Evidence map›Paper›PMID 36266136›Full record

ArticleAmerican journal of surgery2023

The predictive probability of mortality in the presence of full-thickness burns.

Kathryn Atkins, Andrew Schneider, Christian Rodriguez, Jared Gallaher, Anthony Charles

Abstract read
In one paragraph

Article in American journal of surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

5 authors.

Kathryn AtkinsDepartment of Surgery, University of North Carolina at Chapel Hill, United States.
Andrew SchneiderDepartment of Surgery, University of North Carolina at Chapel Hill, United States.
Christian RodriguezDepartment of Surgery, University of North Carolina at Chapel Hill, United States.
Jared GallaherDepartment of Surgery, University of North Carolina at Chapel Hill, United States.
Anthony CharlesDepartment of Surgery, University of North Carolina at Chapel Hill, United States. Electronic address: anthchar@med.unc.edu.

Funding

TRAUMA RESEARCH FELLOWSHIPT32GM008450 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Bruce A Cairns, Anthony G Charles · 1992 to 2026
$3.8M
NIGMS NIH HHS T32 GM008450
6 · The paper itself

Abstract

introductionPrognostication of burn injury mortality is challenging. Recent mortality prognostication tools have incorporated the percent of full-thickness surface area (FTSA). We hypothesize that the presence of full-thickness burn injury independently increases in-hospital mortality.

methodsWe performed a retrospective review of the National Trauma Data Bank (NTDB) from 2007 to 2019 of adults (≥16 years old) with burn injuries. Variables evaluated included basic demographics, presence of inhalation injury, percent TBSA, and percent FTSA burned. The primary outcome was in-hospital mortality. We performed modified Poisson regression modeling adjusting for significant variables to estimate the relative mortality risk.

results75,816 patients met inclusion criteria. When controlling for TBSA, the presence of a full-thickness burn had a relative risk of in-hospital mortality of 1.42 (95% CI 1.09-1.85, p = 0.008). The predicted probability of mortality was 100% at 50% FTSA.

conclusionThe presence of full-thickness burns and the proportion of full-thickness burns independently and significantly increased in-hospital mortality. Therefore, clinicians should utilize prognostication models incorporating percent full-thickness burn area to predict mortality more accurately.

Indexed as

BurnsLung InjuryAdolescentAdultHospital MortalityHumansProbabilityRetrospective Studies

Identifiers

PMID36266136
PMCPMC11701862

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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.