Evidence map›Paper›PMID 39607120›Full record

ArticleCritical care science2024

Analyzing how the components of the SOFA score change over time in their contribution to mortality.

Barbara D Lam, Tristan Struja, Yanran Li, João Matos, Ziyue Chen, Xiaoli Liu, Leo Anthony Celi, Yugang Jia, Jesse Raffa

Abstract read
In one paragraph

Article in Critical care science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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.

Barbara D LamDepartment of Medicine, Beth Israel Deaconess Medical Center - Boston (MA), United States.ORCID http://orcid.org/0000-0002-5585-3823
Tristan StrujaLaboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology - Cambridge (MA), United States.ORCID http://orcid.org/0000-0003-0199-0184
Yanran LiHarvard T.H. Chan School of Public Health - Boston (MA), United States.ORCID http://orcid.org/0009-0009-9263-6789
João MatosLaboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology - Cambridge (MA), United States.ORCID http://orcid.org/0000-0002-0312-1647
Ziyue ChenGenome Institute of Singapore, Agency for Science, Technology and Research - Singapore.ORCID http://orcid.org/0009-0004-4041-2904
Xiaoli LiuLaboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology - Cambridge (MA), United States.ORCID http://orcid.org/0000-0001-9592-9020
Leo Anthony CeliDepartment of Medicine, Beth Israel Deaconess Medical Center - Boston (MA), United States.ORCID http://orcid.org/0000-0001-6712-6626
Yugang JiaLaboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology - Cambridge (MA), United States.ORCID http://orcid.org/0009-0008-0628-5461
Jesse RaffaLaboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology - Cambridge (MA), United States.ORCID http://orcid.org/0000-0001-8665-2429

Funding

Integrating Data, Models, and Reasoning in Critical CareR01EB001659 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI MARK, ROGER GREENWOOD · 2003 to 2012
$9.8M
Critical Care Informatics: Ethical considerations around the use and sharing of health-related dataR01EB017205 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI CELI, LEO ANTHONY G, MARK, ROGER GREENWOOD · 2014 to 2021
$3.8M
Critical Care InformaticsR56EB017205 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI CELI, LEO ANTHONY G, GHASSEMI, MARZYEH · 2018 to 2023
$783k
NIBIB NIH HHS R01 EB001659NIBIB NIH HHS R01 EB017205NIBIB NIH HHS R56 EB017205
6 · The paper itself

Abstract

objectiveDetermine how each organ component of the SOFA score differs in its contribution to mortality risk and how that contribution may change over time.

methodsWe performed multivariate logistic regression analysis to assess the contribution of each organ component to mortality risk on Days 1 and 7 of an intensive care unit stay. We used data from two publicly available datasets, eICU Collaborative Research Database (eICU-CRD) (208 hospitals) and Medical Information Mart for Intensive Care IV (MIMIC-IV) (1 hospital). The odds ratio of each SOFA component that contributed to mortality was calculated. Mortality was defined as death either in the intensive care unit or within 72 hours of discharge from the intensive care unit.

resultsA total of 7,871 intensive care unit stays from eICU-CRD and 4,926 intensive care unit stays from MIMIC-IV were included. Liver dysfunction was most predictive of mortality on Day 1 in both cohorts (OR 1.3; 95%CI 1.2 - 1.4; OR 1.3; 95%CI 1.2 - 1.4, respectively). In the eICU-CRD cohort, central nervous system dysfunction was most predictive of mortality on Day 7 (OR 1.4; 95%CI 1.4 - 1.5). In the MIMIC-IV cohort, respiratory dysfunction (OR 1.4; 95%CI 1.3 - 1.5) and cardiovascular dysfunction (OR 1.4; 95%CI 1.3 - 1.5) were most predictive of mortality on Day 7.

conclusionThe SOFA score may be an oversimplification of how dysfunction of different organ systems contributes to mortality over time. Further research at a more granular timescale is needed to explore how the SOFA score can evolve and be ameliorated.

Indexed as

MortalityOrgan Dysfunction ScoresAgedCohort StudiesDatabases as TopicFemaleHumansIntensive Care UnitsMaleMiddle AgedTime Factors

Identifiers

PMID39607120
PMCPMC11634241

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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.