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Ten-Year Risk-Prediction Equations for Incident Heart Failure Hospitalizations in Chronic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort Study and the Multi-Ethnic Study of Atherosclerosis

Published:November 08, 2021DOI:https://doi.org/10.1016/j.cardfail.2021.10.007

      Highlights

      • Previously developed risk-prediction equations for heart failure that use routinely available clinical parameters in the general population perform poorly in a population with chronic kidney disease stages 2–4 who are at high risk for heart failure.
      • The addition of albuminuria, but not estimated glomerular filtration rate, to risk-prediction equations improves model performance in patients with chronic kidney disease as assessed by discrimination and calibration statistics.
      • Routinely available clinical data that include albuminuria in patients with chronic kidney disease can reliably identify individuals at risk of HF hospitalizations.

      ABSTRACT

      Background

      Heart failure (HF) is a leading contributor to cardiovascular morbidity and mortality in the population with chronic kidney disease (CKD). HF risk prediction tools that use readily available clinical parameters to risk-stratify individuals with CKD are needed.

      Methods

      We included Black and White participants aged 30–79 years with CKD stages 2–4 who were enrolled in the Chronic Renal Insufficiency Cohort (CRIC) study and were without self-reported cardiovascular disease. We assessed model performance of the Pooled Cohort Equations to Prevent Heart Failure (PCP-HF) to predict incident hospitalizations due to HF and refit the PCP-HF in the population with CKD by using CRIC data-derived coefficients and survival from CRIC study participants in the CKD population (PCP-HFCKD). We investigated the improvement in HF prediction with inclusion of estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) into the PCP-HFCKD equations by change in C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement index (IDI). We validated the PCP-HFCKD with and without eGFR and UACR in Multi-Ethnic Study of Atherosclerosis (MESA) participants with CKD.

      Results

      Among 2328 CRIC Study participants, 340 incident HF hospitalizations occurred over a mean follow-up of 9.5 years. The PCP-HF equations did not perform well in most participants with CKD and had inadequate discrimination and insufficient calibration (C-statistic 0.64-0.71, Greenwood-Nam-D'Agostino (GND) chi-square statistic P value < 0.05), with modest improvement and good calibration after being refit (PCP-HFCKD: C-statistic 0.61–0.78), GND chi-square statistic P value > 0.05). Addition of UACR, but not eGFR, to the refit PCP-HFCKD improved model performance in all race-sex groups (C-statistic [0.73–0.81], GND chi-square statistic P value > 0.05, delta C-statistic ranging from 0.03–0.11 and NRI and IDI P values < 0.01). External validation of the PCP-HFCKD in MESA demonstrated good discrimination and calibration.

      Conclusions

      Routinely available clinical data that include UACR in patients with CKD can reliably identify individuals at risk of HF hospitalizations.

      Graphical abstract

      Key Words

      Abbreviations:

      CKD (chronic kidney disease), HF (heart failure), CVD (cardiovascular disease), ACC/AHA (American College of Cardiology/American Heart Association), PCP-HF (Pooled Cohort Equations to Prevent Heart Failure), SGLT2i (sodium glucose co-transporter 2 inhibitors), CRIC (Chronic Renal Insufficiency Cohort), eGFR (estimated glomerular filtration rate), MESA (Multi-Ethnic Study of Atherosclerosis), CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration), SD (standard deviation), IQR (interquartile ranges), TC (total cholesterol), GND (Greenwood-Nam-D'Agostino), NRI (net reclassification improvement), IDI (integrated discrimination improvement), ESRD (end stage renal disease), BMI (body mass index), CI (confidence interval)
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