Evidence map›Paper›PMID 36401158›Full record

ArticleBMC emergency medicine2022

Overcrowding analysis in emergency department through indexes: a single center study.

Ylenia Colella, Danilo Di Laura, Anna Borrelli, Maria Triassi, Francesco Amato, Giovanni Improta

Abstract read
In one paragraph

Article in BMC emergency medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

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

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

  1. Pooled it
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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

6 authors.

Ylenia ColellaDepartment of Electrical Engineering and Information Technologies, University of Naples "Federico II", Naples, Italy. ylenia.colella1@gmail.com.
Danilo Di LauraDepartment of Electrical Engineering and Information Technologies, University of Naples "Federico II", Naples, Italy.
Anna Borrelli"San Giovanni Di Dio E Ruggi d'Aragona" University Hospital, Salerno, Italy.
Maria TriassiDepartment of Public Health, University of Naples "Federico II", Naples, Italy.
Francesco AmatoDepartment of Electrical Engineering and Information Technologies, University of Naples "Federico II", Naples, Italy.
Giovanni ImprotaDepartment of Public Health, University of Naples "Federico II", Naples, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionOvercrowding in the Emergency Department (ED) is one of the major issues that must be addressed in order to improve the services provided in emergency circumstances and to optimize their quality. As a result, in order to help the patients and professionals engaged, hospital organizations must implement remedial and preventative measures. Overcrowding has a number of consequences, including inadequate treatment and longer hospital stays; as a result, mortality and the average duration of stay in critical care units both rise. In the literature, a number of indicators have been used to measure ED congestion. EDWIN, NEDOCS and READI scales are considered the most efficient ones, each of which is based on different parameters regarding the patient management in the ED.

methodsIn this work, EDWIN Index and NEDOCS Index have been calculated every hour for a month period from February 9th to March 9th, 2020 and for a month period from March 10th to April 9th, 2020. The choice of the period is related to the date of the establishment of the lockdown in Italy due to the spread of Coronavirus; in fact on 9 March 2020 the Italian government issued the first decree regarding the urgent provisions in relation to the COVID-19 emergency. Besides, the Pearson correlation coefficient has been used to evaluate how much the EDWIN and NEDOCS indexes are linearly dependent.

resultsEDWIN index follows a trend consistent with the situation of the first lockdown period in Italy, defined by extreme limitations imposed by Covid-19 pandemic. The 8:00-20:00 time frame was the most congested, with peak values between 8:00 and 12:00. on the contrary, in NEDOCS index doesn't show a trend similar to the EDWIN one, resulting less reliable. The Pearson correlation coefficient between the two scales is 0,317.

conclusionIn this study, the EDWIN Index and the NEDOCS Index were compared and correlated in order to assess their efficacy, applying them to the case study of the Emergency Department of "San Giovanni di Dio e Ruggi d'Aragona" University Hospital during the Covid-19 pandemic. The EDWIN scale turned out to be the most realistic model in relation to the actual crowding of the ED subject of our study. Besides, the two scales didn't show a significant correlation value.

Indexed as

COVID-19Communicable Disease ControlEmergency Service, HospitalHumansPandemicsProspective StudiesEDWIN IndexEmergency DepartmentNEDOCS IndexOvercrowding

Identifiers

PMID36401158
PMCPMC9673888

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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.