Corinne Bunn1, Sujay Kulshrestha1, Jason Boyda2, Neelam Balasubramanian2, Steven Birch2, Ibrahim Karabayir3, Marshall Baker4, Fred Luchette4, François Modave5, Oguz Akbilgic6. 1. Department of Surgery, Loyola University Medical Center, Maywood, IL; Burn Shock Trauma Research Institute, Loyola University Chicago, Maywood, IL. 2. Informatics and Systems Development, Health Sciences Division, Loyola University Chicago, Maywood IL. 3. Center for Health Outcomes and Informatics Research, Health Sciences Division, Loyola University Chicago, Maywood, IL; Department of Health Informatics and Data Science, Loyola University Chicago, Chicago, IL; Kirklareli University, Kirklareli, Turkey. 4. Department of Surgery, Loyola University Medical Center, Maywood, IL; Edward Hines, Jr Veterans Administration Hospital, Hines, IL. 5. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL. 6. Center for Health Outcomes and Informatics Research, Health Sciences Division, Loyola University Chicago, Maywood, IL; Department of Health Informatics and Data Science, Loyola University Chicago, Chicago, IL. Electronic address: oakbilgic@luc.edu.
Abstract
BACKGROUND: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these patients. METHODS: The National Surgery Quality Improvement Program database was used to identify patients undergoing appendectomy between 2005 and 2017. Logistic regression, support vector machines, random forest decision trees, and extreme gradient boosting machines were used to model the occurrence of postoperative sepsis. RESULTS: In the study, 223,214 appendectomies were identified; 2,143 (0.96%) were indicated as having postoperative sepsis. Logistic regression (area under the curve 0.70; 95% confidence interval, 0.68-0.73), random forest decision trees (area under the curve 0.70; 95% confidence interval, 0.68-0.73), and extreme gradient boosting (area under the curve 0.70; 95% confidence interval, 0.68-0.73) afforded similar performance, while support vector machines (area under the curve 0.51; 95% confidence interval, 0.50-0.52) had worse performance. Variable importance analyses identified preoperative congestive heart failure, transfusion, and acute renal failure as predictors of postoperative sepsis. CONCLUSION: Machine learning methods can be used to predict the development of sepsis after appendectomy with moderate accuracy. Such predictive modeling has potential to ultimately allow for preoperative recognition of patients at risk for developing postoperative sepsis after appendectomy thus facilitating early intervention and reducing morbidity.
BACKGROUND: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these patients. METHODS: The National Surgery Quality Improvement Program database was used to identify patients undergoing appendectomy between 2005 and 2017. Logistic regression, support vector machines, random forest decision trees, and extreme gradient boosting machines were used to model the occurrence of postoperative sepsis. RESULTS: In the study, 223,214 appendectomies were identified; 2,143 (0.96%) were indicated as having postoperative sepsis. Logistic regression (area under the curve 0.70; 95% confidence interval, 0.68-0.73), random forest decision trees (area under the curve 0.70; 95% confidence interval, 0.68-0.73), and extreme gradient boosting (area under the curve 0.70; 95% confidence interval, 0.68-0.73) afforded similar performance, while support vector machines (area under the curve 0.51; 95% confidence interval, 0.50-0.52) had worse performance. Variable importance analyses identified preoperative congestive heart failure, transfusion, and acute renal failure as predictors of postoperative sepsis. CONCLUSION: Machine learning methods can be used to predict the development of sepsis after appendectomy with moderate accuracy. Such predictive modeling has potential to ultimately allow for preoperative recognition of patients at risk for developing postoperative sepsis after appendectomy thus facilitating early intervention and reducing morbidity.
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