Enhancing geriatric trauma mortality prediction: Modifying and assessing the Geriatric Trauma Outcome Score with net benefit and decision curve analysis.

Abstract

Objective: Calibration and discrimination indicators alone are insufficient for evaluating the clinical usefulness of prediction models, as they do not account for the cost of misclassification errors. This study aimed to modify the Geriatric Trauma Outcome Score (GTOS) and assess the clinical utility of the modified model using net benefit (NB) and decision curve analysis (DCA) for predicting in-hospital mortality.

Methods: The Trauma Quality Improvement Program (TQIP) 2017 was used to identify geriatric trauma patients (≥ 65 years) treated at Level I trauma centers. The outcome of interest was in-hospital mortality. The GTOS was modified to include additional patient, injury, and treatment characteristics identified through machine learning methods, focusing on early risk stratification. Calibration and discrimination indicators, along with NB and DCA, were utilized for evaluation.

Results: Of the 67,222 admitted geriatric trauma patients, 5.6% died in the hospital. The modified GTOS score included the following variables with associated weights: initial airway intervention (5), Glasgow Coma Scale ≤13 (5), packed red blood cell transfusion within 24 h (3), penetrating injury (2), age ≥ 75 years (2), preexisting comorbidity (1), and torso injury (1), with a total range from 0 to 19. The modified GTOS demonstrated a significantly higher area under the curve (0.92 vs. 0.84, p < 0.0001), lower misclassification error (4.9% vs. 5.2%), and lower Brier score (0.036 vs. 0.042) compared to the original GTOS. DCA showed that using the modified GTOS for predicting in-hospital mortality resulted in higher NB than treating all, treating none, and treating based on the original GTOS across a wide range of clinician preferences.

Conclusions: The modified GTOS model exhibited superior predictive ability and clinical utility compared to the original GTOS. NB and DCA offer valuable complementary methods to calibration and discrimination indicators, comprehensively evaluating the clinical usefulness of prediction models and decision strategies.