Alexander Van Esbroeck1, Ilan Rubinfeld2, Bruce Hall3, Zeeshan Syed4. 1. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI. 2. Department of Surgery, Henry Ford Hospital, Detroit, MI. 3. Department of Surgery, Washington University, St. Louis, MO. 4. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI. Electronic address: zhs@umich.edu.
Abstract
OBJECTIVE: To investigate the use of machine learning to empirically determine the risk of individual surgical procedures and to improve surgical models with this information. METHODS: American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) data from 2005 to 2009 were used to train support vector machine (SVM) classifiers to learn the relationship between textual constructs in current procedural terminology (CPT) descriptions and mortality, morbidity, Clavien 4 complications, and surgical-site infections (SSI) within 30 days of surgery. The procedural risk scores produced by the SVM classifiers were validated on data from 2010 in univariate and multivariate analyses. RESULTS: The procedural risk scores produced by the SVM classifiers achieved moderate-to-high levels of discrimination in univariate analyses (area under receiver operating characteristic curve: 0.871 for mortality, 0.789 for morbidity, 0.791 for SSI, 0.845 for Clavien 4 complications). Addition of these scores also substantially improved multivariate models comprising patient factors and previously proposed correlates of procedural risk (net reclassification improvement and integrated discrimination improvement: 0.54 and 0.001 for mortality, 0.46 and 0.011 for morbidity, 0.68 and 0.022 for SSI, 0.44 and 0.001 for Clavien 4 complications; P < .05 for all comparisons). Similar improvements were noted in discrimination and calibration for other statistical measures, and in subcohorts comprising patients with general or vascular surgery. CONCLUSION: Machine learning provides clinically useful estimates of surgical risk for individual procedures. This information can be measured in an entirely data-driven manner and substantially improves multifactorial models to predict postoperative complications.
OBJECTIVE: To investigate the use of machine learning to empirically determine the risk of individual surgical procedures and to improve surgical models with this information. METHODS: American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) data from 2005 to 2009 were used to train support vector machine (SVM) classifiers to learn the relationship between textual constructs in current procedural terminology (CPT) descriptions and mortality, morbidity, Clavien 4 complications, and surgical-site infections (SSI) within 30 days of surgery. The procedural risk scores produced by the SVM classifiers were validated on data from 2010 in univariate and multivariate analyses. RESULTS: The procedural risk scores produced by the SVM classifiers achieved moderate-to-high levels of discrimination in univariate analyses (area under receiver operating characteristic curve: 0.871 for mortality, 0.789 for morbidity, 0.791 for SSI, 0.845 for Clavien 4 complications). Addition of these scores also substantially improved multivariate models comprising patient factors and previously proposed correlates of procedural risk (net reclassification improvement and integrated discrimination improvement: 0.54 and 0.001 for mortality, 0.46 and 0.011 for morbidity, 0.68 and 0.022 for SSI, 0.44 and 0.001 for Clavien 4 complications; P < .05 for all comparisons). Similar improvements were noted in discrimination and calibration for other statistical measures, and in subcohorts comprising patients with general or vascular surgery. CONCLUSION: Machine learning provides clinically useful estimates of surgical risk for individual procedures. This information can be measured in an entirely data-driven manner and substantially improves multifactorial models to predict postoperative complications.
Authors: Jun S Kim; Robert K Merrill; Varun Arvind; Deepak Kaji; Sara D Pasik; Chuma C Nwachukwu; Luilly Vargas; Nebiyu S Osman; Eric K Oermann; John M Caridi; Samuel K Cho Journal: Spine (Phila Pa 1976) Date: 2018-06-15 Impact factor: 3.241
Authors: T W Pike; F Mushtaq; R P Mann; P Chambers; G Hall; J E Tomlinson; R Mir; R M Wilkie; M Mon-Williams; J P A Lodge Journal: Br J Surg Date: 2018-03-20 Impact factor: 6.939