Performance of the PREVENT-HF Equations Across Heart Failure Subtypes in Community-Based and Ambulatory Healthcare Samples
Louisa A. Mounsey, Dongyu Wang, Pedro H. Ribeiro, Sophie M. Nemeth, Juhi K. Parekh, Jordan B. Strom, Chiadi Ndumele, Norrina B. Allen, Bruce Psaty, Jorge R. Kizer, Daniel Levy, Rudolf A. de Boer, Mahnaz Maddah, Shaan Khurshid, Patrick T. Ellinor, Emily S. Lau, Jonathan Cunningham, Sadiya S. Khan, Jennifer E. HoBackground: The PREVENT-HF equations estimate 10-year risk of overall heart failure (HF), inclusive of both HF with preserved and reduced ejection fraction (HFpEF; HFrEF). Given differential risk factor profiles, pathophysiology, and therapeutic strategies for each HF subtype, we sought to examine the performance of PREVENT-HF for subtype-specific risk assessment.
Methods: We included data from five pooled longitudinal cohort studies (baseline exam 1989 to 2013) and two longitudinal primary-care electronic health record-based samples (Beth Israel Deaconess Medical Center [BIDMC], baseline 2004 to 2014; Mass General Brigham [MGB], baseline 2001 to 2018). We assessed the performance of the PREVENT-HF equations in predicting 10-year incident HFrEF and HFpEF, including model discrimination (area under the receiver operating characteristic curve [AUROC]; average precision [AP]/event rate) and calibration (integrated calibration index [ICI]), in adults aged 30-79 years without prevalent cardiovascular disease or HF.
Results: The pooled cohort studies included 23,652 (55% women, age 60 ± 13 years), of whom 1,232 developed HF (432 [35%] HFpEF, 522 [42%] HFrEF). The BIDMC sample included 29,815 (55% women, 50 ± 13 years), of whom 259 (1%) developed HF (133 [51%] HFpEF, 101 [39%] HFrEF). MGB included 138,999 (62% women, 52 ± 13 years), of whom 1050 developed HF (586 [56%] HFpEF, 381 [36%] HFrEF). PREVENT-HF demonstrated better discrimination for HFpEF than HFrEF across all samples: in the pooled cohorts, the AUROC for HFpEF was 0.824 (95% CI 0.807, 0.839) versus 0.789 (95% CI 0.770, 0.807) for HFrEF; in BIDMC, the AUROC for HFpEF was 0.897 (95% CI 0.874, 0.918) versus 0.833 (95% CI 0.791, 0.869) for HFrEF; in MGB, the AUROC for HFpEF was 0.864 (95% CI 0.848, 0.880) versus 0.797 (95% CI 0.771, 0.820) for HFrEF. Similar results were reflected in the AP/event rate ratios: 2.92 (95% CI 2.47, 3.54) for HFpEF compared to 2.66 (95% CI 2.33, 3.14) for HFrEF in the pooled cohorts; 8.08 (95% CI 6.12, 12.30) for HFpEF compared to 5.12 (95% CI 3.84, 8.17) for HFrEF in BIDMC; and 6.84 (95% CI 5.66, 8.28) for HFpEF compared to 4.12 (95% CI 3.37, 5.16) for HFrEF in MGB. Calibration was similar for HFpEF and HFrEF across samples. For example, in the pooled cohorts, the ICI for HFpEF was 0.041 (95% CI 0.039, 0.043) and for HFrEF was 0.038 (95% CI 0.036, 0.040).
Conclusions: PREVENT-HF demonstrated excellent discrimination for both HF subtypes across community-based cohorts and real-world samples, with consistently better discrimination for HFpEF as compared to HFrEF. Given PREVENT-HF was developed to estimate overall HF risk rather than subtype-specific risk, future studies are needed to establish subtype-specific risk thresholds.