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 language | English (US) |
|---|---|
| Pages (from-to) | 540-550 |
| Number of pages | 11 |
| Journal | Journal of Cardiac Failure |
| Volume | 28 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 2022 |
Funding
This study was supported by grants P30DK114857, R01DK110087 (TI), K24HL150235 (TI), K23DK120811 (AS), K23HL150236 (RM), and the American Heart Association grant 9TPA34890060 (SSK). Research was also supported by the National Institutes of Health's National Center for Advancing Translational Sciences , Grant Number KL2TR001424 (SSK and RM). Research reported in this publication was also supported, in part, by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant #UL1TR001422 (SSK, RM, and DLJ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government. Funding for the CRIC Study was obtained under a cooperative agreement from National Institute of Diabetes and Digestive and Kidney Diseases (U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, and U01DK060902). In addition, this work was supported in part by: the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award NIH/NCATS UL1TR000003, Johns Hopkins University UL1TR000424, University of Maryland GCRC M01 RR-16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research (MICHR) UL1TR000433, University of Illinois at Chicago CTSA UL1RR029879, Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036, Kaiser Permanente NIH/NCRR UCSF-CTSI UL1 RR-024131. MESA was supported by contracts 75N92020D00001, HHSN268201500003I, N01‐HC‐95159, 75N92020D00005, N01‐HC‐95160, 75N92020D00002, N01‐HC‐95161, 75N92020D00003, N01‐HC‐95162, 75N92020D00006, N01‐HC‐95163, 75N92020D00004, N01‐HC‐95164, 75N92020D00007, N01‐HC‐95165, N01‐HC‐95166, N01‐HC‐95167, N01‐HC‐95168, and N01‐HC‐95169 from the NHLBI, and by grants UL1‐TR‐000040, UL1‐TR‐001079, and UL1‐TR‐001420 from NCATS The authors thank the participants, investigators and staff of the CRIC Study and MESA for their time and commitment. CRIC Study Investigators include Lawrence J. Appel, MD, MPH, Harold I. Feldman, MD, MSCE, Alan S. Go, MD, Jiang He, MD, PhD, Robert G. Nelson, MD, PhD, MS, Panduranga S. Rao, MD, Vallabh O Shah, PhD, MS, Mark L. Unruh, MD, MS. A full list of participating MESA investigators and institutions can be found at http://www.mesa?nhlbi.org. RM has interests in AbbVie and Teva Pharmaceuticals Industries and is a consultant and receives honoraria fees from Akebia/Oksuba and AstraZeneca. TI has received honoraria from Akebia Therapeutics. AS received honoraria from Horizon Pharma and AstraZeneca and consulting fees from CVS Caremark. SS has received research funding from Roche Diagnostics and is the co-owner of a U.S. patent, Methods for Assessing Differential Risk for Developing Heart Failure (patent #10,509,044).
Keywords
- albuminuria
- chronic kidney disease
- heart failure
- kidney function
- risk prediction
ASJC Scopus subject areas
- Cardiology and Cardiovascular Medicine
