Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning
Alison E Fohner1,2, John D Greene1, Brian L Lawson1
1Division of Research, Kaiser Permanente, Oakland, California, USA.
Summary
Unsupervised topic modeling revealed significant variability in sepsis treatment patterns among hospitalized patients. Machine learning identified 42 distinct treatment topics, highlighting the need for personalized sepsis care strategies.
Area of Science:
- Computational biology and health informatics
- Machine learning applications in medicine
- Clinical informatics and data science
Background:
- Sepsis heterogeneity poses challenges for targeted treatment development.
- Current methods for characterizing clinical heterogeneity in sepsis are limited.
- Electronic health records (EHRs) contain granular data on sepsis treatment patterns.
Purpose of the Study:
- To apply unsupervised topic modeling to EHR data for evaluating heterogeneity in sepsis treatment.
- To identify distinct treatment patterns and develop patient-specific clinical signatures.
- To assess the correlation of identified topics with sepsis treatment and outcome metrics.
Main Methods:
- Retrospective cohort study of 29,253 adult sepsis patients (2010-2013).
- Latent Dirichlet Allocation (LDA) applied to EHR data (orders, medications, procedures) within the first 24 hours.
- Analysis of topic correlations with mortality, time to antibiotics, and fluid administration.
Main Results:
- Identified 42 clinically recognizable treatment topics.
- High variability in patient clinical signatures; only 43.1% had a single dominant topic.
- Significant patient- and population-level heterogeneity in early sepsis treatment was captured.
Conclusions:
- Machine learning-based topic modeling effectively captures sepsis treatment heterogeneity.
- This approach may enable more precise clinical characterization and understanding of sepsis variability.
- Findings suggest potential for improved, personalized sepsis management strategies.
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