Evidence map›Paper›PMID 36859647›Full record

ArticleInternal and emergency medicine2023

Relationship between intensive care surge capacity and hospital factors: an extensive experience.

Matteo Nocci, Gianluca Villa, Luca Ragazzoni, Lorenzo Tofani, Stefano Romagnoli, Gabriele Baldini, Pietro Bertini, Ives Hubloue, Sabino Scolletta, Maria Teresa Mechi and 1 more

Abstract read
In one paragraph

Article in Internal and emergency medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Measuring surge capacity: preparing for the unexpected.Internal and emergency medicine · 2023
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Matteo NocciSection of Anesthesia and Critical Care, Department of Anesthesia and Critical Care, Azienda Ospedaliero-Universitaria Careggi-Università di Firenze, 50134, Florence, Italy. matteo.nocci@gmail.com.ORCID 0000-0001-7265-938X
Gianluca VillaSection of Anesthesia and Critical Care, Department of Anesthesia and Critical Care, Azienda Ospedaliero-Universitaria Careggi-Università di Firenze, 50134, Florence, Italy.
Luca RagazzoniCRIMEDIM-Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health, Università del Piemonte Orientale, Novara, Italy.
Lorenzo TofaniDepartment of Statistics, Informatics, Applications, Università Di Firenze, Florence, Italy.
Stefano RomagnoliSection of Anesthesia and Critical Care, Department of Anesthesia and Critical Care, Azienda Ospedaliero-Universitaria Careggi-Università di Firenze, 50134, Florence, Italy.
Gabriele BaldiniSection of Anesthesia and Critical Care, Department of Anesthesia and Critical Care, Azienda Ospedaliero-Universitaria Careggi-Università di Firenze, 50134, Florence, Italy.
Pietro BertiniCardiothoracic and Vascular Anesthesia and Intensive Care, Department of Anesthesia and Critical Care Medicine, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy.
Ives HubloueDepartment of Emergency Medicine, Universitair Ziekenhuis Brussel, Brussels, Belgium.
Sabino ScollettaUnit of Anesthesia and Intensive Care Unit, Department of Emergency, Urgency and Transplantation, University Hospital of Siena, Siena, Italy.
Maria Teresa MechiHospital Health Direction, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
Francesco Della CorteCRIMEDIM-Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health, Università del Piemonte Orientale, Novara, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a prolonged surge scenario, the COVID-19 pandemic has offered an unparalleled opportunity to improve hospital surge capacity (SC) understanding and the ability to manage it. In this study, the authors report the experience of a large hospital network and evaluate potential relationships between Intensive Care Units SC (ICU-SC) and some hospital-related variables: bed occupancy, emergency department admissions, ward admission from ED, and elective surgery procedures. Pearson's partial correlation coefficient (r) has been used to define the relationship between SC and the daily values of the above variables, collected through a dedicated digital platform that also ensured a regular quality check of the data. The observation has concerned several levels of analysis, namely two different types of SC calculation (SC base-SCb and SC actual-SCa), hospital category level and multi-hospital level, and two consecutive pandemic waves. Among the 16 hospitals observed, the correlation was shown to be moderate-positive with non-ICU bed occupancy (r/ = 0.62, r/ = 0.54), strong/moderate with ICU bed occupancy (r/ = 0.72, r/ = 0.54), and moderate with ward admissions from ED (r/ = 0.50, r/ = 0.51) On the contrary, the correlation proved to be moderate-negative with ED admissions (r/ =  - 0.69, r/ =  - 0.62) and low with the number of elective surgery procedures (r/ =  - 0.10, r/ =  - 0.16). This study identified a positive correlation between SC and three variables monitored: ICU bed occupancy, non-ICU bed occupancy, and ward admissions from ED. On the contrary, the correlation was negative for ED admission and the number of elective surgery procedures. The results have been confirmed across all levels of analysis adopted.

Indexed as

COVID-19Surge CapacityCritical CareEmergency Service, HospitalHospitalsHumansIntensive Care UnitsPandemicsRetrospective StudiesDisaster medicineIntensive carePandemicsSurge capacity

Identifiers

PMID36859647
PMCPMC9977086

What OpenQuestion holds

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Read underepoch 390

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.