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Predictors of mortality for blunt trauma patients in intensive care: A retrospective cohort study
Michael Jennings1,2, James Booker3, Amy Addison4
1General Intensive Care Unit, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK.
Insights
Predictors of mortality in major trauma patients include advanced age, low Glasgow Coma Scale (GCS) scores, and specific injury patterns. The Probability of Survival score (Ps19) emerged as the strongest predictor, highlighting its utility in critical care prognostication.
Area of Science:
- Critical Care Medicine
- Trauma Surgery
- Prognostic Research
Background:
- Major trauma significantly impacts critical care services, leading to high morbidity and mortality across all age groups.
- Identifying factors that predict patient outcomes is crucial for effective prognostication in major trauma cases.
Purpose of the Study:
- To identify key predictors of mortality among adult patients admitted to intensive care following major trauma.
- To evaluate the predictive performance of various established injury and physiological scoring systems.
Main Methods:
- Retrospective analysis of 414 adult trauma patients admitted to general intensive care between January 2018 and December 2019.
- Logistic regression was used to assess the impact of patient demographics, injury patterns, and scores (GCS, CCI, APACHE II, ISS, Ps19) on mortality.
- Mechanism of injury and number of surgeries were also analyzed as potential predictors.
Main Results:
- Overall mortality rate was 18.6%. Non-survivors were older, had higher Injury Severity Scores (ISS), and lower Glasgow Coma Scale (GCS) scores upon admission.
- Independent predictors of mortality included advanced age (70-80 years and >80 years) and GCS < 15.
- The Probability of Survival score (Ps19) demonstrated the highest predictive accuracy (AUROC of 0.90) for mortality.
Conclusions:
- Significant mortality predictors identified were age, fall from <2 meters, head or limb injuries, GCS <15, and Ps19.
- Charlson's Comorbidity Index (CCI) and APACHE II were not significant predictors in this cohort.
- While Ps19 is a strong prognostic tool, a unified scoring system integrating physiological data and injury patterns is needed for improved trauma prognostication.
Background:
Major trauma places substantial demand on critical care services, is a leading cause of death in under 40-year-olds and causes significant morbidity and mortality across all age groups. Various factors influence patient outcome and predefining these could allow prognostication. The aim of this study was to identify predictors of mortality from major trauma in intensive care.
Methods:
This was a retrospective study of adult trauma patients admitted to general intensive care between January 2018 and December 2019. We assessed the impact on mortality of patient demographics, patterns of injury, injury scores (Glasgow Coma Score (GCS), Charlson's comorbidity index (CCI), Acute Physiology and Health Evaluation II (APACHE II), Injury Severity Score (ISS) and Probability of Survival Score (Ps19)), number of surgeries and mechanism of injury using logistic regression.
Results:
A total of 414 patients were included with a median age of 54 years (IQR 34-72). Overall mortality was 18.6%. The most common mechanism of injury was traffic collision (46%). Non-survivors were older, had higher ISS scores with lower GCS on admission and lower probability of survival scores. Factors independently predictive of mortality were age 70-80 (OR 3.267, p = 0.029), age >80 (OR 27.043, p < 0.001) and GCS < 15 (OR 8.728, p < 0.001). Ps19 was the best predictor of mortality (p <0.001 for each score category), with an AUROC of 0.90.
Conclusions:
The significant mortality predictors were age, fall from <2 metres, injury of head or limbs, GCS <15 and Ps19. Contrary to previous studies, CCI and APACHE II did not significantly predict mortality. Although Ps19 was found to be the best current prognostic score, trauma prognostication would benefit from a single validated scoring system incorporating both physiological variables and injury patterns.
