A Multidimensional Approach of Surgical Mortality Assessment and Stratification (Smatt Score)

Abstract

Surgical mortality is the most significant measure of outcome in surgical healthcare. The objective was to assess surgical 30 days mortality and improve the identification of predictors for personalized risk stratification of patients undergoing elective and emergency surgery. The study was conducted as a single-center cohort retrospective observational study, based on the analysis of data collected from patients surgically treated from 2002 to 2014 in a multi-disciplinary research and care referral hospital with global case mix of 1.27. The overall in-hospital mortality rate was 1.89% (95% CI 1.82–1.95). In the univariable analysis, numerous predictors were significantly associated with in-hospital death following surgery. In the multivariable model, age, BMI (Body Mass Index), ASA score, department, planned surgical complexity, surgical priority, previous surgeries in the same hospitalization, cardiovascular, pulmonary, hepato-renal comorbidities, drug intolerance, cancer and AIDS were independently associated with mortality after surgery. At logistic regression, the computed SMATT score (graded 0–100), generated on the basis of multivariate analysis, demonstrated a good discrimination (10-fold cross-validated AUC-ROC 0.945, 95%CI 0.941–0.948) and correctly classified 98.5% of those admissions with a probability of death >50%. The novel SMATT score, based on individual preoperative and surgical factors, accurately predicts mortality and provides dynamic information of the risk in redo/reoperative surgery.