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.

Keywords: infection; latent Dirichlet allocation; machine learning; topic modeling; treatment heterogeneity.

Figure 1.

Schematic overview of EHR data extraction and LDA implementation in hospitalized sepsis patients. Abbreviations: EHR, electronic health record; LDA, Latent Dirichlet Allocation.

Figure 2.

Aggregate representation of each of 42 statistically generated treatment topics based on electronic health record data, with post hoc assigned clinical labels (top) and categories (bottom and color bars). The width of each individual colored bar represents the proportion of that treatment topic within the sepsis cohort. The highest aggregate proportions are attributable to “diabetes,” “viral pneumonia” (viral PNA), “pneumonia,” and “urinary tract infection” (UTI). Abbreviations: Cardio, cardiovascular; COPD, chronic obstructive pulmonary disease; GI, gastrointestinal; Heme, hematologic; MSK, musculoskeletal; Neuro, neurologic; PNA, pneumonia; UTI, urinary tract infection.

Figure 3.

Computable clinical signatures of individual patients based on the LDA topic modeling approach. Each bar color represents a different topic as displayed in Figure 2 and the width of the color bar represents the proportion of the clinical signature that topic composes. On the left are 9 randomly selected sepsis patients, including 3 each from sepsis (top), severe sepsis (middle), and septic shock (bottom) severity strata. On the right are 9 sepsis patients randomly chosen but based on 3 specific pairings of treatment topics (overall clinical signature comprised of at least 0.33 from both cellulitis and pneumonia; complex care and diarrhea; and heart failure and urinary tract infection).

Figure 4.

“Dominant topic” chord plot representing the co-occurrence of EHR topics within individual computable clinical signatures. The 42 topics are arrayed on the periphery with the width of each band representing the number of patients with that topic being their dominant or second topic in the clinical signature. Each line represents a single hospitalization connecting a dominant topic (bands around the periphery and lines arising from the bands of the same color) to the next topic (endpoint of the line with different color than the adjacent band). The width of the lines represents the number of hospitalizations with that same co-occurrence as the dominant and second topics in their clinical signature. Abbreviations: AbdPain, abdominal pain; ACS, acute coronary syndrome; AFib, atrial fibrillation; AKI, acute kidney injury; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; DKA, diabetic ketoacidosis; FTT, failure to thrive; GIBleed, gastrointestinal bleeding; NIPPV, non-invasive ventilation; Osteo, osteomyelitis; PNA, pneumonia; UTI, urinary tract infection.

Figure 5.

Clinical sepsis measures, stratified by dominant treatment topic of each patient’s computable clinical signatures during sepsis hospitalization. a) Mean time to first antibiotic from emergency department triage in relation to mean amount of intravenous fluid administered in the first 24 hours of hospitalization; b) Mean hospital mortality in relation to mean chronic comorbid disease burden (COPS2) score.