Targeted Rapid Endotoxin Adsorption (TREA): can we bring precision medicine to sepsis?
Bibliographic record
Abstract
BACKGROUND: Historically, extracorporeal blood purification (EBP) treatment for sepsis was mainly used as an adjunctive therapy for the management of multiple organ failure rather than targeting the removal of toxins from the body that are contributing to the disease state. Approximately 10-15% of sepsis cases, or approximately one-third to half of patients with septic shock, exhibit high levels of endotoxin activity in their blood. Humans are exquisitely sensitive to endotoxin making endotoxic septic shock (ESS) particularly deadly. Today, we have an emerging class of EBP that is specific to endotoxin - targeted rapid endotoxin adsorption (TREA) - that can be used for the treatment of ESS. SUMMARY: In septic patients, evidence for the use of hemofiltration and therapeutic plasma exchange, the two most prevalent forms of EBP, has been difficult to obtain. Additionally, broad-spectrum EBP therapies that target multiple solutes for removal have struggled to identify the right patients. There is significant clinical heterogeneity of the innate immune response across patients with sepsis. In contrast, targeted EBP therapies, which involve measuring a single solute, then choosing appropriate therapy to target its removal, allow for the specific selection of a suitable patient. Unfortunately, measuring the target can prove challenging. Endotoxin can be measured in whole blood using the endotoxin activity assay. However, owing to the size of intact endotoxin molecule, it cannot be filtered using hemofiltration membranes. Adsorption, which only requires the contact of blood or plasma with a sorbent, is therefore a suitable model to target its removal. TREA technologies include devices that specifically target endotoxin (Alteco LPS Adsorber, MATISSE adsorber, Toraymyxin 20R, Toxipak sorption column) and those for which endotoxin removal is included in a more broad-spectrum device (Efferon LPS, oXiris). KEY MESSAGES: While only a small number of devices are currently available in the TREA class of EBP, there is an opportunity here to bring precision medicine to sepsis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".