Evidence map›Paper›PMID 39354616›Full record

ArticleCritical care (London, England)2024

Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study.

Tilendra Choudhary, Pulakesh Upadhyaya, Carolyn M Davis, Philip Yang, Simon Tallowin, Felipe A Lisboa, Seth A Schobel, Craig M Coopersmith, Eric A Elster, Timothy G Buchman and 2 more

Abstract readMulticenter Study
In one paragraph

Article in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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

13 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Tilendra Choudhary *Department of Surgery, Duke University School of Medicine, Durham, NC, 27707, USA. tilendra.choudhary@duke.edu.
Pulakesh Upadhyaya *Department of Surgery, Duke University School of Medicine, Durham, NC, 27707, USA.
Carolyn M DavisDepartment of Surgery, Emory University School of Medicine, Atlanta, GA, 30332, USA.
Philip YangDivision of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Atlanta, GA, 30322, USA.
Simon TallowinSurgical Critical Care Initiative (SC2i), Uniformed Services University of the Health Sciences, Bethesda, MD, 20814, USA.
Felipe A LisboaSurgical Critical Care Initiative (SC2i), Uniformed Services University of the Health Sciences, Bethesda, MD, 20814, USA.
Seth A SchobelSurgical Critical Care Initiative (SC2i), Uniformed Services University of the Health Sciences, Bethesda, MD, 20814, USA.
Craig M CoopersmithDepartment of Surgery, Emory University School of Medicine, Atlanta, GA, 30332, USA.
Eric A ElsterSurgical Critical Care Initiative (SC2i), Uniformed Services University of the Health Sciences, Bethesda, MD, 20814, USA.
Timothy G BuchmanDepartment of Surgery, Emory University School of Medicine, Atlanta, GA, 30332, USA.
Christopher J DenteDepartment of Surgery, Emory University School of Medicine, Atlanta, GA, 30332, USA.
Rishikesan KamaleswaranDepartment of Surgery, Duke University School of Medicine, Durham, NC, 27707, USA. r.kamaleswaran@duke.edu.

Funding

Implementing a Maternal health and PRegnancy Outcomes Vision for Everyone (IMPROVE)UL1TR002378 · NCATS · EMORY UNIVERSITY · PI Andres J Garcia, Elizabeth O. Ofili · 2017 to 2026
$92.1M
Critical Care Training ProgramT32GM095442 · NIGMS · EMORY UNIVERSITY · PI Craig M Coopersmith · 2011 to 2026
$4.7M
Sepsis Physiomarkers for Appropriate Risk Knowledge of monitored patients in the ICU (SPARK-ICU)R01GM139967 · NIGMS · EMORY UNIVERSITY · PI KAMALESWARAN, RISHIKESAN · 2021 to 2025
$2.8M
The Gut as a Target to Improve Outcomes in SepsisR35GM148217 · NIGMS · EMORY UNIVERSITY · PI Craig M Coopersmith · 2023 to 2026
$2.2M
NCATS NIH HHS UL1 TR002378NIGMS NIH HHS R01 GM139967NIGMS NIH HHS R35 GM148217NIGMS NIH HHS T32 GM095442NIH HHS GM095442NIH HHS GM148217NIH HHS R01GM139967NIH HHS UL1TR002378U.S. Department of Defense HU0001-15-2-0001U.S. Department of Defense USUHS HT9404-13-1-0032
6 · The paper itself

Abstract

backgroundSeptic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of these patients may reveal insights about the broader heterogeneity in the clinical course of sepsis, considering multi-organ dynamics. We aimed to derive novel phenotypes of sepsis-induced ARF using observational clinical data and investigate the generalizability of the derived phenotypes.

methodsWe performed a multi-center retrospective study of ICU patients with sepsis who required mechanical ventilation for ≥ 24 h. Data from two different high-volume academic hospital centers were used, where all phenotypes were derived in MICU of Hospital-I (N = 3225). The derived phenotypes were validated in MICU of Hospital-II (N = 848), SICU of Hospital-I (N = 1112), and SICU of Hospital-II (N = 465). Clinical data from 24 h preceding intubation was used to derive distinct phenotypes using an explainable machine learning-based clustering model interpreted by clinical experts.

resultsFour distinct ARF phenotypes were identified: A (severe multi-organ dysfunction (MOD) with a high likelihood of kidney injury and heart failure), B (severe hypoxemic respiratory failure [median P/F = 123]), C (mild hypoxia [median P/F = 240]), and D (severe MOD with a high likelihood of hepatic injury, coagulopathy, and lactic acidosis). Patients in each phenotype showed differences in clinical course and mortality rates despite similarities in demographics and admission co-morbidities. The phenotypes were reproduced in external validation utilizing the MICU of Hospital-II and SICUs from Hospital-I and -II. Kaplan-Meier analysis showed significant difference in 28-day mortality across the phenotypes (p < 0.01) and consistent across MICU and SICU of both Hospital-I and -II. The phenotypes demonstrated differences in treatment effects associated with high positive end-expiratory pressure (PEEP) strategy.

conclusionThe phenotypes demonstrated unique patterns of organ injury and differences in clinical outcomes, which may help inform future research and clinical trial design for tailored management strategies.

Indexed as

Critical IllnessPhenotypeRespiratory InsufficiencySepsisAgedFemaleHumansIntensive Care UnitsMaleMiddle AgedRespiration, ArtificialRetrospective StudiesAcute respiratory failureCritical carePhenotypeSepsis-induced ARFUnsupervised machine learning

Identifiers

PMID39354616
PMCPMC11445942

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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.