Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets

Abstract

Background Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. “CKD Patch” is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures. Methods Utilizing data from 4,143,535 adults from 35 datasets, we developed several “CKD Patches” incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). The risk enhancement by CKD Patch was determined by the deviation between individual CKD measures and the values expected from their traditional CVD risk factors and the hazard ratios for eGFR and albuminuria. We then validated this approach among 4,932,824 adults from 37 independent datasets, comparing the original PCE and SCORE equations (recalibrated in each dataset) to those with addition of CKD Patch. Findings We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-statistic 0.027 [95% CI 0.018–0.036] and 0.010 [0.007–0.013] and categorical net reclassification improvement 0.080 [0.032–0.127] and 0.056 [0.044–0.067], respectively). The median (IQI) of the ratio of predicted risk for CVD mortality with CKD Patch vs. the original prediction with SCORE was 2.64 (1.89–3.40) in very high-risk CKD (e.g., eGFR 30–44 ml/min/1.73m 2 with albuminuria ≥30 mg/g), 1.86 (1.48–2.44) in high-risk CKD (e.g., eGFR 45–59 ml/min/1.73m 2 with albuminuria 30–299 mg/g), and 1.37 (1.14–1.69) in moderate risk CKD (e.g., eGFR 60–89 ml/min/1.73m 2 with albuminuria 30–299 mg/g), indicating considerable risk underestimation in CKD with SCORE. The corresponding estimates for ASCVD with PCE were 1.55 (1.37–1.81), 1.24 (1.10–1.54), and 1.21 (0.98–1.46). Interpretation The “CKD Patch” can be used to quantitatively enhance ASCVD and CVD mortality risk prediction equations recommended in major US and European guidelines according to CKD measures, when available. Funding US National Kidney Foundation and the NIDDK.

The Effect of Angiotensin Converting Enzyme (ACE) I/D Polymorphism on Atherosclerotic Cardiovascular Disease and Cardiovascular Mortality Risk in Non-Hemodialyzed Chronic Kidney Disease: The Mediating Role of Plasma ACE Level

“…The CKD patch score algorithm (https://ckdpcrisk.org/ ckdpatchscore/ accessed on 10 November 2021) was also used to calculate the 10-year risk of cardiovascular death, including eGFR, ACR, age, gender, systolic blood pressure, total cholesterol, and smoking status. The Chronic Kidney Disease Prognosis Consortium 2 was used to develop the CKD patch equation (https://ckdpcrisk.org/ckdpatchpce/ accessed on 10 November 2021) [24]. The flow chart of the sample collection is presented in Figure 1.…”

Framingham and American College of Cardiology/American Heart Association Pooled Cohort Equations, High‐Sensitivity Troponin T, and N‐Terminal Pro–Brain‐Type Natriuretic Peptide for Predicting Atherosclerotic Cardiovascular Events Across the Spectrum of Kidney Dysfunction

“…Albuminuria and eGFR have not previously improved the performance of traditional risk factors for ASCVD 20 ; however, a recent study incorporating multiple cohorts with a mean eGFR of 86 mL/min per 1.73 m 2 demonstrated modestly improved discrimination when applying terms for eGFR and albuminuria. 66 While discrimination may be improved in mild CKD, it may be advisable to consider alternative ways to predict ASCVD, or to create de novo ASCVD risk scores specifically for use in patients with moderate‐to‐severe CKD. Patients with CKD may have CKD‐specific, pro‐atherogenic risk factors, including disordered mineral bone metabolism, inflammation, and proteinuria, which are not accounted for by existing risk scores.…”

Chronic kidney disease measures for cardiovascular risk prediction

“…1), according to the KDIGO CKD guideline, as "very high" and "high" CVD risk. However, this approach does not take into account other major risk factors and thus may misclassify the CVD risk [96]. This new guideline recommends using a new risk prediction equation, SCORE2, for estimating 10-year total CVD risk, but unfortunately this equation does not incorporate the two key CKD measures, eGFR and albuminuria [95].…”

“…4. We recently validated that the addition of CKD measures with this "CKD Add-on" approach improved the risk prediction of CVD outcomes on top of two major risk prediction models used in clinical guidelines, the PCE and SCORE [100].…”