Short-Term Global Cardiovascular Disease Risk Prediction in Older Adults

Anum Saeed, Vijay Nambi, Wensheng Sun, Salim S. Virani, George E. Taffet, Anita Deswal, Elizabeth Selvin, Kunihiro Matsushita, Lynne E. Wagenknecht, Ron Hoogeveen, Josef Coresh, James A. de Lemos, Christie M. Ballantyne

Research output: Contribution to journalArticle

Abstract

Background: Current prevention guidelines recommend using the Pooled Cohort Equation (PCE) for 10-year atherosclerotic cardiovascular disease (CVD) risk assessment. However, the PCE has serious limitations in older adults: it excludes heart failure (HF) hospitalization, estimates 10-year risk, which may not be the most relevant time frame, and is not indicated for individuals age >79 years. Objectives: This study sought to determine whether adding biomarkers to PCE variables improves global CVD (coronary heart disease, stroke, and HF) risk prediction in older adults over a shorter time period. Methods: Atherosclerosis Risk in Communities study participants without prevalent CVD including HF (n = 4,760; age 75.4 ± 5.1 years) were followed for incident global CVD events. Adding N-terminal pro–B-type natriuretic peptide, high-sensitivity cardiac troponin T, and high-sensitivity C-reactive protein to the PCE and a “lab model” with the biomarkers, age, race, and gender were assessed for prediction improvement. Area under the receiver operating characteristic curve (AUC) and net reclassification index (NRI) were calculated. Results: Over median follow-up of ∼4 years, incident HF was the leading CVD event (n = 193 vs. 118 coronary heart disease and 81 stroke events). Compared to the PCE, each biomarker improved risk prediction. The largest improvement in risk prediction metrics was with the addition of all 3 biomarkers (ΔAUC 0.103; continuous NRI 0.484). The lab model also performed better than the PCE model (ΔAUC 0.091, continuous NRI 0.355). Conclusions: Adding biomarkers to the PCE or a simpler “lab model” improves short-term global CVD risk prediction and may be useful to inform short-term preventive strategies in older adults.

Original languageEnglish (US)
Pages (from-to)2527-2536
Number of pages10
JournalJournal of the American College of Cardiology
Volume71
Issue number22
DOIs
StatePublished - Jun 5 2018

Fingerprint

Cardiovascular Diseases
Biomarkers
Heart Failure
Area Under Curve
Coronary Disease
Stroke
Natriuretic Peptides
Troponin T
ROC Curve
C-Reactive Protein
Atherosclerosis
Hospitalization
Guidelines

Keywords

  • biomarkers
  • cardiovascular disease
  • elderly
  • heart failure
  • prevention
  • risk assessment

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Cite this

Saeed, A., Nambi, V., Sun, W., Virani, S. S., Taffet, G. E., Deswal, A., ... Ballantyne, C. M. (2018). Short-Term Global Cardiovascular Disease Risk Prediction in Older Adults. Journal of the American College of Cardiology, 71(22), 2527-2536. https://doi.org/10.1016/j.jacc.2018.02.050

Short-Term Global Cardiovascular Disease Risk Prediction in Older Adults. / Saeed, Anum; Nambi, Vijay; Sun, Wensheng; Virani, Salim S.; Taffet, George E.; Deswal, Anita; Selvin, Elizabeth; Matsushita, Kunihiro; Wagenknecht, Lynne E.; Hoogeveen, Ron; Coresh, Josef; de Lemos, James A.; Ballantyne, Christie M.

In: Journal of the American College of Cardiology, Vol. 71, No. 22, 05.06.2018, p. 2527-2536.

Research output: Contribution to journalArticle

Saeed, A, Nambi, V, Sun, W, Virani, SS, Taffet, GE, Deswal, A, Selvin, E, Matsushita, K, Wagenknecht, LE, Hoogeveen, R, Coresh, J, de Lemos, JA & Ballantyne, CM 2018, 'Short-Term Global Cardiovascular Disease Risk Prediction in Older Adults', Journal of the American College of Cardiology, vol. 71, no. 22, pp. 2527-2536. https://doi.org/10.1016/j.jacc.2018.02.050
Saeed, Anum ; Nambi, Vijay ; Sun, Wensheng ; Virani, Salim S. ; Taffet, George E. ; Deswal, Anita ; Selvin, Elizabeth ; Matsushita, Kunihiro ; Wagenknecht, Lynne E. ; Hoogeveen, Ron ; Coresh, Josef ; de Lemos, James A. ; Ballantyne, Christie M. / Short-Term Global Cardiovascular Disease Risk Prediction in Older Adults. In: Journal of the American College of Cardiology. 2018 ; Vol. 71, No. 22. pp. 2527-2536.
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AU - Nambi, Vijay

AU - Sun, Wensheng

AU - Virani, Salim S.

AU - Taffet, George E.

AU - Deswal, Anita

AU - Selvin, Elizabeth

AU - Matsushita, Kunihiro

AU - Wagenknecht, Lynne E.

AU - Hoogeveen, Ron

AU - Coresh, Josef

AU - de Lemos, James A.

AU - Ballantyne, Christie M.

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N2 - Background: Current prevention guidelines recommend using the Pooled Cohort Equation (PCE) for 10-year atherosclerotic cardiovascular disease (CVD) risk assessment. However, the PCE has serious limitations in older adults: it excludes heart failure (HF) hospitalization, estimates 10-year risk, which may not be the most relevant time frame, and is not indicated for individuals age >79 years. Objectives: This study sought to determine whether adding biomarkers to PCE variables improves global CVD (coronary heart disease, stroke, and HF) risk prediction in older adults over a shorter time period. Methods: Atherosclerosis Risk in Communities study participants without prevalent CVD including HF (n = 4,760; age 75.4 ± 5.1 years) were followed for incident global CVD events. Adding N-terminal pro–B-type natriuretic peptide, high-sensitivity cardiac troponin T, and high-sensitivity C-reactive protein to the PCE and a “lab model” with the biomarkers, age, race, and gender were assessed for prediction improvement. Area under the receiver operating characteristic curve (AUC) and net reclassification index (NRI) were calculated. Results: Over median follow-up of ∼4 years, incident HF was the leading CVD event (n = 193 vs. 118 coronary heart disease and 81 stroke events). Compared to the PCE, each biomarker improved risk prediction. The largest improvement in risk prediction metrics was with the addition of all 3 biomarkers (ΔAUC 0.103; continuous NRI 0.484). The lab model also performed better than the PCE model (ΔAUC 0.091, continuous NRI 0.355). Conclusions: Adding biomarkers to the PCE or a simpler “lab model” improves short-term global CVD risk prediction and may be useful to inform short-term preventive strategies in older adults.

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