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An international model to predict recurrent cardiovascular disease.

Publication ,  Journal Article
Wilson, PWF; D'Agostino, R; Bhatt, DL; Eagle, K; Pencina, MJ; Smith, SC; Alberts, MJ; Dallongeville, J; Goto, S; Hirsch, AT; Liau, C-S ...
Published in: Am J Med
July 2012

BACKGROUND: Prediction models for cardiovascular events and cardiovascular death in patients with established cardiovascular disease are not generally available. METHODS: Participants from the prospective REduction of Atherothrombosis for Continued Health (REACH) Registry provided a global outpatient population with known cardiovascular disease at entry. Cardiovascular prediction models were estimated from the 2-year follow-up data of 49,689 participants from around the world. RESULTS: A developmental prediction model was estimated from 33,419 randomly selected participants (2394 cardiovascular events with 1029 cardiovascular deaths) from the pool of 49,689. The number of vascular beds with clinical disease, diabetes, smoking, low body mass index, history of atrial fibrillation, cardiac failure, and history of cardiovascular event(s) <1 year before baseline examination increased risk of a subsequent cardiovascular event. Statin (hazard ratio 0.75; 95% confidence interval, 0.69-0.82) and acetylsalicylic acid therapy (hazard ratio 0.90; 95% confidence interval, 0.83-0.99) also were significantly associated with reduced risk of cardiovascular events. The prediction model was validated in the remaining 16,270 REACH subjects (1172 cardiovascular events, 494 cardiovascular deaths). Risk of cardiovascular death was similarly estimated with the same set of risk factors. Simple algorithms were developed for prediction of overall cardiovascular events and for cardiovascular death. CONCLUSIONS: This study establishes and validates a risk model to predict secondary cardiovascular events and cardiovascular death in outpatients with established atherothrombotic disease. Traditional risk factors, burden of disease, lack of treatment, and geographic location all are related to an increased risk of subsequent cardiovascular morbidity and cardiovascular mortality.

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Published In

Am J Med

DOI

EISSN

1555-7162

Publication Date

July 2012

Volume

125

Issue

7

Start / End Page

695 / 703.e1

Location

United States

Related Subject Headings

  • Vascular Diseases
  • Risk Assessment
  • Recurrence
  • Models, Cardiovascular
  • Male
  • Humans
  • General & Internal Medicine
  • Female
  • Algorithms
  • Aged
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Wilson, P. W. F., D’Agostino, R., Bhatt, D. L., Eagle, K., Pencina, M. J., Smith, S. C., … REACH Registry, . (2012). An international model to predict recurrent cardiovascular disease. Am J Med, 125(7), 695-703.e1. https://doi.org/10.1016/j.amjmed.2012.01.014
Wilson, Peter W. F., Ralph D’Agostino, Deepak L. Bhatt, Kim Eagle, Michael J. Pencina, Sidney C. Smith, Mark J. Alberts, et al. “An international model to predict recurrent cardiovascular disease.Am J Med 125, no. 7 (July 2012): 695-703.e1. https://doi.org/10.1016/j.amjmed.2012.01.014.
Wilson PWF, D’Agostino R, Bhatt DL, Eagle K, Pencina MJ, Smith SC, et al. An international model to predict recurrent cardiovascular disease. Am J Med. 2012 Jul;125(7):695-703.e1.
Wilson, Peter W. F., et al. “An international model to predict recurrent cardiovascular disease.Am J Med, vol. 125, no. 7, July 2012, pp. 695-703.e1. Pubmed, doi:10.1016/j.amjmed.2012.01.014.
Wilson PWF, D’Agostino R, Bhatt DL, Eagle K, Pencina MJ, Smith SC, Alberts MJ, Dallongeville J, Goto S, Hirsch AT, Liau C-S, Ohman EM, Röther J, Reid C, Mas J-L, Steg PG, REACH Registry. An international model to predict recurrent cardiovascular disease. Am J Med. 2012 Jul;125(7):695-703.e1.
Journal cover image

Published In

Am J Med

DOI

EISSN

1555-7162

Publication Date

July 2012

Volume

125

Issue

7

Start / End Page

695 / 703.e1

Location

United States

Related Subject Headings

  • Vascular Diseases
  • Risk Assessment
  • Recurrence
  • Models, Cardiovascular
  • Male
  • Humans
  • General & Internal Medicine
  • Female
  • Algorithms
  • Aged