Evidence map›Paper›PMID 38807163›Full record

ArticleEuropean journal of medical research2024

Identifying novel clinical phenotypes of acute respiratory distress syndrome using trajectories of daily fluid balance: a secondary analysis of randomized controlled trials.

Fei Wu, Suqin Shi, Zixuan Wang, Yurong Wang, Le Xia, Qingling Feng, Xin Hang, Min Zhu, Jinqiang Zhuang

Abstract read
In one paragraph

Article in European journal of medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

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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

9 authors.

Fei Wu *Department of Emergency Intensive Care Unit (EICU), The Affiliated Hospital of Yangzhou University, Yangzhou University, No. 45 Taizhou Road, Guangling District, Yangzhou City, 225000, Jiangsu Province, China.
Suqin Shi *Department of Emergency Intensive Care Unit (EICU), The Affiliated Hospital of Yangzhou University, Yangzhou University, No. 45 Taizhou Road, Guangling District, Yangzhou City, 225000, Jiangsu Province, China.
Zixuan WangSchool of Nursing, School of Public Health, Yangzhou University, No. 136 Jiangyang Middle Road, Yangzhou, 225009, Jiangsu, China.
Yurong WangDepartment of Emergency Intensive Care Unit (EICU), The Affiliated Hospital of Yangzhou University, Yangzhou University, No. 45 Taizhou Road, Guangling District, Yangzhou City, 225000, Jiangsu Province, China.
Le XiaDepartment of Emergency Intensive Care Unit (EICU), The Affiliated Hospital of Yangzhou University, Yangzhou University, No. 45 Taizhou Road, Guangling District, Yangzhou City, 225000, Jiangsu Province, China.
Qingling FengDepartment of Emergency Intensive Care Unit (EICU), The Affiliated Hospital of Yangzhou University, Yangzhou University, No. 45 Taizhou Road, Guangling District, Yangzhou City, 225000, Jiangsu Province, China.
Xin HangDepartment of Emergency Intensive Care Unit (EICU), The Affiliated Hospital of Yangzhou University, Yangzhou University, No. 45 Taizhou Road, Guangling District, Yangzhou City, 225000, Jiangsu Province, China.
Min ZhuDepartment of Emergency Intensive Care Unit (EICU), The Affiliated Hospital of Yangzhou University, Yangzhou University, No. 45 Taizhou Road, Guangling District, Yangzhou City, 225000, Jiangsu Province, China. 13773516528@163.com.
Jinqiang ZhuangDepartment of Emergency Intensive Care Unit (EICU), The Affiliated Hospital of Yangzhou University, Yangzhou University, No. 45 Taizhou Road, Guangling District, Yangzhou City, 225000, Jiangsu Province, China. zjq7642807@163.com.

Funding

the Open Project Program of Key Laboratory of Big Data Analysis and Knowledge Services, Yangzhou City, Yangzhou University YBK 202208
6 · The paper itself

Abstract

backgroundPreviously identified phenotypes of acute respiratory distress syndrome (ARDS) could not reveal the dynamic change of phenotypes over time. We aimed to identify novel clinical phenotypes in ARDS using trajectories of fluid balance, to test whether phenotypes respond differently to different treatment, and to develop a simplified model for phenotype identification.

methodsFACTT (conservative vs liberal fluid management) trial was classified as a development cohort, joint latent class mixed models (JLCMMs) were employed to identify trajectories of fluid balance. Heterogeneity of treatment effect (HTE) for fluid management strategy across phenotypes was investigated. We also constructed a parsimonious probabilistic model using baseline data to predict the fluid trajectories in the development cohort. The trajectory groups and the probabilistic model were externally validated in EDEN (initial trophic vs full enteral feeding) trial.

resultsUsing JLCMM, we identified two trajectory groups in the development cohort: Class 1 (n = 758, 76.4% of the cohort) had an early positive fluid balance, but achieved negative fluid balance rapidly, and Class 2 (n = 234, 24.6% of the cohort) was characterized by persistent positive fluid balance. Compared to Class 1 patients, patients in Class 2 had significantly higher 60-day mortality (53.5% vs. 17.8%, p < 0.001), and fewer ventilator-free days (0 vs. 20, p < 0.001). A significant HTE between phenotypes and fluid management strategies was observed in the FACTT. An 8-variables model was derived for phenotype assignment.

conclusionsWe identified and validated two novel clinical trajectories for ARDS patients, with both prognostic and predictive enrichment. The trajectories of ARDS can be identified with simple classifier models.

Indexed as

Fluid TherapyPhenotypeRespiratory Distress SyndromeWater-Electrolyte BalanceAgedFemaleHumansMaleMiddle AgedRandomized Controlled Trials as TopicAcute respiratory distress syndromeFluid balanceJoint latent class mixed modelPhenotype

Identifiers

PMID38807163
PMCPMC11134929

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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.