Variation in mortality risk factors with time after coronary artery bypass graft operation.

BACKGROUND Differences in mortality risk factor sets during different time periods (eg, short-term versus intermediate-term) after coronary artery bypass grafting have been reported. However, little is known about the time-varying effects of mortality risk factors after the operation. METHODS We analyzed 11,815 veterans who had coronary artery bypass grafting at any of the 43 Veterans Affairs cardiac surgery centers from October 1997 to September 1999. Time-varying effects of 14 mortality risk factors during the 210 days after coronary artery bypass grafting were evaluated using Cox B-spline regression, which provides an estimate of risk for each variable for each day after operation. RESULTS Eight variables showed significant time-varying effects after operation. The effect of prior heart operation was very high immediately after operation, but disappeared within 1 week. Three other cardiac variables (prior myocardial infarction, preoperative intraaortic balloon pump, and Canadian Cardiovascular Society anginal class III or IV) also conferred the highest risk on the day of operation and decreased thereafter. In contrast, the four time-varying noncardiac risk variables (age, impaired functional status, chronic obstructive pulmonary disease, and renal dysfunction) showed little or no association with mortality immediately after operation, but had increasing impact during the several months after operation. CONCLUSIONS A sizable number of mortality risk factors have time-varying effects after coronary artery bypass grafting. Several cardiac risk factors have peak impact immediately after operation but dissipate thereafter. Several noncardiac risk factors confer little risk immediately after operation, but these risks increase during several months. This information may help clinicians focus management strategies for patients during the 7 months after operation.

[1]  G. Grunwald,et al.  Risk factors for intermediate-term survival after coronary artery bypass grafting. , 2001, The Annals of thoracic surgery.

[2]  A L Shroyer,et al.  The 1996 coronary artery bypass risk model: the Society of Thoracic Surgeons Adult Cardiac National Database. , 1998, The Annals of thoracic surgery.

[3]  S. Nitter‐Hauge,et al.  Risk Factors for Early and Late Mortality in Surgical Treatment of Coronary Artery Disease , 1995, Cardiovascular surgery.

[4]  E Shapiro,et al.  Self-rated health: a predictor of mortality among the elderly. , 1982, American journal of public health.

[5]  R. Simon,et al.  Flexible regression models with cubic splines. , 1989, Statistics in medicine.

[6]  E L Hannan,et al.  Identification of preoperative variables needed for risk adjustment of short-term mortality after coronary artery bypass graft surgery. The Working Group Panel on the Cooperative CABG Database Project. , 1996, Journal of the American College of Cardiology.

[7]  A. Shroyer,et al.  Calculating risk and outcome: the Veterans Affairs database. , 1996, The Annals of thoracic surgery.

[8]  S. Fisher,et al.  Mortality ascertainment in the veteran population: alternatives to the National Death Index. , 1995, American journal of epidemiology.

[9]  D. Cox Regression Models and Life-Tables , 1972 .

[10]  A. Kshirsagar,et al.  Effect of ACE inhibitors in diabetic and nondiabetic chronic renal disease: a systematic overview of randomized placebo-controlled trials. , 2000, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[11]  Michal Abrahamowicz,et al.  Time-Dependent Hazard Ratio: Modeling and Hypothesis Testing with Application in Lupus Nephritis , 1996 .

[12]  C Safran,et al.  Predicting In‐Hospital Mortality The Importance of Functional Status Information , 1995, Medical care.

[13]  J Concato,et al.  Importance of functional measures in predicting mortality among older hospitalized patients. , 1998, JAMA.

[14]  G. Marshall,et al.  The Veterans Affairs Continuous Improvement in Cardiac Surgery Study. , 1994, The Annals of thoracic surgery.

[15]  K R Hess,et al.  Assessing time-by-covariate interactions in proportional hazards regression models using cubic spline functions. , 1994, Statistics in medicine.

[16]  Eric R. Ziegel,et al.  Survival analysis using the SAS system , 1995 .

[17]  E. Hannan,et al.  Adult open heart surgery in New York State. An analysis of risk factors and hospital mortality rates. , 1990, JAMA.

[18]  C. D. Boor,et al.  B-Splines without Divided Differences. , 1985 .