Evidence map›Paper›PMID 31748888›Full record

ArticleWorld journal of surgery2020

Advanced Modeling to Predict Pneumonia in Combat Trauma Patients.

Matthew Bradley, Christopher Dente, Vivek Khatri, Seth Schobel, Felipe Lisboa, Audrey Shi, Hannah Hensman, Allan Kirk, Timothy G Buchman, Eric Elster

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In one paragraph

Article in World journal of surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Trial
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Matthew BradleyDepartment of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD, 20814, USA.
Christopher DenteEmory University, Atlanta, GA, USA.
Vivek KhatriDepartment of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD, 20814, USA.
Seth SchobelDepartment of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD, 20814, USA.
Felipe LisboaDepartment of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD, 20814, USA.
Audrey ShiSurgical Critical Care Initiative (SC2i), Bethesda, MD, USA.
Hannah HensmanSurgical Critical Care Initiative (SC2i), Bethesda, MD, USA.
Allan KirkDuke University, Durham, NC, USA.
Timothy G BuchmanEmory University, Atlanta, GA, USA.
Eric ElsterDepartment of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD, 20814, USA. eric.elster@usuhs.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTools to assist clinicians in predicting pneumonia could lead to a significant decline in morbidity. Therefore, we sought to develop a model in combat trauma patients for identifying those at highest risk of pneumonia.

methodsThis was a retrospective study of 73 primarily blast-injured casualties with combat extremity wounds. Binary classification models for pneumonia prediction were developed with measurements of injury severity from the Abbreviated Injury Scale (AIS), transfusion blood products received before arrival at Walter Reed National Military Medical Center (WRNMMC), and serum protein levels. Predictive models were generated with leave-one-out-cross-validation using the variable selection method of backward elimination (BE) and the machine learning algorithms of random forests (RF) and logistic regression (LR). BE was attempted with two predictor sets: (1) all variables and (2) serum proteins alone.

resultsIncidence of pneumonia was 12% (n = 9). Different variable sets were produced by BE when considering all variables and just serum proteins alone. BE selected the variables ISS, AIS chest, and cryoprecipitate within the first 24 h following injury for the first predictor set 1 and FGF-basic, IL-2R, and IL-6 for predictor set 2. Using both variable sets, a RF was generated with AUCs of 0.95 and 0.87-both higher than LR algorithms.

conclusionAdvanced modeling allowed for the identification of clinical and biomarker data predictive of pneumonia in a cohort of predominantly blast-injured combat trauma patients. The generalizability of the models developed here will require an external validation dataset.

Indexed as

Clinical Decision RulesMilitary PersonnelAdultAlgorithmsBlast InjuriesCross InfectionExtremitiesHumansIncidenceLogistic ModelsMachine LearningMaleModels, StatisticalPneumoniaRetrospective StudiesRisk Assessment

Identifiers

PMID31748888

What OpenQuestion holds

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Registered trials

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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.