Need for better risk models in very elderly surgery: evaluating ACS-NSQIP in patients = 85 years

BACKGROUND: cholecystectomy is the treatment of choice for symptomatic cholelithiasis, although its safety in very elderly patients remains a cause for concern. Accurate risk prediction tools are essential to guide decision-making in this vulnerable population. METHODS: a retrospective cohort study was conducted that included patients ≥85 years of age who underwent cholecystectomy for symptomatic cholelithiasis in a tertiary hospital. Clinical and surgical variables were collected, and postoperative complications were classified according to Clavien-Dindo. The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) risk calculator was applied to each patient. Discrimination was evaluated using the area under the curve (AUC) and calibration using the Brier score, Spiegelhalter test, standardized differences (d-values), and overlap between observed and predicted probabilities. RESULTS: eighty-five patients were included, 65 (76.5%) elective and 20 (23.5%) urgent. Overall morbidity was 32.9% and mortality was 4.7%, both significantly higher in urgent surgery (60% and 15%) than in elective surgery (24.6% and 1.5%). The ACS-NSQIP showed good discrimination in the overall cohort for any complication (AUC 0.741) and serious complications (AUC 0.760), with satisfactory performance for cardiac complications, surgical site infection, and mortality, but limited performance for renal failure and sepsis. In elective surgery, calibration was adequate, with concordance between observed and predicted outcomes. In urgent surgery, the model markedly underestimated the actual risk, especially for complications (60% vs. 17%) and mortality (15% vs. 3.3%). CONCLUSIONS: in patients ≥85 years of age, ACS-NSQIP adequately predicts risk in elective cholecystectomies but underestimates morbidity and mortality in urgent surgery. The incorporation of frailty parameters and geriatric assessment could improve perioperative decision-making in this high-risk population.