Development and Validation of a Model to Quantify Injury Severity in Real Time.

Importance: Quantifying injury severity is integral to trauma care benchmarking, decision-making, and research, yet the most prevalent metric to quantify injury severity-Injury Severity Score (ISS)- is impractical to use in real time. Objective: To develop and validate a practical model that uses a limited number of injury patterns to quantify injury severity in real time through 3 intuitive outcomes. Design, Setting, and Participants: In this cohort study for prediction model development and validation, training, development, and internal validation cohorts comprised 223 545, 74 514, and 74 514 admission encounters, respectively, of adults (age ≥18 years) with a primary diagnosis of traumatic injury hospitalized more than 2 days (2017-2018 National Inpatient Sample). The external validation cohort comprised 3855 adults admitted to a level I trauma center who met criteria for the 2 highest of the institution's 3 trauma activation levels. Main Outcomes and Measures: Three outcomes were hospital length of stay, probability of discharge disposition to a facility, and probability of inpatient mortality. The prediction performance metric for length of stay was mean absolute error. Prediction performance metrics for discharge disposition and inpatient mortality were average precision, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUROC). Calibration was evaluated using calibration plots. Shapley addictive explanations analysis and bee swarm plots facilitated model explainability analysis. Results: The Length of Stay, Disposition, Mortality (LDM) Injury Index (the model) comprised a multitask deep learning model trained, developed, and internally validated on a data set of 372 573 traumatic injury encounters (mean [SD] age = 68.7 [19.3] years, 56.6% female). The model used 176 potential injuries to output 3 interpretable outcomes: the predicted hospital length of stay, probability of discharge to a facility, and probability of inpatient mortality. For the external validation set, the ISS predicted length of stay with mean absolute error was 4.16 (95% CI, 4.13-4.20) days. Compared with the ISS, the model had comparable external validation set discrimination performance (facility discharge AUROC: 0.67 [95% CI, 0.67-0.68] vs 0.65 [95% CI, 0.65-0.66]; recall: 0.59 [95% CI, 0.58-0.61] vs 0.59 [95% CI, 0.58-0.60]; specificity: 0.66 [95% CI, 0.66-0.66] vs 0.62 [95%CI, 0.60-0.63]; mortality AUROC: 0.83 [95% CI, 0.81-0.84] vs 0.82 [95% CI, 0.82-0.82]; recall: 0.74 [95% CI, 0.72-0.77] vs 0.75 [95% CI, 0.75-0.76]; specificity: 0.81 [95% CI, 0.81-0.81] vs 0.76 [95% CI, 0.75-0.77]). The model had excellent calibration for predicting facility discharge disposition, but overestimated inpatient mortality. Explainability analysis found the inputs influencing model predictions matched intuition. Conclusions and Relevance: In this cohort study using a limited number of injury patterns, the model quantified injury severity using 3 intuitive outcomes. Further study is required to evaluate the model at scale.

TraumaICDBERT, A Natural Language Processing Algorithm to Extract Injury ICD-10 Diagnosis Code from Free Text.

OBJECTIVE: To develop and validate TraumaICDBERT, a natural language processing algorithm to predict injury ICD-10 diagnosis codes from trauma tertiary survey notes. SUMMARY BACKGROUND DATA: The adoption of ICD-10 diagnosis codes in clinical settings for injury prediction is hindered by the lack of real-time availability. Existing natural language processing algorithms have limitations in accurately predicting injury ICD-10 diagnosis codes. METHODS: Trauma tertiary survey notes from hospital encounters of adults between January 2016 and June 2021 were used to develop and validate TraumaICDBERT, an algorithm based on BioLinkBERT. The performance of TraumaICDBERT was compared to Amazon Web Services Comprehend Medical, an existing natural language processing tool. RESULTS: A dataset of 3,478 tertiary survey notes with 15,762 4-character injury ICD-10 diagnosis codes was analyzed. TraumaICDBERT outperformed Amazon Web Services Comprehend Medical across all evaluated metrics. On average, each tertiary survey note was associated with 3.8 (standard deviation: 2.9) trauma registrar-extracted 4-character injury ICD-10 diagnosis codes. CONCLUSIONS: TraumaICDBERT demonstrates promising initial performance in predicting injury ICD-10 diagnosis codes from trauma tertiary survey notes, potentially facilitating the adoption of downstream prediction tools in clinical settings.

Rib Fracture Frailty Index: A risk stratification tool for geriatric patients with multiple rib fractures.

BACKGROUND: Rib fractures are consequential injuries for geriatric patients (age, ≥65 years). Although age and injury patterns drive many rib fracture management decisions, the impact of frailty-which baseline conditions affect rib fracture-specific outcomes-remains unclear for geriatric patients. We aimed to develop and validate the Rib Fracture Frailty (RFF) Index, a practical risk stratification tool specific for geriatric patients with rib fractures. We hypothesized that a compact list of frailty markers can accurately risk stratify clinical outcomes after rib fractures. METHODS: We queried nationwide US admission encounters of geriatric patients admitted with multiple rib fractures from 2016 to 2017. Partitioning around medoids clustering identified a development subcohort with previously validated frailty characteristics. Ridge regression with penalty for multicollinearity aggregated baseline conditions most prevalent in this frail subcohort into RFF scores. Regression models with adjustment for injury severity, sex, and age assessed associations between frailty risk categories (low, medium, and high) and inpatient outcomes among validation cohorts (odds ratio [95% confidence interval]). We report results according to Transparent Reporting of Multivariable Prediction Model for Individual Prognosis guidelines. RESULTS: Development cohort (n = 55,540) cluster analysis delineated 13 baseline conditions constituting the RFF Index. Among external validation cohort (n = 77,710), increasing frailty risk (low [reference group], moderate, high) was associated with stepwise worsening adjusted odds of mortality (1.5 [1.2-1.7], 3.5 [3.0-4.0]), intubation (2.4 [1.5-3.9], 4.7 [3.1-7.5]), hospitalization ≥5 days (1.4 [1.3-1.5], 1.8 [1.7-2.0]), and disposition to home (0.6 [0.5-0.6], 0.4 [0.3-0.4]). Locally weighted scatterplot smoothing showed correlations between increasing RFF scores and worse outcomes. CONCLUSION: The RFF Index is a practical frailty risk stratification tool for geriatric patients with multiple rib fractures. The mobile app we developed may facilitate rapid implementation and further validation of RFF Index at the bedside. LEVEL OF EVIDENCE: Prognostic study, level III.