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Khan SS, Matsushita K, Sang Y, Ballew SH, Grams ME, Surapaneni A, Blaha MJ, Carson AP, Chang AR, Ciemins E, Go AS, Gutierrez OM, Hwang SJ, Jassal SK, Kovesdy CP, Lloyd-Jones DM, Shlipak MG, Palaniappan LP, Sperling L, Virani SS, Tuttle K, Neeland IJ, Chow SL, Rangaswami J, Pencina MJ, Ndumele CE, Coresh J. Development and Validation of the American Heart Association's PREVENT Equations. Circulation 2024; 149:430-449. [PMID: 37947085 PMCID: PMC10910659 DOI: 10.1161/circulationaha.123.067626] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the American Heart Association Predicting Risk of CVD EVENTs (PREVENT) equations among US adults 30 to 79 years of age without known CVD. METHODS The derivation sample included individual-level participant data from 25 data sets (N=3 281 919) between 1992 and 2017. The primary outcome was CVD (atherosclerotic CVD and heart failure). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, antihypertensive or statin use, and diabetes) and estimated glomerular filtration rate. Models were sex-specific, race-free, developed on the age scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each data set and meta-analyzed. Discrimination was assessed using the Harrell C-statistic. Calibration was calculated as the slope of the observed versus predicted risk by decile. Additional equations to predict each CVD subtype (atherosclerotic CVD and heart failure) and include optional predictors (urine albumin-to-creatinine ratio and hemoglobin A1c), and social deprivation index were also developed. External validation was performed in 3 330 085 participants from 21 additional data sets. RESULTS Among 6 612 004 adults included, mean±SD age was 53±12 years, and 56% were women. Over a mean±SD follow-up of 4.8±3.1 years, there were 211 515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763-0.809) in female and 0.757 (0.727-0.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81-1.16) and 0.94 (0.81-1.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD- and heart failure-specific models. The improvement in discrimination was small but statistically significant when urine albumin-to-creatinine ratio, hemoglobin A1c, and social deprivation index were added together to the base model to total CVD (ΔC-statistic [interquartile interval] 0.004 [0.004-0.005] and 0.005 [0.004-0.007] among female and male participants, respectively). Calibration improved significantly when the urine albumin-to-creatinine ratio was added to the base model among those with marked albuminuria (>300 mg/g; 1.05 [0.84-1.20] versus 1.39 [1.14-1.65]; P=0.01). CONCLUSIONS PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults by using routinely available clinical variables.
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Affiliation(s)
- Sadiya S Khan
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (S.S.K.)
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)
| | - Yingying Sang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)
- Department of Population Health, New York University Grossman School of Medicine, New York, NY (Y.S., S.H.B., J.C.)
| | - Shoshana H Ballew
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)
- Department of Population Health, New York University Grossman School of Medicine, New York, NY (Y.S., S.H.B., J.C.)
| | - Morgan E Grams
- Department of Medicine, Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY (M.E.G., A.S.)
| | - Aditya Surapaneni
- Department of Medicine, Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY (M.E.G., A.S.)
| | - Michael J Blaha
- Johns Hopkins Ciccarone Center for Prevention of Cardiovascular Disease, Baltimore, MD (M.J.B.)
| | - April P Carson
- University of Mississippi Medical Center, Jackson (A.P.C.)
| | - Alexander R Chang
- Departments of Nephrology and Population Health Sciences, Geisinger Health, Danville, PA (A.R.C.)
| | | | - Alan S Go
- Division of Research, Kaiser Permanente Northern California, Oakland; Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA; Departments of Epidemiology, Biostatistics and Medicine, University of California, San Francisco; Department of Medicine (Nephrology), Stanford University School of Medicine, Palo Alto, CA (A.S,G.)
| | - Orlando M Gutierrez
- Departments of Epidemiology and Medicine, University of Alabama at Birmingham (O.M.G.)
| | - Shih-Jen Hwang
- National Heart, Lung, and Blood Institute, Framingham, MA (S.-J.H.)
| | - Simerjot K Jassal
- Division of General Internal Medicine, University of California, San Diego and VA San Diego Healthcare, CA (S.K.J.)
| | - Csaba P Kovesdy
- Medicine-Nephrology, Memphis Veterans Affairs Medical Center and University of Tennessee Health Science Center, Memphis (C.P.K.)
| | - Donald M Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL (D.M.L.-J.)
| | - Michael G Shlipak
- Department of Medicine, Epidemiology, and Biostatistics, University of California, San Francisco, and San Francisco VA Medical Center (M.G.S.)
| | - Latha P Palaniappan
- Center for Asian Health Research and Education and the Department of Medicine, Stanford University School of Medicine, CA (L.P.P.)
| | | | - Salim S Virani
- Department of Medicine, The Aga Khan University, Karachi, Pakistan; Texas Heart Institute and Baylor College of Medicine, Houston (S.S.V.)
| | - Katherine Tuttle
- Providence Medical Research Center, Providence Inland Northwest Health, Spokane, WA; Kidney Research Institute and Institute of Translational Health Sciences, University of Washington, Seattle (K.T.)
| | - Ian J Neeland
- UH Center for Cardiovascular Prevention, Translational Science Unit, Center for Integrated and Novel Approaches in Vascular-Metabolic Disease (CINEMA), Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, OH (I.J.N.)
| | - Sheryl L Chow
- Department of Pharmacy Practice and Administration, College of Pharmacy, Western University of Health Sciences, Pomona, CA (S.L.C.)
| | - Janani Rangaswami
- Washington DC VA Medical Center and George Washington University School of Medicine (J.R.)
| | - Michael J Pencina
- Department of Biostatistics, Duke University Medical Center, Durham, NC (M.J.P.)
| | - Chiadi E Ndumele
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD (C.E.N.)
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)
- Department of Population Health, New York University Grossman School of Medicine, New York, NY (Y.S., S.H.B., J.C.)
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Khan SS, Coresh J, Pencina MJ, Ndumele CE, Rangaswami J, Chow SL, Palaniappan LP, Sperling LS, Virani SS, Ho JE, Neeland IJ, Tuttle KR, Rajgopal Singh R, Elkind MSV, Lloyd-Jones DM. Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association. Circulation 2023; 148:1982-2004. [PMID: 37947094 DOI: 10.1161/cir.0000000000001191] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.
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Diabetes Mellitus and Heart Failure: Epidemiology, Pathophysiologic Mechanisms, and the Role of SGLT2 Inhibitors. Life (Basel) 2023; 13:life13020497. [PMID: 36836854 PMCID: PMC9968235 DOI: 10.3390/life13020497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/04/2023] [Accepted: 02/08/2023] [Indexed: 02/15/2023] Open
Abstract
Diabetes mellitus (DM) and heart failure (HF) are frequently encountered afflictions that are linked by a common pathophysiologic background. According to landmark studies, those conditions frequently coexist, and this interaction represents a poor prognostic indicator. Based on mechanistic studies, HF can be propagated by multiple pathophysiologic pathways, such as inflammation, oxidative stress, endothelial dysfunction, fibrosis, cardiac autonomic neuropathy, and alterations in substrate utilization. In this regard, DM may augment myocardial inflammation, fibrosis, autonomic dysfunction, and lipotoxicity. As the interaction between DM and HF appears critical, the new cornerstone in DM and HF treatment, sodium-glucose cotransporter-2 inhibitors (SGLT2i), may be able to revert the pathophysiology of those conditions and lead to beneficial HF outcomes. In this review, we aim to highlight the deleterious pathophysiologic interaction between DM and HF, as well as demonstrate the beneficial role of SGLT2i in this field.
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