ArticleBMC medical informatics and decision making2026
Towards a clinical decision protocol for therapeutic plasma exchange based on biomarker patterns and machine learning.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionTherapeutic plasma exchange (TPE) is increasingly used as an adjunctive intervention in severe, hyperinflammatory critical illness, including COVID-19, yet clinical guidance remains syndromic and evidence is heterogeneous. We present an integrated, interpretable machine-learning framework designed to support protocolizable TPE decision-making by identifying biochemical phenotypes associated with short-horizon laboratory response to TPE. Our dataset consists of real-world intensive care unit cases and captures the treatment heterogeneity and operational constraints that a workable institutional protocol must accommodate, being well-suited as a “protocol seed” for iterative validation.
methodsWe jointly analyze a COVID-19 cohort and a non-COVID comparator cohort receiving TPE. Three decision trees were constructed to represent: (1) global biochemical improvement, (2) strict improvement dependent on key inflammatory/coagulation markers, and (3) early interleukin-6 (IL-6) response. The models revealed distinct favorable phenotypes—particularly patients with IL-6 > 86 pg/mL, lactate dehydrogenase (LDH) >346 U/L, lymphopenia, and fibrinogen ≤ 8.3 g/L. Apriori analysis further identified high-confidence patterns linking high values of IL-6 and LDH with TPE responsiveness. We constructed a unified four-tier clinical algorithm for candidate stratification.
resultsThe resulting logic aligns with how TPE has been applied in practice in published severe COVID-19 series and trials, where candidate selection typically targets cytokine release syndrome-like phenotypes, organ dysfunction, and hyperinflammatory biomarker profiles. We further contextualize these findings against American Society for Apheresis guidance and COVID-era operational considerations, demonstrating convergence while adding quantitative biomarker thresholds.
conclusionThese findings support two complementary perspectives of benefit: (i) an algorithmic framework that provides transparent, protocol-ready guidelines, and (ii) a phenotype-based clinical approach consistent with observed post-TPE marker changes in severe COVID-19 cases. Larger multicenter validation is warranted; however, the present work provides a practical foundation for protocol construction and auditable decision support in settings already performing TPE.
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