Estimating geriatric mortality after injury using age, injury severity, and performance of a transfusion

The geriatric trauma outcome score

Frank Z. Zhao, Steven Wolf, Paul A. Nakonezny, Abu Minhajuddin, Ramona L. Rhodes, M. Elizabeth Paulk, Herb A. Phelan

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

Background: A tool to determine the probability of mortality for severely injured geriatric patients is needed. Objective: We sought to create an easily calculated geriatric trauma prognostic score based on parameters available at the bedside to aid in mortality probability determination. Methods: All patients ≥65 years of age were identified from our Level I trauma center's registry between January 1, 2000 and December 31, 2013. Measurements included age, Injury Severity score (ISS), units of packed red blood cells (PRBCs) transfused in the first 24 hours, and patients' mortality status at the end of their index hospitalization. As a first step, a logistic regression model with maximum likelihood estimation and robust standard errors was used to estimate the odds of mortality from age, ISS, and PRBCs after dichotomizing PRBCs as yes/no. We then constructed a Geriatric Trauma Outcome (GTO) score that became the sole predictor in the re-specified logistic regression model. Results: The sample (n=3841) mean age was 76.5±8.1 years and the mean ISS was 12.4±9.8. In-hospital mortality was 10.8%, and 11.9% received a transfusion by 24 hours. Based on the logistic regression model, the equation with the highest discriminatory ability to estimate probability of mortality was GTO Score=age+(2.5×ISS)+22 (if given PRBCs). The area under the receiver operating characteristic curve (AUC) for this model was 0.82. Selected GTO scores and their related probability of dying were: 205=75%, 233=90%, 252=95%, 310=99%. The range of GTO scores was 67.5 (survivor) to 275.1 (died). Conclusion: The GTO model accurately estimates the probability of dying, and can be calculated at bedside by those possessing a working knowledge of ISS calculation.

Original languageEnglish (US)
Pages (from-to)677-681
Number of pages5
JournalJournal of Palliative Medicine
Volume18
Issue number8
DOIs
StatePublished - Aug 1 2015
Externally publishedYes

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Geriatrics
Injury Severity Score
Logistic Models
Mortality
Wounds and Injuries
Erythrocytes
Trauma Centers
Hospital Mortality
ROC Curve
Area Under Curve
Survivors
Registries
Hospitalization

ASJC Scopus subject areas

  • Nursing(all)
  • Medicine(all)
  • Anesthesiology and Pain Medicine

Cite this

Estimating geriatric mortality after injury using age, injury severity, and performance of a transfusion : The geriatric trauma outcome score. / Zhao, Frank Z.; Wolf, Steven; Nakonezny, Paul A.; Minhajuddin, Abu; Rhodes, Ramona L.; Paulk, M. Elizabeth; Phelan, Herb A.

In: Journal of Palliative Medicine, Vol. 18, No. 8, 01.08.2015, p. 677-681.

Research output: Contribution to journalArticle

Zhao, Frank Z. ; Wolf, Steven ; Nakonezny, Paul A. ; Minhajuddin, Abu ; Rhodes, Ramona L. ; Paulk, M. Elizabeth ; Phelan, Herb A. / Estimating geriatric mortality after injury using age, injury severity, and performance of a transfusion : The geriatric trauma outcome score. In: Journal of Palliative Medicine. 2015 ; Vol. 18, No. 8. pp. 677-681.
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abstract = "Background: A tool to determine the probability of mortality for severely injured geriatric patients is needed. Objective: We sought to create an easily calculated geriatric trauma prognostic score based on parameters available at the bedside to aid in mortality probability determination. Methods: All patients ≥65 years of age were identified from our Level I trauma center's registry between January 1, 2000 and December 31, 2013. Measurements included age, Injury Severity score (ISS), units of packed red blood cells (PRBCs) transfused in the first 24 hours, and patients' mortality status at the end of their index hospitalization. As a first step, a logistic regression model with maximum likelihood estimation and robust standard errors was used to estimate the odds of mortality from age, ISS, and PRBCs after dichotomizing PRBCs as yes/no. We then constructed a Geriatric Trauma Outcome (GTO) score that became the sole predictor in the re-specified logistic regression model. Results: The sample (n=3841) mean age was 76.5±8.1 years and the mean ISS was 12.4±9.8. In-hospital mortality was 10.8{\%}, and 11.9{\%} received a transfusion by 24 hours. Based on the logistic regression model, the equation with the highest discriminatory ability to estimate probability of mortality was GTO Score=age+(2.5×ISS)+22 (if given PRBCs). The area under the receiver operating characteristic curve (AUC) for this model was 0.82. Selected GTO scores and their related probability of dying were: 205=75{\%}, 233=90{\%}, 252=95{\%}, 310=99{\%}. The range of GTO scores was 67.5 (survivor) to 275.1 (died). Conclusion: The GTO model accurately estimates the probability of dying, and can be calculated at bedside by those possessing a working knowledge of ISS calculation.",
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AB - Background: A tool to determine the probability of mortality for severely injured geriatric patients is needed. Objective: We sought to create an easily calculated geriatric trauma prognostic score based on parameters available at the bedside to aid in mortality probability determination. Methods: All patients ≥65 years of age were identified from our Level I trauma center's registry between January 1, 2000 and December 31, 2013. Measurements included age, Injury Severity score (ISS), units of packed red blood cells (PRBCs) transfused in the first 24 hours, and patients' mortality status at the end of their index hospitalization. As a first step, a logistic regression model with maximum likelihood estimation and robust standard errors was used to estimate the odds of mortality from age, ISS, and PRBCs after dichotomizing PRBCs as yes/no. We then constructed a Geriatric Trauma Outcome (GTO) score that became the sole predictor in the re-specified logistic regression model. Results: The sample (n=3841) mean age was 76.5±8.1 years and the mean ISS was 12.4±9.8. In-hospital mortality was 10.8%, and 11.9% received a transfusion by 24 hours. Based on the logistic regression model, the equation with the highest discriminatory ability to estimate probability of mortality was GTO Score=age+(2.5×ISS)+22 (if given PRBCs). The area under the receiver operating characteristic curve (AUC) for this model was 0.82. Selected GTO scores and their related probability of dying were: 205=75%, 233=90%, 252=95%, 310=99%. The range of GTO scores was 67.5 (survivor) to 275.1 (died). Conclusion: The GTO model accurately estimates the probability of dying, and can be calculated at bedside by those possessing a working knowledge of ISS calculation.

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