• 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): 23-31.

ObjectiveTo develop accurate preoperative risk prediction models for multiple adverse postoperative outcomes applicable to a broad surgical population using a parsimonious common set of risk variables and outcomes.Summary Background DataCurrently, preoperative assessment of surgical risk is largely based on subjective clinician experience. We propose a paradigm shift from the current postoperative risk adjustment for cross-hospital comparison to patient-centered quantitative risk assessment during the preoperative evaluation.MethodsWe identify the most common and important predictor variables of postoperative mortality, overall morbidity, and 6 complication clusters from previously published prediction analyses that used forward selection stepwise logistic regression. We then refit the prediction models using only the 8 most common and important predictor variables, and compare the discrimination and calibration of these models to the original full-variable models using the c-index, Hosmer-Lemeshow analysis, and Brier scores.ResultsAccurate risk models for 30-day outcomes of mortality, overall morbidity, and 6 clusters of complications were developed using a set of 8 preoperative risk variables. C-indexes of the 8 variable models are between 97.9% and 99.2% of those of the full models containing up to 28 variables, indicating excellent discrimination using fewer predictor variables. Hosmer-Lemeshow analyses showed observed to expected event rates to be nearly identical between parsimonious models and full models, both showing good calibration.ConclusionsAccurate preoperative risk assessment of postoperative mortality, overall morbidity, and 6 complication clusters in a broad surgical population can be achieved with as few as 8 preoperative predictor variables, improving feasibility of routine preoperative risk assessment for surgical patients.

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