Risk stratification after successful coronary revascularization

Masashi Goto, Shun Kosaka, Noriaki Aoki, Vei Vei Lee, MacArthur A. Elayda, James M. Wilson

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Background: Clinicians treating coronary revascularization patients need to be able to identify those who require more intensive medical therapy or follow-up. However, predictors of survival after coronary revascularization are often reported in terms of hazard ratios, which are accurate but difficult to convert to concrete values. We sought to develop a more practical and user-friendly method of predicting long-term survival in revascularization patients. Methods: We used a decision-tree induction algorithm to retrospectively examine all-cause mortality during 3-year follow-up in 3331 consecutive patients with multivessel or single proximal left anterior descending coronary artery disease who underwent an isolated first revascularization by either coronary stenting or coronary artery bypass graft between 1995 and 1999. Results: Recursive partitioning of the derivation cohort by the algorithm indicated that the best single predictor of long-term mortality was history of congestive heart failure, followed by age greater than 65 years and the presence of renal insufficiency. With these three variables, patients were readily stratified into low-, intermediate-, and high-risk groups whose 3-year mortality risks ranged from 2.0% to 18.8%. Logistic regression revealed nine significant predictors of 3-year mortality, including two interaction terms. Areas under the receiver operation characteristic curve for prediction of 3-year mortality were not significantly different between the decision tree and the logistic regression models [0.72 (95% confidence interval, 0.69 to 0.75) vs. 0.76 (95% confidence interval, 0.73 to 0.80)]. Conclusions: Long-term mortality risk in coronary revascularization patients can be estimated from three predictors that are easily obtained in clinical settings.

Original languageEnglish
Pages (from-to)132-139
Number of pages8
JournalCardiovascular Revascularization Medicine
Volume9
Issue number3
DOIs
Publication statusPublished - 2008 Jul
Externally publishedYes

Fingerprint

Mortality
Decision Trees
Logistic Models
Confidence Intervals
Survival
Coronary Artery Bypass
Renal Insufficiency
Coronary Artery Disease
Heart Failure
Transplants
Therapeutics

Keywords

  • Coronary arteriosclerosis
  • Myocardial revascularization
  • Prognosis

ASJC Scopus subject areas

  • Molecular Medicine
  • Cardiology and Cardiovascular Medicine
  • Surgery

Cite this

Risk stratification after successful coronary revascularization. / Goto, Masashi; Kosaka, Shun; Aoki, Noriaki; Lee, Vei Vei; Elayda, MacArthur A.; Wilson, James M.

In: Cardiovascular Revascularization Medicine, Vol. 9, No. 3, 07.2008, p. 132-139.

Research output: Contribution to journalArticle

Goto, Masashi ; Kosaka, Shun ; Aoki, Noriaki ; Lee, Vei Vei ; Elayda, MacArthur A. ; Wilson, James M. / Risk stratification after successful coronary revascularization. In: Cardiovascular Revascularization Medicine. 2008 ; Vol. 9, No. 3. pp. 132-139.
@article{26d89a34ce2e48b5b8ca05e749a37b51,
title = "Risk stratification after successful coronary revascularization",
abstract = "Background: Clinicians treating coronary revascularization patients need to be able to identify those who require more intensive medical therapy or follow-up. However, predictors of survival after coronary revascularization are often reported in terms of hazard ratios, which are accurate but difficult to convert to concrete values. We sought to develop a more practical and user-friendly method of predicting long-term survival in revascularization patients. Methods: We used a decision-tree induction algorithm to retrospectively examine all-cause mortality during 3-year follow-up in 3331 consecutive patients with multivessel or single proximal left anterior descending coronary artery disease who underwent an isolated first revascularization by either coronary stenting or coronary artery bypass graft between 1995 and 1999. Results: Recursive partitioning of the derivation cohort by the algorithm indicated that the best single predictor of long-term mortality was history of congestive heart failure, followed by age greater than 65 years and the presence of renal insufficiency. With these three variables, patients were readily stratified into low-, intermediate-, and high-risk groups whose 3-year mortality risks ranged from 2.0{\%} to 18.8{\%}. Logistic regression revealed nine significant predictors of 3-year mortality, including two interaction terms. Areas under the receiver operation characteristic curve for prediction of 3-year mortality were not significantly different between the decision tree and the logistic regression models [0.72 (95{\%} confidence interval, 0.69 to 0.75) vs. 0.76 (95{\%} confidence interval, 0.73 to 0.80)]. Conclusions: Long-term mortality risk in coronary revascularization patients can be estimated from three predictors that are easily obtained in clinical settings.",
keywords = "Coronary arteriosclerosis, Myocardial revascularization, Prognosis",
author = "Masashi Goto and Shun Kosaka and Noriaki Aoki and Lee, {Vei Vei} and Elayda, {MacArthur A.} and Wilson, {James M.}",
year = "2008",
month = "7",
doi = "10.1016/j.carrev.2008.03.005",
language = "English",
volume = "9",
pages = "132--139",
journal = "Cardiovascular Revascularization Medicine",
issn = "1553-8389",
publisher = "Elsevier Inc.",
number = "3",

