Evidence map›Paper›PMID 35996117›Full record

Observational studyCritical care (London, England)2022

Using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study.

How-Yang Tseng, Chieh-Lung Chen, Yu-Chao Lin, Ming-Che Chuang, Wu-Huei Hsu, Wan-Yun Hsiao, Tung-Mei Chen, Min-Tzu Wang, Wei-Chun Huang, Chih-Yu Chen and 4 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Critical care (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

6 citing papers in PubMed, 7 citations in OpenAlex.

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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

14 authors at 3 institutions in 1 country.

How-Yang Tseng *Division of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 40402, Taiwan.
Chieh-Lung Chen *Division of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 40402, Taiwan.
Yu-Chao LinDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 40402, Taiwan.
Ming-Che ChuangDepartment of Internal Medicine, China Medical University Hospital, Taichung, Taiwan.
Wu-Huei HsuDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 40402, Taiwan.
Wan-Yun HsiaoDepartment of Respiratory Therapy, China Medical University Hospital, Taichung, Taiwan.
Tung-Mei ChenNursing Department, China Medical University Hospital, Taichung, Taiwan.
Min-Tzu WangDepartment of Respiratory Therapy, China Medical University Hospital, Taichung, Taiwan.
Wei-Chun HuangDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 40402, Taiwan.
Chih-Yu ChenDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 40402, Taiwan.
Biing-Ru WuDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 40402, Taiwan.
Chih-Yen TuDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 40402, Taiwan.
Shinn-Jye LiangDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 40402, Taiwan. D6142@mail.cmuh.org.tw.
Wei-Cheng ChenDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 40402, Taiwan. pion501@gmail.com.
China Medical University Hospital · TWChina Medical University · TWNational Yang Ming Chiao Tung University · TW

Funding

China Medical University Hospital DMR-107-022
6 · The paper itself

Abstract

backgroundAlthough lung protective strategy and adjunctive intervention are associated with improved survival in patients with acute respiratory distress syndrome (ARDS), the implementation of effective therapies remains low. This study aimed to evaluate whether the use of business intelligence (BI) for real-time data visualization is associated with an improvement in lung protective strategy and adjunctive therapy.

methodsA retrospective observational cohort study was conducted on patients with ARDS admitted between September 2020 and June 2021 at two intensive care units (ICUs) of a tertiary referral hospital in Taiwan. BI was imported for data visualization and integration to assist in clinical decision in one of the ICUs. The primary outcomes were the implementation of low tidal volume ventilation (defined as tidal volume/predicted body weight ≤ 8 mL/kg) within 24 h from ARDS onset. The secondary outcomes included ICU and hospital mortality rates.

resultsAmong the 1201 patients admitted to the ICUs during the study period, 148 (12.3%) fulfilled the ARDS criteria, with 86 patients in the BI-assisted group and 62 patients in the standard-of-care (SOC) group. Disease severity was similar between the two groups. The application of low tidal volume ventilation strategy was significantly improved in the BI-assisted group compared with that in the SOC group (79.1% vs. 61.3%, p = 0.018). Despite their ARDS and disease severity, the BI-assisted group tended to achieve low tidal volume ventilation. The ICU and hospital mortality were lower in the BI-assisted group.

conclusionsThe use of real-time visualization system for data-driven decision support was associated with significantly improved compliance to low tidal volume ventilation strategy, which enhanced the outcomes of patients with ARDS in the ICU.

Indexed as

Respiratory Distress SyndromeHumansIntensive Care UnitsLungRespiration, ArtificialRetrospective StudiesTidal VolumeAcute respiratory distress syndromeBusiness intelligenceData-driven decision supportIntensive care unitLung protective strategyReal-time visualization

Identifiers

PMID35996117
PMCPMC9395891
OpenAlexW4292623795

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.