The derivation of an International Classification of Diseases, Tenth Revision–based trauma-related mortality model using machine learning. Issue 3 (20th March 2022)

• Record Type: Journal Article
• Authors: Tran, Zachary; Zhang, Wenhao; Verma, Arjun; Cook, Alan; Kim, Dennis; Burruss, Sigrid; Ramezani, Ramin; Benharash, Peyman
• Is Part Of: Journal of trauma and acute care surgery. Volume 92:Issue 3(2022)
• Page Start: 561
• Page End: 566
• Publication Date: 2022-03-20
• Subjects: eXtreme Gradient Boosting -- ICD-10 -- mortality prediction -- Injury Severity Score -- Trauma Mortality Prediction Model; Surgical intensive care -- Periodicals; Surgical emergencies -- Periodicals; Wounds and injuries -- Surgery -- Periodicals; 617.026
• DOI: 10.1097/TA.0000000000003416
• Languages: English
• ISSNs: 2163-0755

Abstract

Abstract : Supplemental digital content is available in the text. Abstract : BACKGROUND: Existing mortality prediction models have attempted to quantify injury burden following trauma-related admissions with the most notable being the Injury Severity Score (ISS). Although easy to calculate, it requires additional administrative coding. International Classification of Diseases ( ICD )–based models such as the Trauma Mortality Prediction Model (TMPM-ICD10) circumvent these limitations, but they use linear modeling, which may not adequately capture the intricate relationships of injuries on mortality. Using ICD-10 coding and machine learning (ML) algorithms, the present study used the National Trauma Data Bank to develop mortality prediction models whose performance was compared with logistic regression, ISS, and TMPM-ICD10. METHODS: The 2015 to 2017 National Trauma Data Bank was used to identify adults following trauma-related admissions. Of 8, 021 ICD-10 codes, injuries were categorized into 1, 495 unique variables. The primary outcome was in-hospital mortality. eXtreme Gradient Boosting (XGBoost), a ML technique that uses iterations of decision trees, was used to develop mortality models. Model discrimination was compared with logistic regression, ISS, and TMPM-ICD10 using receiver operating characteristic curve and probabilistic accuracy with calibration curves. RESULTS: Of 1, 611, 063 patients, 54, 870 (3.41%) experienced in-hospital mortality. Compared with those who survived, those who died more frequently suffered from penetrating trauma and had a greater number of injuries. The XGBoost model exhibited superior receiver operating characteristic curve (0.863 [95% confidence interval (CI), 0.862–0.864]) compared with logistic regression (0.845 [95% CI, 0.844–0.846]), ISS (0.828 [95% CI, 0.827–0.829]), and TMPM-ICD10 (0.861 [95% CI, 0.860–0.862]) (all p < 0.001). Importantly, the ML model also had significantly improved calibration compared with other methodologies (XGBoost, coefficient of determination ( R 2 ) = 0.993; logistic regression, R 2 = 0.981; ISS, R 2 = 0.649; TMPM-ICD10, R 2 = 0.830). CONCLUSION: Machine learning models using XGBoost demonstrated superior performance and calibration compared with logistic regression, ISS, and TMPM-ICD10. Such approaches in quantifying injury severity may improve its utility in mortality prognostication, quality improvement, and trauma research. LEVEL OF EVIDENCE: Prognostic and Epidemiologic; level III.