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

Rupal Mehta*, Hongyan Ning, Nisha Bansal, Jordana Cohen, Anand Srivastava, Mirela Dobre, Erin D. Michos, Mahboob Rahman, Raymond Townsend, Stephen Seliger, James P. Lash, Tamara Isakova, Donald M. Lloyd-Jones, Sadiya S. Khan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)540-550
Number of pages11
JournalJournal of Cardiac Failure
Volume28
Issue number4
DOIs
StatePublished - Apr 2022

Keywords

  • albuminuria
  • chronic kidney disease
  • heart failure
  • kidney function
  • risk prediction

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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