Bayesian Learning of Clinically Meaningful Sepsis Phenotypes in Northern Tanzania

TL;DR

This paper introduces CLAMR, a Bayesian clustering method that identifies clinically meaningful sepsis subtypes by emphasizing interpretability and relevance of features, specifically applied to patients in Tanzania.

Contribution

The paper presents CLAMR, a novel Bayesian clustering approach that incorporates medical interpretability into sepsis subtype identification, tailored for Tanzanian patient data.

Findings

CLAMR successfully identifies interpretable sepsis subtypes.

Features relevant for clustering are statistically validated.

Application to Tanzanian data reveals distinct sepsis phenotypes.

Abstract

Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Recently, researchers have hypothesized that sepsis consists of a heterogeneous spectrum of distinct subtypes, motivating several studies to identify clusters of sepsis patients that correspond to subtypes, with the long-term goal of using these clusters to design subtype-specific treatments. Therefore, clinicians rely on clusters having a concrete medical interpretation, usually corresponding to clinically meaningful regions of the sample space that have a concrete implication to practitioners. In this article, we propose Clustering Around Meaningful Regions (CLAMR), a Bayesian clustering approach that explicitly models the medical interpretation of each cluster center. CLAMR favors clusterings that can be summarized via meaningful feature values, leading to medically significant sepsis patient clusters. We also provide details on measuring the effect of each feature on the clustering using Bayesian hypothesis tests, so one can assess what features are relevant for cluster interpretation. Our focus is on clustering sepsis patients from Moshi, Tanzania, where patients are younger and the prevalence of HIV infection is higher than in previous sepsis subtyping cohorts.