Development and internal validation of China mortality prediction model in trauma based on ICD-10-CM lexicon: CMPMIT-ICD10. Issue 5 (5th March 2021) - Wang, Yan-Hua; Wang, Tian-Bing; Zhang, Zi-Xiao; Liu, Hui-Xin; Xu, Ting-Min; Wang, Chu; Jiang, Bao-Guo
Abstract : Supplemental Digital Content is available in the text Abstract: Background: Models to predict mortality in trauma play an important role in outcome prediction and severity adjustment, which informs trauma quality assessment and research. Hospitals in China typically use the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) to describe injury. However, there is no suitable prediction model for China. This study attempts to develop a new mortality prediction model based on the ICD-10-CM lexicon and a Chinese database. Methods: This retrospective study extracted the data of all trauma patients admitted to the Beijing Red Cross Emergency Center, from January 2012 to July 2018 ( n  = 40, 205). We used relevant predictive variables to establish a prediction model following logistic regression analysis. The performance of the model was assessed based on discrimination and calibration. The bootstrapping method was used for internal validation and adjustment of model performance. Results: Sex, age, new region-severity codes, comorbidities, traumatic shock, and coma were finally included in the new model as key predictors of mortality. Among them, coma and traumatic shock had the highest scores in the model. The discrimination and calibration of this model were significant, and the internal validation performance was good. The values of the area under the curve and Brier score for the new model were 0.9640 and 0.0177, respectively; after adjustment of the bootstrapping method, they were 0.9630 and 0.0178, respectively. Conclusions: The new model (China Mortality Prediction Model in Trauma based on the ICD-10-CM lexicon) showed great discrimination and calibration, and performed well in internal validation; it should be further verified externally.