Evidence map›Paper›PMID 39390393›Full record

ArticleInternational journal of emergency medicine2024

Impact of advanced trauma life support training on 30-day mortality in severely injured patients at a Kenyan tertiary center: a retrospective matched case-control study.

Charbel Kamau, Idris Chikophe, Abdulkarim Abdallah, Edwin Mogere

Abstract read
In one paragraph

Article in International journal of emergency medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Effect of trauma life support training on patient outcomes: a systematic review and meta-analysis.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026
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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.

Charbel KamauDepartment of Surgery, Aga Khan University Hospital, Nairobi, Kenya.
Idris ChikopheDepartment of Anesthesia, Aga Khan University Hospital, Nairobi, Kenya.
Abdulkarim AbdallahDepartment of Surgery, Aga Khan University Hospital, Nairobi, Kenya.
Edwin MogereDepartment of Surgery, Aga Khan University Hospital, Nairobi, Kenya. edwin.mogere@aku.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTrauma is a leading cause of mortality worldwide, particularly in low and middle-income countries (LMICs) like Kenya. This study evaluates the impact of Advanced Trauma Life Support (ATLS) training on 30-day mortality outcomes in severely injured patients at a tertiary care center in Kenya.

methodsA retrospective matched case-control study was conducted at Aga Khan University Hospital, Nairobi. The study included adult patients (≥ 18 years) with polytrauma (Injury Severity Score [ISS] > 15) from 2011 to 2022. Propensity score matching was used to pair 81 post-ATLS cases with 81 pre-ATLS controls based on age and ISS. Data analysis was performed using R Statistical language (version 4.3.0).

resultsThe 30-day mortality rate decreased significantly from 17% (95% CI: 9.4-27.4%) pre-ATLS to 6% (95% CI: 2.0-13.5%) post-ATLS (p = 0.028). No significant differences were found in baseline characteristics between the two groups. Road traffic accidents were the primary cause of trauma in both groups (72% pre-ATLS vs. 78% post-ATLS).

conclusionATLS training significantly reduced 30-day mortality in severely injured patients, demonstrating its effectiveness even in resource-limited settings. Further prospective randomized studies are recommended to confirm these findings and evaluate long-term outcomes.

Identifiers

PMID39390393
PMCPMC11466019

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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.