Evidence map›Paper›PMID 34354174›Full record

ArticleScientific reports2021

Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19.

Chang Su, Zhenxing Xu, Katherine Hoffman, Parag Goyal, Monika M Safford, Jerry Lee, Sergio Alvarez-Mulett, Luis Gomez-Escobar, David R Price, John S Harrington and 5 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
4.1field-weighted citation impact, top 5% 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

23 citing papers in PubMed, 30 citations in OpenAlex.

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  9. Building the Model.Archives of pathology & laboratory medicine · 2023
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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

15 authors at 5 institutions in 1 country.

Chang SuDepartment of Population Health Sciences, Weill Cornell Medicine, 425 E 61 St., New York, NY, 10065, USA.
Zhenxing XuDepartment of Population Health Sciences, Weill Cornell Medicine, 425 E 61 St., New York, NY, 10065, USA.
Katherine HoffmanDepartment of Population Health Sciences, Weill Cornell Medicine, 425 E 61 St., New York, NY, 10065, USA.
Parag GoyalDivision of General Internal Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Monika M SaffordDivision of General Internal Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Jerry LeeWeill Cornell Medical College, Weill Cornell Medicine, New York, NY, USA.
Sergio Alvarez-MulettNew York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA.
Luis Gomez-EscobarNew York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA.
David R PriceNew York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA.
John S HarringtonNew York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA.
Lisa K TorresNew York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA.
Fernando J MartinezNew York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA.
Thomas R CampionDepartment of Population Health Sciences, Weill Cornell Medicine, 425 E 61 St., New York, NY, 10065, USA.
Fei WangDepartment of Population Health Sciences, Weill Cornell Medicine, 425 E 61 St., New York, NY, 10065, USA. few2001@med.cornell.edu.
Edward J SchenckNew York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA. ejs9005@med.cornell.edu.
Weill Cornell Medicine · USCornell University · USNew York Hospital Queens · USNewYork–Presbyterian Hospital · USPresbyterian Hospital · US

Funding

Clinical and Translational Science CenterUL1TR000457 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI IMPERATO-MCGINLEY, JULIANNE L · 2012 to 2016
$46.0M
Multidisciplinary Approach Training in Respiratory ResearchT32HL134629 · NHLBI · WEILL MEDICAL COLL OF CORNELL UNIV · PI Robert J Kaner, Heather Winona Stout Delgado · 2018 to 2026
$5.2M
Necroinflammatory Cell Death in SepsisK23HL151876 · NHLBI · WEILL MEDICAL COLL OF CORNELL UNIV · PI SCHENCK, EDWARD JAMES · 2021 to 2025
$975k
NCATS NIH HHS UL1 TR000457NHLBI NIH HHS K23 HL151876NHLBI NIH HHS T32 HL134629
6 · The paper itself

Abstract

COVID-19-associated respiratory failure offers the unprecedented opportunity to evaluate the differential host response to a uniform pathogenic insult. Understanding whether there are distinct subphenotypes of severe COVID-19 may offer insight into its pathophysiology. Sequential Organ Failure Assessment (SOFA) score is an objective and comprehensive measurement that measures dysfunction severity of six organ systems, i.e., cardiovascular, central nervous system, coagulation, liver, renal, and respiration. Our aim was to identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of SOFA score. Intubated COVID-19 patients at two hospitals in New York city were leveraged as development and validation cohorts. Patients were grouped into mild, intermediate, and severe strata by their baseline post-intubation SOFA. Hierarchical agglomerative clustering was performed within each stratum to detect subphenotypes based on similarities amongst SOFA score trajectories evaluated by Dynamic Time Warping. Distinct worsening and recovering subphenotypes were identified within each stratum, which had distinct 7-day post-intubation SOFA progression trends. Patients in the worsening suphenotypes had a higher mortality than those in the recovering subphenotypes within each stratum (mild stratum, 29.7% vs. 10.3%, p = 0.033; intermediate stratum, 29.3% vs. 8.0%, p = 0.002; severe stratum, 53.7% vs. 22.2%, p < 0.001). Pathophysiologic biomarkers associated with progression were distinct at each stratum, including findings suggestive of inflammation in low baseline severity of illness versus hemophagocytic lymphohistiocytosis in higher baseline severity of illness. The findings suggest that there are clear worsening and recovering subphenotypes of COVID-19 respiratory failure after intubation, which are more predictive of outcomes than baseline severity of illness. Distinct progression biomarkers at differential baseline severity of illness suggests a heterogeneous pathobiology in the progression of COVID-19 respiratory failure.

Indexed as

AgedCOVID-19Critical IllnessFemaleHumansMaleMiddle AgedMultiple Organ FailureOrgan Dysfunction ScoresPrognosisSARS-CoV-2Severity of Illness Index

Identifiers

PMID34354174
PMCPMC8342520
OpenAlexW3189525725

What OpenQuestion holds

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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.