Evidence map›Paper›PMID 40474781›Full record

ArticlePhysiological reports2025

Parenclitic network mapping predicts survival in critically ill patients with sepsis.

Emily Ito, Tope Oyelade, Matthew Wikner, Jinyuan Liu, Watjana Lilaonitkul, Ali R Mani

Abstract read
In one paragraph

Article in Physiological reports, 2025. 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.

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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

6 authors.

Emily ItoNetwork Physiology Lab, Division of Medicine, UCL, London, UK.
Tope OyeladeNetwork Physiology Lab, Division of Medicine, UCL, London, UK.ORCID https://orcid.org/0000-0003-1151-0295
Matthew WiknerInstitute of Health Informatics, UCL, London, UK.
Jinyuan LiuNetwork Physiology Lab, Division of Medicine, UCL, London, UK.
Watjana LilaonitkulInstitute of Health Informatics, UCL, London, UK.
Ali R ManiNetwork Physiology Lab, Division of Medicine, UCL, London, UK.ORCID https://orcid.org/0000-0003-0830-2022

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is a complex disease involving multiple organ systems. A network physiology approach to sepsis may reveal collective system behaviors and intrinsic organ interactions. However, mapping functional connectivity for individual patients has been challenging due to the lack of analytical methods for evaluating physiological networks using routine clinical and laboratory data. This study explored the use of parenclitic network mapping to assess organ connectivity and predict sepsis outcomes based on routine laboratory data. Data from 162 sepsis patients meeting Sepsis-3 criteria were retrospectively analyzed from the MIMIC-III database. Fifteen physiological variables representing organ systems were used to construct organ network connectivity through correlation analysis. Correlation analysis identified 7 interactions linked to 30-day survival. Parenclitic network analysis was used to measure deviations in individual patients' correlations between organ systems from the reference physiological interactions observed in survivors. Parenclitic deviations in the pH-bicarbonate axis (hazard ratio = 2.081, p < 0.001) and pH-lactate axis (hazard ratio = 2.773, p = 0.024) significantly predicted 30-day mortality, independent of the Sequential Organ Failure Assessment (SOFA) score and ventilation status. This study highlights the potential of parenclitic network mapping to provide insights into sepsis pathophysiology and differences in organ system connectivity between survivors and non-survivors independent of sepsis severity and mechanical ventilation status.

Indexed as

SepsisAgedCritical IllnessFemaleHumansMaleMiddle AgedOrgan Dysfunction ScoresPrognosisRetrospective Studiesintensive carenetworknetwork physiologyparencliticsepsissurvival

Identifiers

PMID40474781
PMCPMC12141926

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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.