Evidence map›Paper›PMID 36435755›Full record

Observational studyBMC anesthesiology2022

Clinical impact of physician staffing transition in intensive care units: a retrospective observational study.

Yosuke Fujii, Kiichi Hirota, Kentaro Muranishi, Yumiko Mori, Kei Kambara, Yoshitaka Nishikawa, Mitsuko Hashiguchi

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMC anesthesiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 35% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Observational
  2. Article
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 at 3 institutions in 1 country.

Yosuke FujiiDepartment of Anesthesia, Otsu City Hospital, 2-9-9 Motomiya, Otsu, Shiga, 520-0804, Japan. yofujii-kyt@umin.ac.jp.
Kiichi HirotaDepartment of Human Stress Response Science, Institute of Biomedical Science, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka, 573-1191, Japan.
Kentaro MuranishiDepartment of Emergency and Intensive Care Medicine, Otsu City Hospital, 2-9-9 Motomiya, Otsu, Shiga, 520-0804, Japan.
Yumiko MoriDepartment of Anesthesia, Otsu City Hospital, 2-9-9 Motomiya, Otsu, Shiga, 520-0804, Japan.
Kei KambaraDepartment of Anesthesia, Otsu City Hospital, 2-9-9 Motomiya, Otsu, Shiga, 520-0804, Japan.
Yoshitaka NishikawaDepartment of Health Informatics, Kyoto University School of Public Health, Yoshida Konoe-cho, Sakyo-ku, Kyoto, Kyoto, 606-8501, Japan.
Mitsuko HashiguchiDepartment of Anesthesia, Otsu City Hospital, 2-9-9 Motomiya, Otsu, Shiga, 520-0804, Japan.
Otsu Municipal Hospital · JPKansai Medical University · JPKyoto University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntensivists play an essential role in improving the outcomes of critically ill patients in intensive care units (ICUs). The transition of ICU physician staffing from low-intensity ICUs (elective intensivist or no intensivist consultation) to high-intensity ICUs (mandatory intensivist consultation or a closed ICU) improves clinical outcomes. However, whether a transition from high-intensity to low-intensity ICU staffing affects ICU outcomes and quality of care remains unknown.

methodsA retrospective observational study was conducted to examine the impact of high- versus low-intensity staffing models on all-cause mortality in a suburban secondary community hospital with 400 general beds and 8 ICU beds. The ICU was switched from a high-intensity staffing model (high-former period) to low-intensity staffing in July 2019 (low-mid period) and then back to high-intensity staffing in March 2020 (high-latter period). Patients admitted from the emergency department, general ward, or operating room after emergency surgery were enrolled in these three periods and compared, balancing the predicted mortality and covariates of the patients. The primary outcome was all-cause mortality analyzed using hazard ratios (HRs) from Cox proportional hazards regression. An interrupted time-series analysis (ITSA) was also conducted to evaluate the effects of events (level change) and time.

resultsThere were 962 eligible admissions, of which 251, 213, and 498 occurred in the high-former, low-mid, and high-latter periods, respectively. In the matched group (n = 600), the all-cause mortality rate comparing the high-former period with the low-mid period showed an HR of 0.88 [95% confidence interval (CI), 0.56, 1.39; p = 0.58] and that comparing the high-latter period with the low-mid period showed an HR of 0.84 [95% CI, 0.54, 1.30; p = 0.43]. The result for comparison between the three periods was p = 0.80. ITSA showed level changes of 4.05% [95% CI, -13.1, 21.2; p = 0.63] when ICU staffing changed from the high-former to the low-mid period and 1.35% [95% CI, -13.8, 16.5; p = 0.86] when ICU staffing changed from the low-mid to the high-latter period.

conclusionThere was no statistically significant difference in all-cause mortality among the three ICU staffing periods. This study suggests that low-intensity ICU staffing might not worsen clinical outcomes in the ICU in a medium-sized community hospital. Multiple factors, including the presence of an intensivist, other medical staff, and practical guidelines, influence the prognosis of critically ill patients.

Indexed as

Critical IllnessPhysiciansHospital MortalityHumansIntensive Care UnitsPersonnel Staffing and SchedulingWorkforceHigh-intensity staffingICU staffingIntensive care unitIntensivistInterrupted time-series analysisLow-intensity staffing

Identifiers

PMID36435755
PMCPMC9701368
OpenAlexW4309998838

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.