The derivation of an International Classification of Diseases, Tenth Revision-based trauma-related mortality model
Zachary Tran1, Wenhao Zhang, Arjun Verma
1From the Cardiovascular Outcomes Research Laboratories (Z.T., A.V., P.B.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles; Division of Acute Care Surgery, Department of Surgery (Z.T., S.B.), Loma Linda University Medical Center, Loma Linda; Department of Computer Science (W.Z., R.R.), University of California, Los Angeles, California; Department of Surgery (A.C.), University of Texas Health Science Center at Tyler, Tyler, Texas; and Department of Surgery (D.K.), Harbor-UCLA Medical Center, Torrance, California.
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 (R2) = 0.993; logistic regression, R2 = 0.981; ISS, R2 = 0.649; TMPM-ICD10, R2 = 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.
