Analyzing Candidemia in Critically Ill Patients: Unsupervised Clustering Reveals Three Distinct Phenotypes
By
Zejun Yang
Yaotian Zhang
August 28, 2026
Clinical Scorecard: Analyzing Candidemia in Critically Ill Patients: Unsupervised Clustering Reveals Three Distinct Phenotypes
At a Glance
Category Detail
Condition Candidemia in critically ill patients Key Mechanisms Unsupervised machine learning identifies distinct clinical phenotypes with varying mortality rates. Target Population Critically ill patients with candidemia Care Setting Intensive care unit
Key Highlights
Three distinct phenotypes identified: immunosuppressed hematological patients, deteriorating cirrhotic patients, and catheter-related candidemia patients. Phenotypes 1 and 2 have approximately 70% mortality, while Phenotype 3 has 50% mortality. Methodological weaknesses in the study design raise questions about the clinical utility of the identified phenotypes. Guideline-Based Recommendations
Diagnosis
Consider clinical phenotypes when assessing candidemia outcomes. Management
Future models should exclude broad clinical scores linked to mortality from baseline features. Monitoring & Follow-up
Use validated baseline factors to assess clinical relevance post hoc. Risks
High mortality rates in immunosuppressed and cirrhotic patients. Patient & Prescribing Data
Critically ill patients with candidemia
Targeted immunomodulatory or antifungal therapies may be developed from unbiased clinical profiles.
Clinical Best Practices
Avoid using composite severity measures like SAPS II in unsupervised clustering for candidemia. Utilize unbiased clinical, transcriptomic, or biomarker profiles for future models. Related Resources & Content
