Evidence map›Paper›PMID 30507930›Full record

Observational studyPloS one2018

Identifying temporal patterns in trauma admissions: Informing resource allocation.

David P Stonko, Bradley M Dennis, Rachael A Callcut, Richard D Betzold, Michael C Smith, Andrew J Medvecz, Oscar D Guillamondegui

Abstract readObservational Study
In one paragraph

Observational study in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

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

  1. Pooled it
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  13. Observational
  14. How Did the COVID-19 Pandemic Affect Trends in Facial Trauma?Craniomaxillofacial trauma & reconstruction · 2022
    Article
  15. Article
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  17. The Effect of California's Stay-at-Home Order on Trauma Patient Volume During the Coronavirus Disease 2019 Pandemic.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2020
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  18. Review
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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

7 authors.

David P StonkoThe Johns Hopkins Hospital, Department of Surgery, Baltimore, MD, United States of America.ORCID 0000-0002-2804-2857
Bradley M DennisVanderbilt University Hospital, Division of Trauma and Surgical Critical Care, Nashville, TN, United States of America.
Rachael A CallcutDepartment of Surgery, University of California, San Fransisco, San Fransisco, CA, United States of America.ORCID 0000-0002-1582-6716
Richard D BetzoldVanderbilt University Hospital, Division of Trauma and Surgical Critical Care, Nashville, TN, United States of America.
Michael C SmithVanderbilt University Hospital, Division of Trauma and Surgical Critical Care, Nashville, TN, United States of America.
Andrew J MedveczVanderbilt University Hospital, Division of Trauma and Surgical Critical Care, Nashville, TN, United States of America.ORCID 0000-0002-1918-1329
Oscar D GuillamondeguiVanderbilt University Hospital, Division of Trauma and Surgical Critical Care, Nashville, TN, United States of America.

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
NIEHS NIH HHS K01 ES026834
6 · The paper itself

Abstract

backgroundIncreased knowledge of the temporal patterns in the distribution of trauma admissions could be beneficial to staffing and resource allocation efforts. However, little work has been done to understand how this distribution varies based on patient acuity, trauma mechanism or need for intervention. We hypothesize that temporal patterns exist in the distribution of trauma admissions, and that deep patterns exist when traumas are analyzed by their type and severity. STUDY

designWe conducted a cross-sectional observational study of adult patient flow at a level one trauma center over three years, 7/1/2013-6/30/2016. Primary thermal injuries were excluded. Frequency analysis was performed for patients grouped by ED disposition and mechanism against timing of admission; in subgroup analysis additional exclusion criteria were imposed.

results10,684 trauma contacts were analyzed. Trauma contacts were more frequent on Saturdays and Sundays than on weekdays (p<0.001). Peak arrival time was centered around evening shift change (6-7pm), but differed based on ED disposition: OR and ICU or Step-Down admissions (p = 0.0007), OR and floor admissions (p<0.0001), and ICU or Step-Down and floor admissions (p<0.0001). Step-Down and ICU arrival times (p = 0.42) were not different. Penetrating injuries peaked later than blunt (p<0.0001). Trauma varies throughout the year; we establish a high incidence trauma season (April to late October). Different mechanisms have varying dependence upon season; Motorcycle crashes (MCCs) have the greatest dependence.

conclusionWe identify new patterns in the temporal and seasonal variation of trauma and of specific mechanisms of injury, including the novel findings that 1) penetrating trauma tends to present at later times than blunt, and 2) critically ill patients requiring an OR tend to present later than those who are less acute and require an ICU or Step-Down unit. These patients present later than those who are admitted to the floor. Penetrating trauma patients arriving later than blunt may be the underlying reason why operative patients arrive later than other patients.

Indexed as

AdolescentAdultAgedAged, 80 and overCohort StudiesCross-Sectional StudiesFemaleHumansIncidenceInjury Severity ScoreMaleMiddle AgedPatient AdmissionRegistriesSeasonsTime Factors

Identifiers

PMID30507930
PMCPMC6277067

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.