}

TY - JOUR

T1 - Risk stratification after successful coronary revascularization

AU - Goto, Masashi

AU - Kosaka, Shun

AU - Aoki, Noriaki

AU - Lee, Vei Vei

AU - Elayda, MacArthur A.

AU - Wilson, James M.

PY - 2008/7

Y1 - 2008/7

N2 - Background: Clinicians treating coronary revascularization patients need to be able to identify those who require more intensive medical therapy or follow-up. However, predictors of survival after coronary revascularization are often reported in terms of hazard ratios, which are accurate but difficult to convert to concrete values. We sought to develop a more practical and user-friendly method of predicting long-term survival in revascularization patients. Methods: We used a decision-tree induction algorithm to retrospectively examine all-cause mortality during 3-year follow-up in 3331 consecutive patients with multivessel or single proximal left anterior descending coronary artery disease who underwent an isolated first revascularization by either coronary stenting or coronary artery bypass graft between 1995 and 1999. Results: Recursive partitioning of the derivation cohort by the algorithm indicated that the best single predictor of long-term mortality was history of congestive heart failure, followed by age greater than 65 years and the presence of renal insufficiency. With these three variables, patients were readily stratified into low-, intermediate-, and high-risk groups whose 3-year mortality risks ranged from 2.0% to 18.8%. Logistic regression revealed nine significant predictors of 3-year mortality, including two interaction terms. Areas under the receiver operation characteristic curve for prediction of 3-year mortality were not significantly different between the decision tree and the logistic regression models [0.72 (95% confidence interval, 0.69 to 0.75) vs. 0.76 (95% confidence interval, 0.73 to 0.80)]. Conclusions: Long-term mortality risk in coronary revascularization patients can be estimated from three predictors that are easily obtained in clinical settings.

AB - Background: Clinicians treating coronary revascularization patients need to be able to identify those who require more intensive medical therapy or follow-up. However, predictors of survival after coronary revascularization are often reported in terms of hazard ratios, which are accurate but difficult to convert to concrete values. We sought to develop a more practical and user-friendly method of predicting long-term survival in revascularization patients. Methods: We used a decision-tree induction algorithm to retrospectively examine all-cause mortality during 3-year follow-up in 3331 consecutive patients with multivessel or single proximal left anterior descending coronary artery disease who underwent an isolated first revascularization by either coronary stenting or coronary artery bypass graft between 1995 and 1999. Results: Recursive partitioning of the derivation cohort by the algorithm indicated that the best single predictor of long-term mortality was history of congestive heart failure, followed by age greater than 65 years and the presence of renal insufficiency. With these three variables, patients were readily stratified into low-, intermediate-, and high-risk groups whose 3-year mortality risks ranged from 2.0% to 18.8%. Logistic regression revealed nine significant predictors of 3-year mortality, including two interaction terms. Areas under the receiver operation characteristic curve for prediction of 3-year mortality were not significantly different between the decision tree and the logistic regression models [0.72 (95% confidence interval, 0.69 to 0.75) vs. 0.76 (95% confidence interval, 0.73 to 0.80)]. Conclusions: Long-term mortality risk in coronary revascularization patients can be estimated from three predictors that are easily obtained in clinical settings.

KW - Coronary arteriosclerosis

KW - Myocardial revascularization

KW - Prognosis

UR - http://www.scopus.com/inward/record.url?scp=46149104344&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=46149104344&partnerID=8YFLogxK

U2 - 10.1016/j.carrev.2008.03.005

DO - 10.1016/j.carrev.2008.03.005

M3 - Article

C2 - 18606375

AN - SCOPUS:46149104344

VL - 9

SP - 132

EP - 139

JO - Cardiovascular Revascularization Medicine

JF - Cardiovascular Revascularization Medicine

SN - 1553-8389

IS - 3

ER -