Investigating Fibrinolysis Resistance in Sepsis Using High-Multiplex Plasma Proteomics: A Statistical Analysis Plan.

Authors

Lucy A Coupland, Mark Richardson, Eurwin Suryana, Sam Olechnowicz, Matthew Self, Allan Motyer, Rory Bowden, Anders Aneman

Published in

Acta anaesthesiologica Scandinavica. Volume 70. Issue 10. Pages e70358.

Abstract

Sepsis-induced coagulopathy is characterised by consumptive coagulation abnormalities and resistance to fibrinolysis, which is increasingly recognised as a driver of microvascular thrombosis, organ dysfunction and mortality. The molecular determinants of the fibrinolysis-resistant phenotype remain incompletely defined.
To present a pre-specified statistical analysis plan for a plasma proteomic investigation of fibrinolysis resistance in 89 samples collected serially from 32 critically ill patients with sepsis, using the Olink Explore HT platform (approximately 5400 proteins). The plan is designed to characterise protein associations with a validated bedside measure of fibrinolysis resistance, the ratio of tissue plasminogen activator-induced lysis time to fibrin clot amplitude (TPA-LT/FIBA10). The associations will be examined across the fibrinolysis resistance severity continuum and between three ordinal severity grades, and over time within individual patients.
The analytic framework combines five complementary approaches: (i) limit-of-detection-based data filtering, (ii) continuous-variable modelling of protein abundance against TPA-LT/FIBA10 using limma-trend regression, (iii) categorical comparison across severity grades using empirical Bayes moderated statistics, (iv) linear mixed-effects modelling of longitudinal intra-patient trajectories and (v) pathway-level enrichment analysis to translate statistical associations into mechanistic hypotheses. A semi-supervised pathway clustering analysis will describe how proteins change by fibrinolysis resistance in eight a priori defined domains.
This multi-model design is intended to define genuine biological signals in a high-dimensional dataset while remaining transparent about the exploratory nature of several components. The statistical reasoning behind each method is discussed alongside anticipated limitations.
ClinicalTrials.gov: NCT06680180.

PMID:
42885115
Bibliographic data and abstract were imported from PubMed on 16 Oct 2026.

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