Novel bypass risk predictive tool is superior to the 5-Factor Modified Frailty Index in predicting postoperative outcomes

Objective This study aimed to develop risk predictive models of 30-day mortality, morbidity, and major adverse limb events (MALE) after bypass surgery for aortoiliac occlusive disease (AIOD) and to compare their performances with a 5-Factor Frailty Index. Methods The American College of Surgeons National Surgical Quality Improvement Program 2012-2017 Procedure Targeted Aortoiliac (Open) Participant Use Data Files were queried to identify all patients who had elective bypass for AIOD: femorofemoral bypass, aortofemoral bypass, and axillofemoral bypass (AXB). Outcomes assessed included mortality, major morbidity, and MALE within 30 days postoperatively. Major morbidity was defined as pneumonia, unplanned intubation, ventilator support for >48 hours, progressive or acute renal failure, cerebrovascular accident, cardiac arrest, or myocardial infarction. Demographics, comorbidities, procedure type, and laboratory values were considered for inclusion in the risk predictive models. Logistic regression models for mortality, major morbidity and MALE were developed. The discriminative ability of these models (C-indices) were compared with that of the 5-Factor Modified Frailty Index (mFI-5): a general frailty tool determined from diabetes, functional status, history of chronic obstructive pulmonary disease, history of congestive heart failure, and hypertension. Calculators were derived using the most significant variables for each of the three risk predictive models. Results A total of 2612 cases (mean age 65.0, 60% male) were identified, of which 1149 (44.0%) were femorofemoral bypass, 1138 (43.6%) were aortofemoral bypass, and 325 (12.4%) were axillofemoral bypass. Overall, the rates of mortality, major morbidity, and MALE were 2.0%, 8.5%, and 4.9%, respectively. Twenty preoperative risk factors were considered for incorporation in the risk tools. Apart from procedure type, age was the most statistically significant predictor of both mortality and morbidity. Preoperative anemia and critical limb ischemia were the most significant predictors of MALE. All three constructed models demonstrated significantly better discriminative ability (P Conclusions Our models outperformed the mFI-5 in predicting 30-day mortality, major morbidity, and adverse limb events in patients with AIOD undergoing elective bypass surgery. Calculators were created using the most statistically significant variables to help calculate individual patient's postoperative risks and allow for better informed consent and risk-adjusted comparison of provider outcomes.