).
Calibration was adequate in high-risk groups, but all studies had high risk of bias and heterogeneity.

Conclusions:

Prediction models exhibit modest discrimination for IS and bleeding in AF patients with CKD or on dialysis, comparable to those without CKD.
Despite limitations including high risk of bias and heterogeneity, these models can be applied in clinical practice for managing patients with AF, CKD, and dialysis status.

Read full analysis

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 CHA₂DS₂-VASc, CHADS₂, HAS-BLED, and HEMORR₂HAGES 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 CHA₂DS₂-VASc score was validated in 35 studies, the CHADS₂ and HAS-BLED scores in 19 each, and the HEMORR₂HAGES 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 CHADS₂ model score showed nominally better discrimination predicting IS (pooled c-statistic score of 0.70) than the CHA₂DS₂-VASc model score (0.64). In patients with AF undergoing dialysis, the CHA₂DS₂-VASc and CHADS₂ model scores showed similar discrimination predicting IS (both 0.70), and the HAS-BLED and HEMORR₂HAGES 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.

Keywords:

Prediction atrial fibrillation bias bleeding dialysis kidney disease renal failure stroke

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