Optimizing the Prediction of Perioperative Mortality in Vascular Surgery by Using a Customized Probability Model

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Abstract
Background Accurately assessing the probability of perioperative mortality can be useful in preoperative risk assessment and management. This study aimed to revise and customize the revised cardiac risk (Lee) index to estimate the probability of perioperative all-cause mortality in patients undergoing noncardiac vascular surgery. Methods We studied 2310 patients (mean age, 67.8 ± 11.3 years; 1747 males) who underwent acute or elective major noncardiac vascular surgery between January 1, 1991, and December 31, 2000, at the Erasmus Medical Center, Rotterdam, the Netherlands. A total of 1537 patients were assigned for model development, in which the associations between predictor variables and mortality occurring within 30 days after surgery were identified to modify the Lee index, which was then evaluated in a validation cohort of 773 patients. Results The perioperative mortality rates were similar in the development (n = 103 [6.7%]) and validation (n = 50 [6.5%]) populations. The customized risk-prediction model for perioperative mortality identified type of vascular surgery, ischemic heart disease, congestive heart failure, previous stroke, hypertension, renal dysfunction, and chronic pulmonary disease as being associated with increased risk, whereas β-blocker and statin use were associated with a lower risk of mortality. The performance of the customized index had excellent discriminative ability in both derivation and validation populations (concordance statistic, 0.88 and 0.85, respectively). Conclusions The customized index provides more detailed information than the Lee index about the type of vascular procedure, clinical risk factors, and concomitant medication use. The customized probability model can be a useful tool to estimate the risk of perioperative all-cause mortality and facilitate subsequent treatment strategies.