Evidence map›Paper›PMID 29766125›Full record

ArticleTrauma surgery & acute care open2018

Trauma Early Mortality Prediction Tool (TEMPT) for assessing 28-day mortality.

Ryan C Kunitake, Lucy Z Kornblith, Mitchell Jay Cohen, Rachael A Callcut

Registry-linked trialAbstract read
In one paragraph

Article in Trauma surgery & acute care open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04188938 (Occult Pneumothorax in Patients With Blunt or Penetrating Trauma), which is not on this map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04188938 completednot on this map

Occult Pneumothorax in Patients With Blunt or Penetrating Trauma: A Descriptive, Cross-sectional Study

Typeobservational_patient_registrySponsorEuropean University of LefkeRan2008 to 2011Enrolled105ConditionsPneumothorax, Acute, Trauma ChestArmsEvaluation of occult pneumothorax in who had a blunt or penetrating chest trauma
3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Review
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  3. Review
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  10. Review
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  15. Article
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  17. Article
  18. Article
  19. Predicting mortality in severe polytrauma with limited resources.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2022
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ryan C KunitakeDepartment of Surgery, University of California, San Francisco, California, USA.
Lucy Z KornblithDepartment of Surgery, University of California, San Francisco, California, USA.
Mitchell Jay CohenDepartment of Surgery, Denver Health Medical Center, Denver, Colorado, USA.
Rachael A CallcutDepartment of Surgery, University of California, San Francisco, California, USA.

Funding

Advancing Outcome Metrics in Trauma Surgery Through Utilization of Big DataK01ES026834 · NIEHS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CALLCUT, RACHAEL A · 2015 to 2018
$889k
Clinical and Transtional Science InstituteTL1TR000144 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRANDIS, JENNIFER RUBIN · 2012 to 2015
$643k
NCATS NIH HHS TL1 TR000144NIEHS NIH HHS K01 ES026834
6 · The paper itself

Abstract

backgroundPrior mortality prediction models have incorporated severity of anatomic injury quantified by Abbreviated Injury Severity Score (AIS). Using a prospective cohort, a new score independent of AIS was developed using clinical and laboratory markers present on emergency department presentation to predict 28-day mortality.

methodsAll patients (n=1427) enrolled in an ongoing prospective cohort study were included. Demographic, laboratory, and clinical data were recorded on admission. True random number generator technique divided the cohort into derivation (n=707) and validation groups (n=720). Using Youden indices, threshold values were selected for each potential predictor in the derivation cohort. Logistic regression was used to identify independent predictors. Significant variables were equally weighted to create a new mortality prediction score, the Trauma Early Mortality Prediction Tool (TEMPT) score. Area under the curve (AUC) was tested in the validation group. Pairwise comparison of Trauma Injury Severity Score (TRISS), Revised Trauma Score, Glasgow Coma Scale, and Injury Severity Score were tested against the TEMPT score.

resultsThere was no difference between baseline characteristics between derivation and validation groups. In multiple logistic regression, a model with presence of traumatic brain injury, increased age, elevated systolic blood pressure, decreased base excess, prolonged partial thromboplastin time, increased international normalized ratio (INR), and decreased temperature accurately predicted mortality at 28 days (AUC 0.93, 95% CI 0.90 to 0.96, P<0.001). In the validation cohort, this score, termed TEMPT, predicted 28-day mortality with an AUC 0.94 (95% CI 0.92 to 0.97). The TEMPT score preformed similarly to the revised TRISS score for severely injured patients and was highly predictive in those having mild to moderate injury. DISCUSSION: TEMPT is a simple AIS-independent mortality prediction tool applicable very early following injury. TEMPT provides an AIS-independent score that could be used for early identification of those at risk of doing poorly following even minor injury. LEVEL OF EVIDENCE: Level II.

Indexed as

mortality predictionTEMPTtrauma mortalityTRISS

Identifiers

PMID29766125
PMCPMC5887834

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.