ArticleJournal of emergencies, trauma, and shock
Beyond Mortality: Does Trauma-related Injury Severity Score Predict Complications or Lengths of Stay Using a Large Administrative Dataset.
Article in Journal of emergencies, trauma, and shock. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Article
- Correlating Various Clinical Outcomes Associated with Motor Vehicle Collision-Related Trauma.Healthcare (Basel, Switzerland) · 2025Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDespite its shortcomings, trauma-related injury severity score (TRISS) correlates well with mortality in large trauma datasets. The aim of this study was to determine if TRISS correlates with morbidity and hospital lengths of stay using data from an institutional registry at a Level I Trauma Center. We hypothesized that higher TRISS correlates with increased complications and longer hospital stays.
methodsA retrospective review of our institutional registry was performed, examining all trauma admissions between January 1999 and June 30, 2015. Out of a total of 32,026 patient records, TRISS data were available in 23,205 cases. Abstracted data included patient age, gender, ISS, TRISS, presence of complication, Glasgow Coma Scale (GCS), hospital length of stay, intensive care unit LOS, step-down unit LOS, functional independence measure, and 30-day mortality.
resultsTRISS was highly predictive of mortality, with the AUC value of 0.95 (95% confidence interval 0.936-0.954,
conclusionsDespite previous criticisms, we found that TRISS is superior to ISS for mortality and morbidity prediction. TRISS correlated significantly with a hospital, step down, and ICU lengths of stay using a large administrative dataset.
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