Alexandria M Hertz1, Nicholas M Hertz2, Niels V Johnsen3. 1. Madigan Army Medical Center, 9040 Jackson Ave, Tacoma, WA 98431, USA. Electronic address: Alexandria.m.hertz.mil@mail.mil. 2. Madigan Army Medical Center, 9040 Jackson Ave, Tacoma, WA 98431, USA; Vanderbilt University Medical Center, USA. 3. Vanderbilt University Medical Center, USA. Electronic address: niels.v.johnsen@vumc.org.
Abstract
INTRODUCTION: Bladder rupture following blunt pelvic trauma is rare though can have significant sequelae. We sought to determine whether machine learning could help predict the presence of bladder injury using certain factors at the time of presentation of blunt pelvic trauma. MATERIALS AND METHODS: Adult patients at a Level I trauma center with blunt trauma pelvic fractures from January 1, 2005 to December 31, 2017 were identified. Patients with admission urinalysis data, fracture ICD 9 codes, and mechanism of injury available in the trauma registry were included. Patients with bladder rupture and pelvic fracture were compared to those with pelvic fracture alone. The classification of results was performed using the MATLAB Classification Learner Tool. The classification performances were tested by machine learning algorithms in the domains of Decision Tree, Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), Nearest Neighbor (KNN), and Ensemble classifiers. RESULTS: Of the 3063 eligible pelvic fracture patients identified, 208 (6.8%) had concomitant bladder ruptures. Twenty machine learning algorithms were then tested based on pelvic fracture ICD-9 code, admission urinalysis, and mechanism of injury. The best classification results were obtained using the Gaussian Naïve Bayes and Kernel Naïve Bayes classifiers, both with accuracy of 97.8%, specificity of 99%, sensitivity of 83%, and area under the curve (AUC) of the ROC curve of 0.99. CONCLUSION: Machine learning algorithms can be used to help predict with a high level of accuracy the presence of bladder rupture with blunt pelvic trauma using readily available information at the time of presentation. This has the potential to improve selection of patients for additional imaging, while also more appropriately allocating hospital resources and reducing patient risks. Published by Elsevier Ltd.
INTRODUCTION:Bladder rupture following blunt pelvic trauma is rare though can have significant sequelae. We sought to determine whether machine learning could help predict the presence of bladder injury using certain factors at the time of presentation of blunt pelvic trauma. MATERIALS AND METHODS: Adult patients at a Level I trauma center with blunt trauma pelvic fractures from January 1, 2005 to December 31, 2017 were identified. Patients with admission urinalysis data, fracture ICD 9 codes, and mechanism of injury available in the trauma registry were included. Patients with bladder rupture and pelvic fracture were compared to those with pelvic fracture alone. The classification of results was performed using the MATLAB Classification Learner Tool. The classification performances were tested by machine learning algorithms in the domains of Decision Tree, Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), Nearest Neighbor (KNN), and Ensemble classifiers. RESULTS: Of the 3063 eligible pelvic fracturepatients identified, 208 (6.8%) had concomitant bladder ruptures. Twenty machine learning algorithms were then tested based on pelvic fracture ICD-9 code, admission urinalysis, and mechanism of injury. The best classification results were obtained using the Gaussian Naïve Bayes and Kernel Naïve Bayes classifiers, both with accuracy of 97.8%, specificity of 99%, sensitivity of 83%, and area under the curve (AUC) of the ROC curve of 0.99. CONCLUSION: Machine learning algorithms can be used to help predict with a high level of accuracy the presence of bladder rupture with blunt pelvic trauma using readily available information at the time of presentation. This has the potential to improve selection of patients for additional imaging, while also more appropriately allocating hospital resources and reducing patient risks. Published by Elsevier Ltd.
Authors: Gilbert Georg Klamminger; Jean-Jacques Gérardy; Finn Jelke; Giulia Mirizzi; Rédouane Slimani; Karoline Klein; Andreas Husch; Frank Hertel; Michel Mittelbronn; Felix B Kleine-Borgmann Journal: Neurooncol Adv Date: 2021-06-18