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The Efficacy and Safety Of Therapeutic Aheresis In Sepsis and Septic Shock: A Systematic Review and Meta-Analysis

2013· review· en· W2280104852 on OpenAlexaff
Emily Rimmer, Brett L. Houston, Anand Kumar, Ahmed M Abou-Setta, Carol A. Friesen, Alexis F. Turgeon, Donald S. Houston, Ryan Zarychanski

Bibliographic record

VenueBlood · 2013
Typereview
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsCancerCare ManitobaUniversité LavalMcMaster UniversityHôpital de l'Enfant-JésusGeorge & Fay Yee Centre for Healthcare InnovationUniversity of Manitoba
Fundersnot available
KeywordsMedicineSeptic shockSepsisInternal medicineIntensive care medicinePopulationMeta-analysisOdds ratioRandomized controlled trialClinical trialAdverse effectSOFA score

Abstract

fetched live from OpenAlex

Abstract Introduction Sepsis and septic shock are leading causes of ICU mortality. They are characterized by excessive host inflammation, upregulation of procoagulant proteins and depletion of natural anticoagulants. Therapeutic apheresis has the potential to improve survival in sepsis by removing injurious elements and inflammatory cytokines and restoring deficient plasma proteins. The objective of our systematic review was to evaluate the efficacy and safety of apheresis in patients with sepsis or septic shock. Methods We searched PubMed, EMBASE, and CENTRAL (from inception to February 2013), the International Clinical Trials Registry Platform, relevant conference proceedings and bibliographies of pertinent reviews and included clinical trials. Two reviewers independently identified randomized controlled trials of patients diagnosed with sepsis, severe sepsis, septic shock or disseminated intravascular coagulation due to infection who received plasmapheresis, plasma exchange, or plasma filtration compared to placebo or usual care. Two reviewers independently extracted trial-level data including population characteristics, interventions, outcomes, and funding sources. We assessed risk of bias using the Cochrane risk of bias tool. Our primary outcome was all-cause mortality reported at the longest follow-up. Secondary outcomes were hospital and ICU lengths of stay, and reported adverse events. We expressed summary effect measures as odds ratios (OR) with 95% confidence intervals (CI). Random effect models using the Mantel-Haenszel method were used for pooled analyses. Results We identified 1771 potential citations of which 3 trials (144 patients) met inclusion criteria. The mean age of patients ranged from 38 to 53 years in the two adult trials and 1 to 18 years in the single pediatric trial. The mean APACHE score was 25.2 (APACHE II) in one study and 54.9 (APACHE III) in the other study reporting illness severity scores. All 3 studies were adjudicated to be unclear or high risk of bias. We observed that the use of apheresis was not associated with a significant reduction in all cause mortality (OR 0.42, 95% CI 0.16 - 1.12, I2=30%) (see Figure). In a subgroup analysis of studies including children exclusively, we observed that apheresis was associated with a significant reduction in mortality (OR 0.03, 95% CI 0.00 – 0.94). None of the included studies reported ICU or hospital length of stay. Only one study reported adverse events associated with apheresis including 6 episodes of hypotension and one allergic reaction to fresh frozen plasma. Central-venous catheter related complications were not reported. Conclusions In patients with sepsis or septic shock, apheresis is not associated a significant reduction in all cause mortality. There is currently insufficient evidence to recommend apheresis as an adjunctive therapy in patients with sepsis or septic shock. Rigorous randomized controlled trials powered to detect differences in patient-centered, clinically relevant outcomes are required to evaluate the impact of apheresis in this patient population. Disclosures: No relevant conflicts of interest to declare.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.040
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0250.030
Bibliometrics0.0070.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.157
GPT teacher head0.386
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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