Clinical Applications of Polymyxin B Hemadsorption in Sepsis and Septic Shock
Bibliographic record
Abstract
Sepsis and septic shock (SS) represent complex, life-threatening conditions driven by a dysregulated host immune response, leading to multi-organ failure (MOF). The SEPSIS-3 guidelines have emphasized the role of immunology in defining sepsis, but therapies targeting individual mediators have largely failed. Hemoadsorption (HA), particularly with polymyxin B (PMX), presents a promising approach to modulate this immune response by non-specifically removing endotoxins and other mediators, potentially restoring physiological homeostasis. This review explores the use of PMX hemoperfusion (PMX-HA) over the last 20 years in critically ill patients, examining its role in sepsis, particularly in endotoxemic septic shock. PMX-HA works by targeting endotoxin removal, reducing inflammatory mediators, and modulating immune cell activity, including neutrophil and monocyte function. However, treatment success varies due to patient heterogeneity. Identifying optimal target populations, based on markers like endotoxin activity (EAA), SOFA scores, and lactate levels, is critical for determining the timing, dose, and duration of PMX-HA therapy. Recent studies have highlighted the importance of stratifying patients by severity and endotoxin burden, suggesting that PMX-HA is most beneficial for patients with high endotoxin activity and severe organ dysfunction. Additionally, prolonged PMX-HA sessions may improve outcomes in patients with sustained endotoxin levels. This review emphasizes the need for a personalized approach to PMX-HA, with tailored treatment protocols to optimize clinical outcomes in sepsis and septic shock patients. Future research should focus on refining patient selection criteria and determining the most effective treatment regimens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".