Evidence map›Paper›PMID 38287432›Full record

ArticleJournal of intensive care2024

Association between regional critical care capacity and the incidence of invasive mechanical ventilation for coronavirus disease 2019: a population-based cohort study.

Hiroyuki Ohbe, Satoru Hashimoto, Takayuki Ogura, Mitsuaki Nishikimi, Daisuke Kudo, Nobuaki Shime, Shigeki Kushimoto

Abstract read
In one paragraph

Article in Journal of intensive care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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.

Hiroyuki OhbeDepartment of Emergency and Critical Care Medicine, Tohoku University Hospital, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8574, Japan.
Satoru HashimotoNon-Profit Organization ICU Collaboration Network (ICON), Tokyo, Japan.
Takayuki OguraTochigi Prefectural Emergency and Critical Care Centre, Imperil Gift Foundation SAISEIKAI, Utsunomiya Hospital, 911-1 Takebayashi-Machi, Utsunomiya, 321-0974, Japan.
Mitsuaki NishikimiDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical & Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, 734-8551, Japan.
Daisuke KudoDepartment of Emergency and Critical Care Medicine, Tohoku University Hospital, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8574, Japan.
Nobuaki ShimeDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical & Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, 734-8551, Japan.
Shigeki KushimotoDepartment of Emergency and Critical Care Medicine, Tohoku University Hospital, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8574, Japan. kussie@emergency-medicine.med.tohoku.ac.jp.ORCID http://orcid.org/0000-0001-9232-5782

Funding

Japan Agency for Medical Research and Development JP22fk0108654
6 · The paper itself

Abstract

backgroundCoronavirus disease 2019 (COVID-19) has exposed critical care supply shortages worldwide. This study aimed to investigate the association between regional critical care capacity and the incidence of invasive mechanical ventilation following novel COVID-19 during the pandemic in Japan, a country with a limited intensive care unit (ICU) bed capacity of a median of 5.1 ICU beds per 100,000 individuals.

methodsThis population-based cohort study used data from the CRoss Icu Searchable Information System database and publicly available databases provided by the Japanese government and Japanese Society of Intensive Care Medicine. We identified patients recently diagnosed with COVID-19, those who received invasive mechanical ventilation, and those who received extracorporeal membrane oxygenation (ECMO) between February 2020 and March 2023. We analyzed the association between regional critical care capacity (ICU beds, high-dependency care unit (HDU) beds, resource-rich ICU beds, and intensivists) and the incidence of invasive mechanical ventilation, ECMO, and risk-adjusted mortality across 47 Japanese prefectures.

resultsAmong the approximately 127 million individuals residing in Japan, 33,189,809 were recently diagnosed with COVID-19, with 12,203 and 1,426 COVID-19 patients on invasive mechanical ventilation and ECMO, respectively, during the study period. Prefecture-level linear regression analysis revealed that the addition of ICU beds, resource-rich ICU beds, and intensivists per 100,000 individuals increased the incidence of IMV by 5.37 (95% confidence interval, 1.99-8.76), 7.27 (1.61-12.9), and 13.12 (3.48-22.76), respectively. However, the number of HDU beds per 100,000 individuals was not statistically significantly associated with the incidence of invasive mechanical ventilation. None of the four indicators of regional critical care capacity was statistically significantly associated with the incidence of ECMO and risk-adjusted mortality.

conclusionsThe results of prefecture-level analyses demonstrate that increased numbers of ICU beds, resource-rich ICU beds, and intensivists are associated with the incidence of invasive mechanical ventilation among patients recently diagnosed with COVID-19 during the pandemic. These findings have important implications for healthcare policymakers, aiding in efficiently allocating critical care resources during crises, particularly in regions with limited ICU bed capacities. Registry and the registration no. of the study/trial The approval date of the registry was August 20, 2020, and the registration no. of the study was lUMIN000041450.

Indexed as

COVID-19Critical care capacityIntensive care unitInvasive mechanical ventilationPopulation study

Identifiers

PMID38287432
PMCPMC10826037

What OpenQuestion holds

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LicenceCC BY
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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.