Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Decoding candidemia in critically ill patients: unsupervised clustering identifies three unique phenotypes.

Critical care (London, England) - 28 Jun 2026

Reizine Florian, Henry Juliette, Desmedt Luc, Camus Christophe, Coirier Valentin, Arrive François, Burban Estelle, Eustache Gabriel, Belleville Tony, Marc Antoine, Malherbe Jolan, Bouju Pierre, Jaubert Paul, Lesieur Olivier, Leclerc Maxime, Fillâtre Pierre, Frérou Aurélien, Prével Renaud, Mainguy Nolwenn, Cady Anne, Morio Florent, Le Gal Solène, Perraud Estelle, Delhaes Laurence, Imbert Sébastien, Guegan Helene, Bonhomme Julie, Pihet Marc, Dupin Clarisse, Delbove Agathe, Du Cheyron Damien, Launey Yoann, Aubron Cécile, Gangneux Jean-Pierre

Abstract excerpt

BACKGROUND: Candidemia displays significant clinical heterogeneity in critically ill patients. This study aimed to identify distinct clinical phenotypes and to assess their association with 90-day mortality. METHODS: We conducted a multicenter retrospective cohort study of 492 intensive care unit (ICU) patients with candidemia from 16 French ICUs (2015-2023). We performed a factor analysis of mixed data (FAMD)...

Read the complete abstract on PubMed

Topics

Humans
Candidemia
Critical Illness

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Retrospective Studies

Phenotype

Female

Male

France

Aged

Intensive Care Units

Middle Aged

Cohort Studies

Cluster Analysis

Clustering Algorithms

Proportional Hazards Models

Kaplan-Meier Estimate

Source

Open in PubMed Open full text

Identifiers

PMID: 42365321
DOI: 10.1186/s13054-026-05964-4
PMCID: PMC13573444

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 25 Oct 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Decoding candidemia in critically ill patients: unsupervised clustering identifies three unique phenotypes.PMID 42365321Read ↗

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain.Semantic proximity Read ↗

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2,022 critically ill patients with COVID-19 in SpainSemantic proximity Read ↗

Deploying Machine Learning to Validate Clinical Phenotypes and Factors Associated with Mortality Risk in 2,022 Critically Ill Patients with COVID-19 in SpainSemantic proximity Read ↗

Development and validation of clinical phenotypes in sepsis-associated liver injury using multi-algorithm consensus classification: a retrospective cohort study.Semantic proximity Read ↗

Identification and validation of robust hospital-acquired pneumonia subphenotypes associated with all-cause mortality: a multi-cohort derivation and validation.Semantic proximity Read ↗

Predicting clinical outcome with phenotypic clusters in COVID-19 pneumonia: an analysis of 12,066 hospitalized patients from the Spanish registry SEMI-COVID-19Semantic proximity Read ↗

Hybrid phenotype-guided modeling across algorithm-feature regimes with application to ICU mortality prediction for Acinetobacter baumannii.Semantic proximity Read ↗

Phenotypic Consensus Clustering and Treatment Heterogeneity Analysis in Critically Ill Patients with Comorbid Type 2 Diabetes Mellitus.Semantic proximity Read ↗

Development and Validation of Clinical Subphenotypes in Sepsis-Associated Liver Injury Using Multi-Algorithm Consensus Classification: A Retrospective Cohort StudySemantic proximity Read ↗

Risk stratification and data-driven phenotyping of peripherally inserted central catheter-related bloodstream infection: an explainable machine learning analysis of a large critical care cohort.Semantic proximity Read ↗

Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A Multicenter Cohort StudySemantic proximity Read ↗

Data-driven phenotypes across the full AKI severity spectrum in patients admitted to the ICU.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.