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Introduction <p>Therapeutic 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.</p> Methods <p>We 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 &gt; 86 pg/mL, lactate dehydrogenase (LDH) &gt;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.</p> Results <p>The 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.</p> Conclusion <p>These 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.</p>

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Towards a clinical decision protocol for therapeutic plasma exchange based on biomarker patterns and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月16日

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  • Nicoleta Sgăvârdea,
  • Darian Onchiş,
  • Fabian Galiş,
  • Ciprian Gindac,
  • Mirela Poroşnicu,
  • Adelina Marinescu,
  • Codruţa Istin,
  • Voichiţa Lăzureanu

摘要

Introduction

Therapeutic 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.

Methods

We 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.

Results

The 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.

Conclusion

These 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.