Evidence map›Paper›PMID 40699519›Full record

ArticleInfection2025

Identification and longitudinal assessment of sepsis phenotypes derived from routine clinical data in critically ill patients: a retrospective repeated measures latent profile analysis.

Carolin Jung, Niklas Oetzmann, Hans-Joerg Gillmann, Thomas Stueber

Abstract read
In one paragraph

Article in Infection, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Carolin JungDepartment of Anaesthesiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg Strasse 1, 30625, Hannover, Germany. Jung.Carolin@mh-hannover.de.
Niklas OetzmannDepartment of Anaesthesiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg Strasse 1, 30625, Hannover, Germany.
Hans-Joerg GillmannDepartment of Anaesthesiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg Strasse 1, 30625, Hannover, Germany.
Thomas StueberDepartment of Anaesthesiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg Strasse 1, 30625, Hannover, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe definition of sepsis as an organ dysfunction with dysregulated host response leads to a considerable heterogeneity in this cohort of patients. Research is ongoing to identify subgroups of septic patients who share a common, potentially treatable pathomechanism. There are now several examples of reproducible subgroups, but they often rely on complex biomarker panels and data on their longitudinal stability are scarce, which limits their translation to the bedside. The objective of this study was to identify sepsis subgroups using routinely available clinical data and to assess the temporal stability of these subgroups.

methodsWe retrospectively collected data on all adult patients treated for sepsis according to sepsis-3 criteria in our intensive care unit at a university hospital in Germany between 2013 and 2021. Subgroup identification was performed using latent profile analysis, based on data collected within 48 h of the onset of their sepsis, and was repeated with data collected 120-144 h after onset. We assessed the stability of subgroup assignment over time and the in-hospital mortality of these subgroups.

resultsThe analysis included 637 patients with sepsis, 83% of whom were in septic shock. Latent profile analysis of clinical data from the first period identified four subgroups with a high median probability of class membership in all subgroups and distinct characteristics. Subgroup 1 included 76 patients (12%) and was characterized by a hepatobiliary and cardiovascular dysfunction. Subgroup 2, which included 242 patients (38%), showed the least inflammation and organ dysfunction. Subgroup 3 included 236 patients (37%) and was characterized by hyperinflammation. Subgroup 4 included 83 patients (13%) who were older and had more comorbidities. They tended to have high procalcitonin and INR levels. In-hospital mortality was excessive in Subgroup 1 and lowest in Subgroups 4 and 2. In the longitudinal assessment conducted at 120-144 h post-inclusion, subgroup 1 demonstrated the greatest stability over time.

conclusionAnalysis of clinical routine data identified four distinct clinical subgroups. In the longitudinal analysis, subgroup 1, which was characterized by hepatobiliary dysfunction and high mortality, demonstrated stability over the course of the illness.

Indexed as

Critical IllnessSepsisAdultAgedAged, 80 and overBiomarkersFemaleGermanyHospital MortalityHumansIntensive Care UnitsLongitudinal StudiesMaleMiddle AgedPhenotypeRetrospective StudiesBiomarkersLatent profile analysisPhenotypesSepsisTime stability

Identifiers

PMID40699519
PMCPMC12675699

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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.