Evidence map›Paper›PMID 38681797›Full record

ArticleJournal of intensive medicine2024

Prognostic value of time-varying dead space estimates in mechanically ventilated patients with acute respiratory distress syndrome.

Lianlian Jiang, Hui Chen, Jianfeng Xie, Ling Liu, Yi Yang

Abstract read
In one paragraph

Article in Journal of intensive medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  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

5 authors.

Lianlian JiangJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China.
Hui ChenJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China.
Jianfeng XieJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China.
Ling LiuJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China.
Yi YangJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The dead space fraction (V Methods: This retrospective observational study was conducted using data from the Chinese database in intensive care (CDIC). Eligible participants were adult ARDS patients receiving invasive mechanical ventilation while in the intensive care unit between 1st January 2014 and 31st March 2021. We collected data during the first 7 days of ventilation to calculate various dead space estimates, including ventilatory ratio (VR), corrected minute ventilation ( Results: A total of 392 patients (median age 66 [interquartile range: 55-77] years, median SOFA score 9 [interquartile range: 7-12]) were finally included in our analysis, among whom 132 (33.7%) patients died within 28 days of admission. VR (hazard ratio [HR]=1.04 per 0.1 increase, 95% confidence interval [CI]: 1.01 to 1.06; Conclusions: In cases where V

Indexed as

28-day mortalityAcute respiratory distress syndromeDead spaceMechanical ventilation

Identifiers

PMID38681797
PMCPMC11043632

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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.