Evidence map›Paper›PMID 36159546›Full record

ArticleWorld journal of clinical cases2022

Evaluation of progressive early rehabilitation training mode in intensive care unit patients with mechanical ventilation.

Xiao-Jing Qie, Zhi-Hong Liu, Li-Min Guo

Abstract read
In one paragraph

Article in World journal of clinical cases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 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

7 citing papers in PubMed, 2 syntheses or guidelines 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

3 authors.

Xiao-Jing QieDepartment of Cardiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, Hebei Province, China.
Zhi-Hong LiuDepartment of Intensive Care Unit, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, Hebei Province, China.
Li-Min GuoDepartment of Cardiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, Hebei Province, China. gweid66@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMechanical ventilation is a common resuscitation method in the intensive care unit (ICU). Unfortunately, this treatment process prolongs the ICU stay of patients with an increased incidence of delirium, which ultimately affects the prognosis.

aimTo evaluate the effect of progressive early rehabilitation training on treatment and prognosis of patients with mechanical ventilation in ICU.

methodsThe convenience sampling method selected 190 patients with mechanical ventilation admitted to the Fourth Hospital of Hebei Medical University from March 2020 to March 2021. According to the random number table method, they were divided into the control and intervention groups. The control group received routine nursing and rehabilitation measures, whereas the intervention group received progressive early rehabilitation training. In addition, the incidence and duration of delirium were compared for the two groups along with mechanical ventilation time, ICU hospitalization time, functional independence measure (FIM) score, Barthel index, and the incidence of complications (deep venous thrombosis, pressure sores, and acquired muscle weakness).

resultsIn the intervention group, the incidence of delirium was significantly lower than in the control group (28%

conclusionProgressive early rehabilitation training reduced the incidence of delirium and complications in ICU patients with mechanical ventilation, which improved prognosis and quality of life.

Indexed as

Barthel indexDeliriumEarly rehabilitation trainingIntensive care unitMechanical ventilation

Identifiers

PMID36159546
PMCPMC9403689

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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.