Accuracy of algorithms to predict injury severity in older adults for trauma triage
Creators: name: Hartka, Thomas
name: Gancayco, Christina
name: McMurry, Timothy
name: Robson, Marina
name: Weaver, Ashley
Publisher: name: Taylor & Francis
Year: 2019
Subjects: subject: Biochemistry
subject: Medicine
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: FOS: Earth and related environmental sciences
subject: Immunology
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Science Policy
subject: Plant Biology

description: <b>Objective</b>: Older adults make up a rapidly increasing proportion of motor vehicle occupants and previous studies have demonstrated that this population is more susceptible to traumatic injuries. The CDC recommends that patients anticipated to have severe injuries (Injury Severity Score [ISS] ≥ 16) be transported to a trauma center. The recommended target rate for undertriage is ≤ 5% and for overtriage is ≤ 50%. Several regression-based algorithms for injury prediction have been developed in order to predict severe injury in occupants involved in a motor vehicle collision (MVC). The objective of this study to was to determine if the accuracy of regression-based injury severity prediction algorithms decreases for older adults. <b>Methods</b>: Data were obtained from the National Automotive Sampling System – Crashworthiness Data System (NASS-CDS) from the years 2000–2015. Adult occupants involved in non-rollover MVCs were included. Regression-based injury risk models to predict severe injury (ISS ≥ 16) were developed using random split-samples with the following variables: age, delta-V, direction of impact, belt status, and number of impacts. Separate models were trained using data from the following age groups: (1) all adults, (2) 15–54 years, (3) ≥45 years, (4) ≥55 years, and (5) ≥65 years. The models were compared using the mean receiver operating characteristic area under curve (ROC-AUC) after 1,000 iterations of training and testing. The predicted rates of overtriage were then determined for each group in order to achieve an undertriage rate of 5%. <b>Results</b>: There were 24,577 occupants (6,863,306 weighted) included in this analysis. The injury prediction model trained using data from all adults did not perform as well when tested on older adults (ROC-AUC: 15–54 years: 0.874 [95% CI: [0.851–0.895]; 45+ years: 0.837 [95% CI: 0.802–869]; 55+ years: 0.821 [95% CI: 0.775–0.864]; and 65+ years: 0.813 [95% CI: 0.754–0.866]). The accuracy of this model decreased in each decade of life. The performance did not change significantly when age-specific data were used to train the prediction models (ROC-AUC: 18–54 years: 0.874 [95% CI: 0.851–0.896]; 45+ years: 0.836 [95% CI: 0.798–0.871]; 55+ years: 0.822 [95% CI: 0.779–0.864]; and 65+ years: 0.808 [95% CI: 0.748–0.868]). In order to achieve an undertriage rate of 5%, the predicted overtriage rate by these models were 50% for occupants 15–54 years, 61% for occupants ≥ 55 years, 70% for occupants ≥ 55 years, and 71% for occupants ≥ 65 years. <b>Conclusion</b>: The results of this study indicate that it is more difficult to accurately predict severe injury in older adults involved in MVCs, which has the potential to result in significant overtriage. This decreased accuracy is likely due to variations in fragility in older adults. These findings indicate that special care should be taken when using regression-based prediction models to determine the appropriate hospital destination for older occupants.