Modeling anesthetic times. Predictors and implications for short-term outcomes

Background: Little is known about the predictors of anesthetic times and impact of anesthetic and operative times on patient outcomes. Methods: We documented operative case length, anesthetic induction time length, and anesthetic recovery time length in 1713 consecutive patients who underwent elective vascular surgical interventions. We recorded patient and procedure-related characteristics that might influence the anesthetic time length, including a variable for possible July effect. Multivariate linear regression was used to model the length of anesthetic times. Multivariate logistic regression was used to model the impact of anesthetic and operative time lengths on a composite outcome of perioperative (30-d postoperative) death, myocardial infarction, cardiac arrhythmias, stroke, and congestive heart failure. Results: Statistically significant predictors of anesthetic induction time included body mass index, anesthesia type, and procedure type. Statistically significant predictors of anesthetic recovery time included operative case length, procedure type, and anesthesia type. After adjusting for the statistically significant covariates of total blood transfusion, history of coronary artery disease, and procedure type, there was a trend for increased likelihood of the composite end point as a function of operative time (odds ratio, 1.14; 95% confidence interval, 0.97-1.33; P = 0.09), which did not reach statistical significance. Multivariate analysis showed no association between the anesthetic time and composite end point. Conclusions: Modeling individually anesthetic induction and recovery time on the basis of operative and anesthetic procedure characteristics is feasible. Anesthetic and operative times do not impact perioperative morbidity and mortality. Published by Elsevier Inc.