• Robert A Meguid, Michael R Bronsert, Elizabeth Juarez-Colunga, Karl E Hammermeister, and William G Henderson.
• *Surgical Outcomes and Applied Research program, University of Colorado School of Medicine, Aurora, CO†Department of Surgery, University of Colorado School of Medicine, Aurora, CO‡Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora, CO§Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO¶Division of Cardiology, Department of Medicine, University of Colorado School of Medicine, Aurora, CO.
• Ann. Surg. 2016 Jul 1; 264 (1): 10-22.

ObjectiveTo develop parsimonious prediction models for postoperative mortality, overall morbidity, and 6 complication clusters applicable to a broad range of surgical operations in adult patients.Summary Background DataQuantitative risk assessment tools are not routinely used for preoperative patient assessment, shared decision making, informed consent, and preoperative patient optimization, likely due in part to the burden of data collection and the complexity of incorporation into routine surgical practice.MethodsMultivariable forward selection stepwise logistic regression analyses were used to develop predictive models for 30-day mortality, overall morbidity, and 6 postoperative complication clusters, using 40 preoperative variables from 2,275,240 surgical cases in the American College of Surgeons National Surgical Quality Improvement Program data set, 2005 to 2012. For the mortality and overall morbidity outcomes, prediction models were compared with and without preoperative laboratory variables, and generic models (based on all of the data from 9 surgical specialties) were compared with specialty-specific models. In each model, the cumulative c-index was used to examine the contribution of each added predictor variable. C-indexes, Hosmer-Lemeshow analyses, and Brier scores were used to compare discrimination and calibration between models.ResultsFor the mortality and overall morbidity outcomes, the prediction models without the preoperative laboratory variables performed as well as the models with the laboratory variables, and the generic models performed as well as the specialty-specific models. The c-indexes were 0.938 for mortality, 0.810 for overall morbidity, and for the 6 complication clusters ranged from 0.757 for infectious to 0.897 for pulmonary complications. Across the 8 prediction models, the first 7 to 11 variables entered accounted for at least 99% of the c-index of the full model (using up to 28 nonlaboratory predictor variables).ConclusionsOur results suggest that it will be possible to develop parsimonious models to predict 8 important postoperative outcomes for a broad surgical population, without the need for surgeon specialty-specific models or inclusion of laboratory variables.

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