Predictive Accuracy of the American College of Surgeons Risk Calculator in Patients Undergoing Major Lower Extremity Amputation.

BACKGROUND

The American College of Surgeons Risk Calculator (ACS-RC) provides an assessment of a patient's risk of 30-day postoperative complications. The Surgeon Adjusted Risk (SAR) parameter of the calculator allows for ad hoc adjustment of risk based on risk factors not considered by the model. This study aims to evaluate the predictive accuracy of the ACS-RC in vascular surgery patients undergoing major lower-extremity amputation (LEA) and identify additional risk factors that warrant use of the SAR parameter.

METHODS

This is a retrospective study of 298 sequential amputations at a single institution. At the population level, the mean of predicted 30-day outcomes from the ACS-RC with a SAR score of 1 (no adjustment necessary) and 2 (risk somewhat higher than estimate) were compared to the rate of observed outcomes. Predictive accuracy at the individual level was completed using receiver operating curve area under the curve (AUC). Logistic regression with respect to mortality was performed over variables not considered by the ACS-RC. Efficacy of selectively utilizing the SAR parameter in predicting mortality was analyzed with a stratified analysis in which patients with risk factors significant for mortality were assigned increased risk.

RESULTS

At the population level, ACS-RC grossly underpredicted serious complications, SSI, VTE, and unplanned RTOR, while overpredicting mortality and cardiac complications. At the individual level, SAR1 was more predictive for serious complications (AUC = 0.624), SSI (AUC = 0.610), and unplanned RTOR (AUC = 0.541). Conversely, SAR2 was more predictive for mortality (AUC = 0.709), cardiac complications (AUC = 0.561), and VTE (AUC = 0.539). Logistic regression identified history of CVA with a residual deficit (OR = 4.61, P = 0.033) and ischemic rest pain without tissue loss (OR = 4.497, P = 0.047) as independent risk factors for postoperative mortality. Stratified analysis with utilization of the SAR2 based on the 2 independent risk factors improved AUC in predicting mortality (AUC 0.792 from 0.709).

CONCLUSIONS

Major LEAs are associated with high perioperative morbidity and mortality. In a veteran population, the ACS-RC showed mixed predictability at the population level and fair predictability at the individual level with regards to postoperative outcomes. Rest pain without tissue loss and history of CVA with residual deficit were identified as risk factors for postoperative mortality. Although ad hoc adjustment with the subjective SAR modifier based on the presence of these 2 risk factors increased the calculator's accuracy, this study highlights some potential limitations of the ACS-RC when applied to vascular surgery patients undergoing major LEA.