Evidence map›Paper›PMID 39399278›Full record

ArticleCureus2024

Predicting Mortality in Trauma Research: Evaluating the Performance of Trauma Scoring Tools in a South African Population.

Christopher E Collora, Mengli Xiao, Bailey Fosdick, Hendrick J Lategan, Julia Finn, Steven G Schauer, Julia Dixon, Smitha Bhaumik, Willem Stassen, Shaheem de Vries and 2 more

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Observational
  3. Article
  4. Observational
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

12 authors.

Christopher E ColloraDepartment of Emergency Medicine, University of Colorado School of Medicine, Aurora, USA.
Mengli XiaoBiostatistics and Informatics, Colorado School of Public Health, Aurora, USA.
Bailey FosdickBiostatistics and Informatics, Colorado School of Public Health, Aurora, USA.
Hendrick J LateganDivision of Surgery, Department of Surgical Sciences, Stellenbosch University, Cape Town, ZAF.
Julia FinnDepartment of Emergency Medicine, University of Colorado School of Medicine, Aurora, USA.
Steven G SchauerDepartment of Anesthesiology, University of Colorado School of Medicine, Aurora, USA.
Julia DixonDepartment of Emergency Medicine, University of Colorado School of Medicine, Aurora, USA.
Smitha BhaumikDepartment of Emergency Medicine, University of Colorado School of Medicine, Aurora, USA.
Willem StassenDivision of Emergency Medicine, Department of Family, Community and Emergency Care, University of Cape Town, Cape Town, ZAF.
Shaheem de VriesEmergency Medicine, Collaborative for Emergency Care in Africa, Cape Town, ZAF.
Craig WylieEmergency Medical Services, Western Cape Government Health and Wellness, Cape Town, ZAF.
Nee-Kofi Mould-MillmanDepartment of Emergency Medicine, University of Colorado School of Medicine, Aurora, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Trauma is a leading cause of death and disability in low-resource settings, yet trauma severity scores are seldom validated in these contexts. There is a pressing need to better characterize and compare trauma scoring tools, especially within research frameworks. This study aimed to evaluate the performance of various trauma scoring tools in predicting in-hospital mortality among trauma patients in South Africa. Methods This study conducted a secondary analysis of existing data from the multicenter Epidemiology and Outcomes of Prolonged Trauma Care (EpiC) study, which included 13,548 adult trauma patients aged 18 years and older, collected between August 2021 and March 2024. The predictive ability of the scoring tools was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Results The mortality rate was 2.5% (n = 298). The Kampala Trauma Score (KTS) demonstrated the highest predictive ability for seven-day in-hospital mortality, with an AUROC of 0.95 and an AUPRC of 0.53. Similarly, the Trauma and Injury Severity Score (TRISS) and the New Injury Severity Score (NISS) also exhibited strong predictive capabilities, with AUROC values of 0.96 and AUPRC values of 0.62 for TRISS and an AUROC of 0.96 and AUPRC of 0.53 for NISS. In contrast, the Revised Trauma Score and Mechanism, Glasgow Coma Scale, Age, and Arterial Pressure (MGAP) showed lower predictive performance, with AUROC values of 0.87 (AUPRC = 0.51) and 0.86 (AUPRC = 0.47), respectively. Conclusions The KTS exhibited optimal performance characteristics for retrospectively predicting mortality in our cohort, outperforming other scoring tools. Notably, it is also the simplest scoring tool, featuring the fewest variables compared to other trauma severity assessments. These findings highlight the necessity for external validation of trauma scoring tools in resource-limited populations to ensure their applicability and effectiveness in trauma research across diverse healthcare settings.

Indexed as

predicting deathssouth africatraumatrauma mortality predictiontrauma scoring toolstrauma severity scoring

Identifiers

PMID39399278
PMCPMC11469657

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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.