BACKGROUND: The American College of Surgeons National Surgical Quality Improvement Program collects information related to procedures in the form of the work relative value unit (RVU) and current procedural terminology (CPT) code. We propose and evaluate a fully automated nonparametric learning approach that maps individual CPT codes to perioperative risk. STUDY DESIGN: National Surgical Quality Improvement Program participant use file data for 2005-2006 were used to develop 2 separate support vector machines (SVMs) to learn the relationship between CPT codes and 30-day mortality or morbidity. SVM parameters were determined using cross-validation. SVMs were evaluated on participant use file data for 2007 and 2008. Areas under the receiver operating characteristic curve (AUROCs) were each compared with the respective AUROCs for work RVU and for standard CPT categories. We then compared the AUROCs for multivariable models, including preoperative variables, RVU, and CPT categories, with and without the SVM operation scores. RESULTS: SVM operation scores had AUROCs between 0.798 and 0.822 for mortality and between 0.745 and 0.758 for morbidity on the participant use file used for both training (2005-2006) and testing (2007 and 2008). This was consistently higher than the AUROCs for both RVU and standard CPT categories (p < 0.001). AUROCs of multivariable models were higher for 30-day mortality and morbidity when SVM operation scores were included. This difference was not significant for mortality but statistically significant, although small, for morbidity. CONCLUSIONS: Nonparametric methods from artificial intelligence can translate CPT codes to aid in the assessment of perioperative risk. This approach is fully automated and can complement the use of work RVU or traditional CPT categories in multivariable risk adjustment models like the National Surgical Quality Improvement Program.
BACKGROUND: The American College of Surgeons National Surgical Quality Improvement Program collects information related to procedures in the form of the work relative value unit (RVU) and current procedural terminology (CPT) code. We propose and evaluate a fully automated nonparametric learning approach that maps individual CPT codes to perioperative risk. STUDY DESIGN: National Surgical Quality Improvement Program participant use file data for 2005-2006 were used to develop 2 separate support vector machines (SVMs) to learn the relationship between CPT codes and 30-day mortality or morbidity. SVM parameters were determined using cross-validation. SVMs were evaluated on participant use file data for 2007 and 2008. Areas under the receiver operating characteristic curve (AUROCs) were each compared with the respective AUROCs for work RVU and for standard CPT categories. We then compared the AUROCs for multivariable models, including preoperative variables, RVU, and CPT categories, with and without the SVM operation scores. RESULTS: SVM operation scores had AUROCs between 0.798 and 0.822 for mortality and between 0.745 and 0.758 for morbidity on the participant use file used for both training (2005-2006) and testing (2007 and 2008). This was consistently higher than the AUROCs for both RVU and standard CPT categories (p < 0.001). AUROCs of multivariable models were higher for 30-day mortality and morbidity when SVM operation scores were included. This difference was not significant for mortality but statistically significant, although small, for morbidity. CONCLUSIONS: Nonparametric methods from artificial intelligence can translate CPT codes to aid in the assessment of perioperative risk. This approach is fully automated and can complement the use of work RVU or traditional CPT categories in multivariable risk adjustment models like the National Surgical Quality Improvement Program.