Evaluation of PREVENT Equations for Estimating Cardiovascular Disease Risk in Individual Asian American and Native Hawaiian and Pacific Islander Groups
Abstract Body (Do not enter title and authors here): Background: The race-free Predicting Risk of cardiovascular disease (CVD) EVENTS (PREVENT) equations estimate 10-year risk for total CVD. However, their accuracy in individual Asian or Native Hawaiian and Pacific Islander (NHPI) ethnic groups remain unknown.
Research Question: What is the risk prediction accuracy of the PREVENT Base and Full Equations in Asian and NHPI ethnic groups?
Methods: A retrospective cohort study was conducted among adults aged 30-79 years without a history of CVD who self-reported as non-Hispanic (NH) White, NH Asian, or NH NHPI and were actively enrolled as members of Kaiser Permanente Southern California on September 30th, 2009, with follow-up through 2019. Asian participants were further disaggregated into ethnic groups. Ten-year risk for total CVD was estimated using the PREVENT Base and Full (Base plus hemoglobin A1c, urine albumin-creatinine ratio, and social deprivation index) equations. Model discrimination was evaluated using Harrell’s C-index, and calibration assessed using predicted-to-observed event ratios.
Results: Among 542,848 adults (424,277 NH White; 110,855 NH Asian; 7,716 NH NHPI), 31,556 CVD events occurred over 10 years. Harrell’s C-index for the PREVENT Base equation was 0.764 (95% CI = 0.761–0.767) in NH White, 0.773 (95% CI = 0.765–0.779) in NH Asian, and 0.757 (95% CI = 0.733–0.780) in NH NHPI adults (Table). PREVENT Full equation showed similar C-indexes. Among Asian ethnic groups, PREVENT Base and Full equation discrimination was highest in Chinese (0.810) adults and lowest in Vietnamese (0.735) adults. The PREVENT Base and Full equations underestimated total CVD risk in NH White (mean calibration 0.63–0.84) and NH NHPI (mean calibration 0.74–0.86) adults. In contrast, the PREVENT Base equation overestimated total CVD risk in overall NH Asian adults (mean calibration 1.18) and most disaggregated Asian ethnic groups (mean calibration 1.04-1.39), while the PREVENT Full equation underestimated total CVD risk in overall NH Asian (mean calibration 0.96) and generally well calibrated or underestimated total CVD risk in disaggregated Asian ethnic groups (mean calibration 0.83–1.07).
Conclusions: Despite the overall strong discrimination of the PREVENT Base and Full equations in predicting total CVD over 10 years, calibration varied across disaggregated Asian and NHPI ethnic groups, highlighting the importance of accounting for population heterogeneity in CVD risk assessment.
Au, Michael
( Kaiser Permanente Bernard J. Tyson School of Medicine
, Pasadena
, California
, United States
)
Shah, Nilay
( Northwestern University
, Chicago
, Illinois
, United States
)
Reynolds, Kristi
( Kaiser Permanente Southern California
, Pasadena
, California
, United States
)
An, Jaejin
( Kaiser Permanente Southern California
, Pasadena
, California
, United States
)
Zhang, Yiyi
( Columbia University
, New York
, New York
, United States
)
Zhou, Mengnan
( Kaiser Permanente Southern California
, Pasadena
, California
, United States
)
Choi, Soonie
( Kaiser Permanente Southern California
, Pasadena
, California
, United States
)
Zhou, Hui
( Kaiser Permanente Southern California
, Pasadena
, California
, United States
)
Harrison, Teresa
( Kaiser Permanente Southern California
, Pasadena
, California
, United States
)
Mefford, Matthew
( Kaiser Permanente Southern California
, Pasadena
, California
, United States
)
Lee, Mingsum
( Kaiser Permanente Los Angeles Medical Center
, Los Angeles
, California
, United States
)
Yang, Eugene
( University of Washington
, Seattle
, Washington
, United States
)
Author Disclosures:
Michael Au:DO NOT have relevant financial relationships
| Nilay Shah:DO NOT have relevant financial relationships
| Kristi Reynolds:DO have relevant financial relationships
;
Research Funding (PI or named investigator):Merck:Past (completed)
| Jaejin An:DO have relevant financial relationships
;
Research Funding (PI or named investigator):Bayer:Active (exists now)
; Research Funding (PI or named investigator):Merck:Past (completed)
; Research Funding (PI or named investigator):AstraZeneca:Active (exists now)
| Yiyi Zhang:DO have relevant financial relationships
;
Research Funding (PI or named investigator):NIH/NHLBI:Active (exists now)
| Mengnan Zhou:No Answer
| Soonie Choi:DO have relevant financial relationships
;
Other (please indicate in the box next to the company name):Bayer AG:Active (exists now)
| Hui Zhou:DO NOT have relevant financial relationships
| Teresa Harrison:DO NOT have relevant financial relationships
| Matthew Mefford:DO have relevant financial relationships
;
Research Funding (PI or named investigator):Merck & Co., Inc.:Past (completed)
; Other (please indicate in the box next to the company name):Abbott - Honorarium and Travel:Past (completed)
| Mingsum Lee:DO NOT have relevant financial relationships
| Eugene Yang:DO have relevant financial relationships
;
Advisor:Qure.ai:Past (completed)
; Advisor:Mineralys:Active (exists now)
; Other (please indicate in the box next to the company name):American College of Cardiology:Active (exists now)
; Advisor:Idorsia:Active (exists now)
; Advisor:Genentech:Past (completed)
; Advisor:SkyLabs:Active (exists now)