Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity

Abstract

Abstract

Purpose Various studies have analyzed sepsis subtypes, yet the reproducibility of such results remains unclear. This study aimed to determine the reproducibility of sepsis subtypes across multiple cohorts.

Methods The study examined 63,547 sepsis patients from six distinct cohorts who had similar sepsis-related characteristics (vital signs, lactate, sequential organ failure assessment score, bilirubin, serum, urine output, and Glasgow coma scale). Identical cluster analysis techniques were used, employing 27 clustering schemes, and normalized mutual information (NMI), a metric ranging from 0 to 1 with higher values indicating better concordance, was employed to quantify the clustering solutions' reproducibility. Principal component analysis (PCA) was utilized to obtain the disease axis, and its uniformity across cohorts was evaluated through patterns of feature loading and correlation.

Results The reproducibility of sepsis clustering subtypes across the various studies was modest (median NMI ranging from 0.08 to 0.54). The top-down transfer learning method (model trained on cohorts with greater severity was transferred to cohorts with lower severity score) had a higher NMI value than the bottom-up approach (median [Q1, Q3]: 0.64 [0.49, 0.78] vs. 0.23 [0.2, 0.31], p < 0.001). The reproducibility was greater when the transfer solution was performed within United States (US) cohorts. The PCA analysis revealed that the correlation pattern between variables was consistent across all cohorts, and the first two disease axes were the "shock axis" and "systemic inflammatory response syndrome (SIRS) axis."

Conclusions Cluster analysis of sepsis patients across various cohorts showed modest reproducibility. Sepsis heterogeneity is better characterized through continuous disease axes that coexist to varying degrees within the same individual instead of mutually exclusive subtypes.