Objectives: To develop and validate a risk-adjusted tool with fewer than 10 variables to measure surgical outcomes in resource-limited hospitals. Design: All National Surgical Quality Improvement Program (NSQIP) preoperative variables were used to develop models to predict inpatient mortality. The models were built by sequential addition of variables selected based on their area under the receiver operator characteristic curve (AUROC) and externally validated using data based on medical record reviews at 1 hospital outside the data set. Setting: Model development was based on data from the NSQIP from 2005 to 2009. Validationwasbasedondatafrom 1nonurbanhospital in the United Statesfrom2009to 2010. Patients: A total of 631 449 patients in NSQIP and 239 patients from the validation hospital. Main Outcome Measures: The AUROC value for each model. Results: The AUROC values reached higher than 90% after only 3 variables (American Society of Anesthesiologists class, functional status at time of surgery, and age). The AUROC values increased to 91% with 4 variables but did not increase significantly with additional variables. On validation, the model with the highest AUROC was the same 3-variable model (0.9398). Conclusions: Fewer than 6 variables may be necessary to develop a risk-adjusted tool to predict inpatient mortality, reducing the cost of collecting variables by 95%. These variables should be easily collectable in resource-poor settings, including low- and middle-income countries, thus creating the first standardized tool to measure surgical outcomes globally. Research is needed to determine which of these limited-variable models is most appropriate in a variety of clinical settings.
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