Risk-Based Stratification Approach for Cardiovascular-Kidney-Metabolic Syndrome Stage 2 Using a Simple Algorithm

Background: The cardiovascular-kidney-metabolic (CKM) syndrome is associated with a graded risk of cardiovascular mortality. Individuals with metabolic risk factors and/or moderate to high-risk chronic kidney disease (CKD) are classified as stage 2 and account for nearly half of U.S. adults. The primary aim of this study was to develop and verify a model with few, easily assessable parameters to further stratify CKM syndrome stage 2 into two risk groups, with the goal of optimizing individual patient care. Methods: Multivariable Cox proportional hazards model and modified lasso regression were applied to adult participant data (n=31,083) from 1999-2018 National Health and Nutrition Examination Survey (NHANES) with linkage to the US National Death Index, using cardiovascular death as the outcome for model development. Discrimination was evaluated using the area under the receiver operating curve (AUC) and C-statistic. TriNetX database with de-identified aggregated patient-level data from medical records was analyzed to prove the clinical performance of the model, taking major adverse cardiovascular events (MACE) as outcome, since cardiovascular death data were not available. Results: In the NHANES cohort, age (≥56 years) and CKD status served as criteria to stratify stage 2 into two subgroups (stage 2a and 2b) for women (AUC 0.80; C-statistic 0.78), while age (≥ 53 years), tobacco use, status of CKD, hypertension and diabetes for men (AUC 0.74; C-statistic 0.79). After applying these criteria to TriNetX cohort, women in stage 2a had a 15-year-risk of 11.3% (95% CI 10.0 to 12.6) compared to 32.9% (95% 31.4 to 34.3) in stage 2b. In man the risk increased from 15% (95% CI 14.2 to 15.9) to 42.1% (95% CI 41.3 to 42.8). Conclusion: Stratification of CKM syndrome stage 2 using our model allows for more tailored and precise patient management aimed at preventing progression to advanced stages. Individuals at higher risk within stage 2 may particularly benefit from early intervention strategies informed by this approach.