Predicting Complications Following Robot-Assisted Partial Nephrectomy with the ACS NSQIP ® Universal Surgical Risk Calculator

The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) collects detailed clinical data from participating hospitals using standardized data definitions, analyzes these data, and provides participating hospitals with reports that permit risk-adjusted comparisons with a surgical quality standard. Since its inception, the ACS NSQIP has worked to refine surgical outcomes measurements and enhance statistical methods to improve the reliability and validity of this hospital profiling. From an original focus on controlling for between-hospital differences in patient risk factors with logistic regression, ACS NSQIP has added a variable to better adjust for the complexity and risk profile of surgical procedures (procedure mix adjustment) and stabilized estimates derived from small samples by using a hierarchical model with shrinkage adjustment. New models have been developed focusing on specific surgical procedures (eg, "Procedure Targeted" models), which provide opportunities to incorporate indication and other procedure-specific variables and outcomes to improve risk adjustment. In addition, comparative benchmark reports given to participating hospitals have been expanded considerably to allow more detailed evaluations of performance. Finally, procedures have been developed to estimate surgical risk for individual patients. This article describes the development of, and justification for, these new statistical methods and reporting strategies in ACS NSQIP.

To develop a risk prediction model for serious complications after bariatric surgery. Despite evidence for improved safety with bariatric surgery, serious complications remain a concern for patients, providers and payers. There is little population-level data on which risk factors can be used to identify patients at high risk for major morbidity. The Michigan Bariatric Surgery Collaborative is a statewide consortium of hospitals and surgeons, which maintains an externally-audited prospective clinical registry. We analyzed data from 25,469 patients undergoing bariatric surgery between June 2006 and December 2010. Significant risk factors on univariable analysis were entered into a multivariable logistic regression model to identify factors associated with serious complications (life threatening and/or associated with lasting disability) within 30 days of surgery. Bootstrap resampling was performed to obtain bias-corrected confidence intervals and c-statistic. Overall, 644 patients (2.5%) experienced a serious complication. Significant risk factors (P < 0.05) included: prior VTE (odds ratio [OR] 1.90, confidence interval [CI] 1.41-2.54); mobility limitations (OR 1.61, CI 1.23-2.13); coronary artery disease (OR 1.53, CI 1.17-2.02); age over 50 (OR 1.38, CI 1.18-1.61); pulmonary disease (OR 1.37, CI 1.15-1.64); male gender (OR 1.26, CI 1.06-1.50); smoking history (OR 1.20, CI 1.02-1.40); and procedure type (reference lap band): duodenal switch (OR 9.68, CI 6.05-15.49); laparoscopic gastric bypass (OR 3.58, CI 2.79-4.64); open gastric bypass (OR 3.51, CI 2.38-5.22); sleeve gastrectomy (OR 2.46, CI 1.73-3.50). The c-statistic was 0.68 (bias-corrected to 0.66) and the model was well-calibrated across deciles of predicted risk. We have developed and validated a population-based risk scoring system for serious complications after bariatric surgery. We expect that this scoring system will improve the process of informed consent, facilitate the selection of procedures for high-risk patients, and allow for better risk stratification across studies of bariatric surgery.