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Record W3087622222 · doi:10.1681/asn.2020050564

Extracorporeal Treatment for Chloroquine, Hydroxychloroquine, and Quinine Poisoning: Systematic Review and Recommendations from the EXTRIP Workgroup

2020· review· en· W3087622222 on OpenAlexaff
Ingrid Berling, Joshua D. King, Greene Shepherd, Robert S. Hoffman, Badria Alhatali, Valéry Lavergne, Darren M. Roberts, Sophie Gosselin, Gabrielle Wilson, Thomas D. Nolin, Marc Ghannoum

Bibliographic record

VenueJournal of the American Society of Nephrology · 2020
Typereview
Languageen
FieldMedicine
TopicDrug-Induced Ocular Toxicity
Canadian institutionsMcGill UniversityHôpital Charles-Le MoyneUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsHydroxychloroquineMedicineQuinineChloroquineExtracorporealPharmacologyIntensive care medicineDrug overdoseAdverse effectPoison controlSurgeryInternal medicineMalariaEmergency medicineCoronavirus disease 2019 (COVID-19)Pathology

Abstract

fetched live from OpenAlex

Significance Statement Although poisoning by chloroquine, hydroxychloroquine, or quinine is relatively uncommon, recent use of chloroquine and hydroxychloroquine for COVID-19 has elevated concerns regarding management of such poisonings. To investigate the effect of and indications for extracorporeal treatments in cases of poisoning with these drugs, the Extracorporeal Treatments in Poisoning workgroup conducted systematic reviews of the relevant literature, screened studies, extracted data, and summarized findings. The group concluded that chloroquine, hydroxychloroquine, and quinine are not dialyzable (not amenable to clinically significant removal by extracorporeal treatments) and the current clinical evidence does not support the use of such treatments for chloroquine and quinine poisonings. Considering that data on extracorporeal treatments for hydroxychloroquine toxicity are sparse, the group proposed pharmacokinetic studies to confirm or refute the current impression that the drug is non-dialyzable. Background Although chloroquine, hydroxychloroquine, and quinine are used for a range of medical conditions, recent research suggested a potential role in treating COVID-19. The resultant increase in prescribing was accompanied by an increase in adverse events, including severe toxicity and death. The Extracorporeal Treatments in Poisoning (EXTRIP) workgroup sought to determine the effect of and indications for extracorporeal treatments in cases of poisoning with these drugs. Methods We conducted systematic reviews of the literature, screened studies, extracted data, and summarized findings following published EXTRIP methods. Results A total of 44 studies (three in vitro studies, two animal studies, 28 patient reports or patient series, and 11 pharmacokinetic studies) met inclusion criteria regarding the effect of extracorporeal treatments. Toxicokinetic or pharmacokinetic analysis was available for 61 patients (13 chloroquine, three hydroxychloroquine, and 45 quinine). Clinical data were available for analysis from 38 patients, including 12 with chloroquine toxicity, one with hydroxychloroquine toxicity, and 25 with quinine toxicity. All three drugs were classified as non-dialyzable (not amenable to clinically significant removal by extracorporeal treatments). The available data do not support using extracorporeal treatments in addition to standard care for patients severely poisoned with either chloroquine or quinine (strong recommendation, very low quality of evidence). Although hydroxychloroquine was assessed as being non-dialyzable, the clinical evidence was not sufficient to support a formal recommendation regarding the use of extracorporeal treatments for this drug. Conclusions On the basis of our systematic review and analysis, the EXTRIP workgroup recommends against using extracorporeal methods to enhance elimination of these drugs in patients with severe chloroquine or quinine poisoning.

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)0.012
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.037
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.009
Bibliometrics0.0160.012
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.053
GPT teacher head0.351
Teacher spread0.298 · 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 designSystematic review
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

Citations24
Published2020
Admission routes1
Has abstractyes

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