Dataset Information

Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.

ABSTRACT:

Objective

To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic health records.

Materials and methods

A multicenter, retrospective cohort study of 29 253 hospitalized adult sepsis patients between 2010 and 2013 in Northern California. We applied an unsupervised machine learning method, Latent Dirichlet Allocation, to the orders, medications, and procedures recorded in the electronic health record within the first 24 hours of each patient's hospitalization to uncover empiric treatment topics across the cohort and to develop computable clinical signatures for each patient based on proportions of these topics. We evaluated how these topics correlated with common sepsis treatment and outcome metrics including inpatient mortality, time to first antibiotic, and fluids given within 24 hours.

Results

Mean age was 70?±?17 years with hospital mortality of 9.6%. We empirically identified 42 clinically recognizable treatment topics (eg, pneumonia, cellulitis, wound care, shock). Only 43.1% of hospitalizations had a single dominant topic, and a small minority (7.3%) had a single topic comprising at least 80% of their overall clinical signature. Across the entire sepsis cohort, clinical signatures were highly variable.

Discussion

Heterogeneity in sepsis is a major barrier to improving targeted treatments, yet existing approaches to characterizing clinical heterogeneity are narrowly defined. A machine learning approach captured substantial patient- and population-level heterogeneity in treatment during early sepsis hospitalization.

Conclusion

Using topic modeling based on treatment patterns may enable more precise clinical characterization in sepsis and better understanding of variability in sepsis presentation and outcomes.

SUBMITTER: Fohner AE

PROVIDER: S-EPMC7647146 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.

Fohner Alison E AE Greene John D JD Lawson Brian L BL Chen Jonathan H JH Kipnis Patricia P Escobar Gabriel J GJ Liu Vincent X VX

Journal of the American Medical Informatics Association : JAMIA 20191201 12

<h4>Objective</h4>To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic health records.<h4>Materials and methods</h4>A multicenter, retrospective cohort study of 29 253 hospitalized adult sepsis patients between 2010 and 2013 in Northern California. We applied an unsupervised machine learning method, Latent Dirichlet Allocation, to the orders, medications, and procedures recorded in the electronic health record with ...[more]

PMID: 31314892

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Dataset Information

Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.

Objective

Materials and methods

Results

Discussion

Conclusion

Publications

Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.

Similar Datasets

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