Evidence map›Paper›PMID 38089596›Full record

ArticleHealth science reports2023

Optimal timing for intubation in patients on non-invasive ventilation: A retrospective cohort study.

Tatsuhiko Abe, Toshishige Takagi, Kazunari Takahashi, Kosuke Yagi, Ai Tsuge, Tomoko Fujii

Abstract read
In one paragraph

Article in Health science reports, 2023. 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
–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

2 citing papers in PubMed.

  1. Article
  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

6 authors.

Tatsuhiko AbeDepartment of Intensive Care Jikei University Hospital Tokyo Japan.ORCID 0009-0006-2793-7225
Toshishige TakagiDepartment of Intensive Care Jikei University Hospital Tokyo Japan.
Kazunari TakahashiDepartment of Intensive Care Jikei University Hospital Tokyo Japan.
Kosuke YagiDepartment of Intensive Care Jikei University Hospital Tokyo Japan.
Ai TsugeDepartment of Clinical Engineering Technology Jikei University Hospital Tokyo Japan.
Tomoko FujiiDepartment of Intensive Care Jikei University Hospital Tokyo Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: The timing of transition from non-invasive ventilation (NIV) to invasive ventilation in the intensive care unit (ICU) is uncertain due to a lack of clinical evidence. This study aimed to identify the optimal timing of intubation in patients with respiratory failure managed with NIVs. Methods: A single-center observational study was conducted in Tokyo, Japan. Patients in the ICU managed with NIV between 2013 and 2022 were screened. The primary outcome was 28-day invasive ventilator-free days. Statistical analyses used locally estimated scatter plot smoothing (LOESS) and generalized linear mixed models to estimate the association between the timing of transition and prolonged intubation duration. Results: During the study period, 139 of 589 adult ICU patients receiving NIV transitioned to invasive ventilation. The LOESS curve indicated the longest 28-day ventilator-free days around 24 h after NIV initiation, after which the primary outcome decreased linearly. Late intubation after 24 h of NIV initiation was associated with fewer 28-day ventilator-free days (adjusted mean difference: -0.22 days [95% confidence interval: -0.31, -0.13]). Conclusion: We identified a non-linear association between the timing of intubation and 28-day invasive ventilator-free days. The critical 24-h time window for patients on NIV was associated with longer 28-day invasive ventilator-free days.

Indexed as

intensive care unitsintubationnon‐invasive ventilationrespiratory insufficiency

Identifiers

PMID38089596
PMCPMC10713869

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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.