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Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index.

Publication ,  Journal Article
Khera, R; Pandey, A; Ayers, CR; Carnethon, MR; Greenland, P; Ndumele, CE; Nambi, V; Seliger, SL; Chaves, PHM; Safford, MM; Cushman, M ...
Published in: JAMA Netw Open
October 1, 2020

IMPORTANCE: Obesity is a global health challenge and a risk factor for atherosclerotic cardiovascular disease (ASVCD). Performance of the pooled cohort equations (PCE) for ASCVD risk by body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is unknown. OBJECTIVE: To assess performance of the PCE across clinical BMI categories. DESIGN, SETTING, AND PARTICIPANTS: This cohort study used pooled individual-level data from 8 community-based, prospective, longitudinal cohort studies with 10-year ASCVD event follow-up from 1996 to 2016. We included all adults ages 40 to 79 years without baseline ASCVD or statin use, resulting in a sample size of 37 311 participants. Data were analyzed from August 2017 to July 2020. EXPOSURES: Participant BMI category: underweight (<18.5), normal weight (18.5 to <25), overweight (25 to <30), mild obesity (30 to <35), and moderate to severe obesity (≥35). MAIN OUTCOMES AND MEASURES: Discrimination (Harrell C statistic) and calibration (Nam-D'Agostino χ2 goodness-of-fit test) of the PCE across BMI categories. Improvement in discrimination and net reclassification with addition of BMI, waist circumference, and high-sensitivity C-reactive protein (hsCRP) to the PCE. RESULTS: Among 37 311 participants (mean [SD] age, 58.6 [11.8] years; 21 897 [58.7%] women), 380 604 person-years of follow-up were conducted. Mean (SD) baseline BMI was 29.0 (6.2), and 360 individuals (1.0%) were in the underweight category, 9937 individuals (26.6%) were in the normal weight category, 13 601 individuals (36.4%) were in the overweight category, 7783 individuals (20.9%) were in the mild obesity category, and 5630 individuals (15.1%) were in the moderate to severe obesity category. Median (interquartile range [IQR]) 10-year estimated ASCVD risk was 7.1% (2.5%-15.4%), and 3709 individuals (9.9%) developed ASCVD over a median (IQR) 10.8 [8.5-12.6] years. The PCE overestimated ASCVD risk in the overall cohort (estimated/observed [E/O] risk ratio, 1.22; 95% CI, 1.18-1.26) and across all BMI categories except the underweight category. Calibration was better near the clinical decision threshold in all BMI groups but worse among individuals with moderate or severe obesity (E/O risk ratio, 1.36; 95% CI, 1.25-1.47) and among those with the highest estimated ASCVD risk ≥20%. The PCE C statistic overall was 0.760 (95% CI, 0.753-0.767), with lower discrimination in the moderate or severe obesity group (C statistic, 0.742; 95% CI, 0.721-0.763) compared with the normal-range BMI group (C statistic, 0.785; 95% CI, 0.772-0.798). Waist circumference (hazard ratio, 1.07 per 1-SD increase; 95% CI, 1.03-1.11) and hsCRP (hazard ratio, 1.07 per 1-SD increase; 95% CI, 1.05-1.09), but not BMI, were associated with increased ASCVD risk when added to the PCE. However, these factors did not improve model performance (C statistic, 0.760; 95% CI, 0.753-0.767) with or without added metrics. CONCLUSIONS AND RELEVANCE: These findings suggest that the PCE had acceptable model discrimination and were well calibrated at clinical decision thresholds but overestimated risk of ASCVD for individuals in overweight and obese categories, particularly individuals with high estimated risk. Incorporation of the usual clinical measures of obesity did not improve risk estimation of the PCE. Future research is needed to determine whether incorporation of alternative high-risk obesity markers (eg, weight trajectory or measures of visceral or ectopic fat) into the PCE may improve risk prediction.

Duke Scholars

Published In

JAMA Netw Open

DOI

EISSN

2574-3805

Publication Date

October 1, 2020

Volume

3

Issue

10

Start / End Page

e2023242

Location

United States

Related Subject Headings

  • Risk Factors
  • Risk Assessment
  • Prospective Studies
  • Proportional Hazards Models
  • Middle Aged
  • Male
  • Longitudinal Studies
  • Humans
  • Female
  • Correlation of Data
 

Citation

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Khera, R., Pandey, A., Ayers, C. R., Carnethon, M. R., Greenland, P., Ndumele, C. E., … Neeland, I. J. (2020). Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index. JAMA Netw Open, 3(10), e2023242. https://doi.org/10.1001/jamanetworkopen.2020.23242
Khera, Rohan, Ambarish Pandey, Colby R. Ayers, Mercedes R. Carnethon, Philip Greenland, Chiadi E. Ndumele, Vijay Nambi, et al. “Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index.JAMA Netw Open 3, no. 10 (October 1, 2020): e2023242. https://doi.org/10.1001/jamanetworkopen.2020.23242.
Khera R, Pandey A, Ayers CR, Carnethon MR, Greenland P, Ndumele CE, et al. Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index. JAMA Netw Open. 2020 Oct 1;3(10):e2023242.
Khera, Rohan, et al. “Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index.JAMA Netw Open, vol. 3, no. 10, Oct. 2020, p. e2023242. Pubmed, doi:10.1001/jamanetworkopen.2020.23242.
Khera R, Pandey A, Ayers CR, Carnethon MR, Greenland P, Ndumele CE, Nambi V, Seliger SL, Chaves PHM, Safford MM, Cushman M, Xanthakis V, Vasan RS, Mentz RJ, Correa A, Lloyd-Jones DM, Berry JD, de Lemos JA, Neeland IJ. Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index. JAMA Netw Open. 2020 Oct 1;3(10):e2023242.

Published In

JAMA Netw Open

DOI

EISSN

2574-3805

Publication Date

October 1, 2020

Volume

3

Issue

10

Start / End Page

e2023242

Location

United States

Related Subject Headings

  • Risk Factors
  • Risk Assessment
  • Prospective Studies
  • Proportional Hazards Models
  • Middle Aged
  • Male
  • Longitudinal Studies
  • Humans
  • Female
  • Correlation of Data