Personalizing cardiovascular prevention in chronic kidney disease: an operational framework for risk-treatment mapping.

Abstract

Patients with chronic kidney disease (CKD) bear a disproportionate burden of cardiovascular (CV) morbidity and mortality, yet preventive strategies in clinical practice remain remarkably conservative and largely therapy-centered. The 2024 KDIGO guideline on CKD evaluation and management advocates the systematic use of validated prediction equations for kidney failure and CV outcomes, while contemporary CV prevention guidelines endorse multivariable risk tools that incorporate kidney measures. However, neither nephrology nor cardiology guidance has yet translated this risk-based paradigm into operational, bedside algorithms that link quantified risk to specific treatment combinations or incorporate frailty, life expectancy, and polypharmacy into decision-making. In this Review, we summarize contemporary risk prediction tools for kidney failure, CV events, and all-cause mortality in people with CKD, with a focus on models updated or externally validated after 2023. We examine how these tools reclassify risk compared with staging based on glomerular filtration rate (GFR) and albuminuria alone, and examine how they can underpin practical, absolute-risk-based strategies for layering disease-modifying therapies, including statins, renin-angiotensin-aldosterone system (RAAS) inhibitors, sodium-glucose cotransporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1RA), and finerenone. Finally, we address polypharmacy, deprescribing, and the challenges posed by extremes of age and CKD severity, arguing that personalized "risk-to-treatment" frameworks are now essential to navigate an increasingly complex therapeutic landscape.