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Three Data-Driven Phenotypes of Multiple Organ Dysfunction Syndrome Preserved from Early Childhood to Middle
Jiancheng Ye1, L Nelson Sanchez-Pinto2
1Institute for Public Health and Medicine (IPHAM), Feinberg School of Medicine, Northwestern University, Chicago, USA.
Abstract:
Multiple organ dysfunction syndrome (MODS) is one of the major causes of death and long-term impairment in critically ill patients. MODS is a complex, heterogeneous syndrome consisting of different phenotypes, which has limited the development of MODS-specific therapies and prognostic models. We used an unsupervised learning approach to derive novel phenotypes of MODS based on the type and severity of six individual organ dysfunctions. In a large, multi-center cohort of pediatric, young and middle-aged adults admitted to three different intensive care units, we uncovered and characterized three distinct data-driven phenotypes of MODS which were reproducible across age groups, where independently associated with outcomes and had unique predictors of in-hospital mortality.
Insights
Researchers identified three distinct patient subgroups for Multiple Organ Dysfunction Syndrome (MODS) using data-driven methods. These phenotypes improve understanding of critically ill patients and may guide future personalized treatments.
Area of Science:
- Critical Care Medicine
- Data Science in Healthcare
- Clinical Phenotyping
Background:
- Multiple Organ Dysfunction Syndrome (MODS) is a leading cause of mortality and morbidity in intensive care units.
- The heterogeneity and complex phenotypes of MODS hinder the development of targeted therapies and accurate prognostic models.
- Current understanding of MODS phenotypes lacks granularity, limiting personalized treatment strategies.
Purpose of the Study:
- To apply an unsupervised learning approach to identify novel, data-driven phenotypes of MODS.
- To characterize these phenotypes based on organ dysfunction patterns and severity.
- To assess the reproducibility, outcome association, and mortality predictors of the identified MODS phenotypes across diverse age groups.
Main Methods:
- Utilized an unsupervised machine learning approach on a large, multi-center intensive care unit (ICU) cohort.
- Analyzed data from pediatric, young, and middle-aged adult patients.
- Derived phenotypes based on the type and severity of six individual organ dysfunctions.
Main Results:
- Identified and characterized three distinct, reproducible data-driven phenotypes of MODS.
- These phenotypes were consistently observed across pediatric and adult patient populations.
- Each phenotype demonstrated independent associations with patient outcomes and unique predictors of in-hospital mortality.
Conclusions:
- Data-driven phenotyping offers a novel approach to understanding MODS heterogeneity.
- The identified MODS phenotypes are clinically relevant, reproducible, and associated with distinct outcomes.
- These findings may pave the way for developing more precise therapeutic strategies and prognostic tools for critically ill patients with MODS.
