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Rituximab For The Treatment Of Hepatitis C Associated IgM Mediated Cold Hemolytic Anemia – Case Report and Literature Review

2013· article· en· W2346498289 on OpenAlexaff
Emma Kolesar, Madeline Song, Mark Crowther

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversitySt. Joseph's Hospital
Fundersnot available
KeywordsMedicineRituximabInternal medicineGastroenterologyAutoimmune hemolytic anemiaAnemiaAntibodyImmunologyHemolytic anemiaPediatrics

Abstract

fetched live from OpenAlex

In April 2007 a 48-year-old woman with a history of hepatitis C and anti-phospholipid antibody syndrome presented with a hemoglobin (Hb) of 5.3g/dL. Lactate dehydrogenase and bilirubin were elevated at 582U/L and 66µmol/L respectively. Severe autoimmune hemolytic anemia due to cold agglutinins was diagnosed based on a positive DAT (C3B and C3D positive, IgG negative) and detection of cold agglutinins. The patient was discharged 4 days later with a Hb of 8.3g/dL after plasma exchange and transfusion of 3 units of RBCs. Immunosuppressive therapy was initially avoided due to the risk of hepatitis C activation. Treatment over the next 12 months consisted of as needed RBC transfusions and IVIg infusions. Treatment with corticosteroids occurred only during 1 of 2 hospital admissions for severe anemia – the patient otherwise refused corticosteroid treatment. Although responses to IVIg were consistently favorable they were short lived. Rituximab was administered in an effort to achieve a more sustained treatment response. The dose of 375mg/m2weekly for 4 weeks was administered in August 2008. A dramatic but transient decrease in IVIg requirements was observed. Periodic treatment with IVIg was continued over the next 19 months until a second rituximab course was administered in June 2010. IVIg has been required on only 6 occasions in the almost three years since the second rituximab course (see Figure 1). To determine the efficacy of rituximab in this condition, we searched the MEDLINE and EMBASE databases. Articles citing the use of rituximab to treat mixed-type or cold autoimmune hemolytic anemia (CAIHA) were identified. Ten studies (summarized in Table 1) and 43 case reports/series (48 total cases) met our criteria for inclusion. A complete response (CR) required Hb>12g/dL and the sustained absence of hemolysis. Disappearance of cold agglutinins was not required for a CR. A partial response (PR) required an improvement of symptoms, transfusion independency, and either: Hb>10g/dL or at least 2.0g/dL above pre-treatment levels, or a decrease in serum IgM concentration by at least 50%. Non-responders met neither CR nor PR criteria. Ages of the 26 male and 22 female patients ranged from 1-85 years with a median of 62 years. Diagnoses of CAIHA were reported in 40 cases, and mixed-type in 8. On average, patients had received 3 modes of treatment prior to rituximab. CRs were observed in 34 patients (70.8%), PRs in 7 (14.6%), and NRs in 7 (14.6%). Three of the 7 non-responders experienced an improvement in condition despite not meeting response criteria. Adverse effects were reported in only 3 cases and included a post-infusion low-grade fever, transient lymphopenia, and agranulocytosis (possibly due to CMV reactivation and not rituximab therapy).Table 1Studies employing rituximab to treat cold or mixed autoimmune hemolytic anemia.CitationStudy typeNumber of participants, Age rangeAIHA typeOutcome (CR, PR, NR)Barcellini et al, 2012prospective multicenter9, 30-71cold16.6%, 33.3%, 0Berentsen et al, 2010prospective multicenter29, 39-87cold21%, 55%, 24%Berentsen et al, 2001prospective6, 54-80cold14%, 57%, 29%Berentsen et al, 2004prospective27, 51-91cold3%, 51%, 46%Berentsen et al, 2006retrospective multicenter52, 51-96*coldsingle agent: 5%, 53%, 42%in combination: 25%, 42%, 33%Cholankeril et al, 2012retrospective single institution6, 62-894 cold, 2 mixedOR 100%, median Hb rise of 1.8g/dLPeñalver et al, 2010retrospective multicenter9, 20-86*coldCR and MR 66.7%Schöllkopf et al, 2006prospective multicenter20, 54-86cold5%, 40%, 55%Dierickx et al, 2009retrospective multicenter14, 1-87*cold28.6%, 35.7%, 35.7%Zecca et al, 2003prospective1, 1.3coldresponderZaja et al, 2003not specified1, 72coldcomplete responder*age range reflects entire study population, not CAIHA subgroupOR=overall response, MR=maintained response In summary, we report a case of CAIHA. A literature summary supported the efficacy of rituximab for this condition. Based on our observations we recommend the use of rituximab over alternative therapies for patients with CAIHA to reduce symptoms, need for transfusion and reduce exposure to immunosuppressive drugs (Grade 2C Recommendation). Disclosures: Off Label Use: Rituximab was used to treat cold autoimmune hemolytic anemia. Crowther:Asahi Kasai: Membership on an entity’s Board of Directors or advisory committees; Baxter: Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau; Boehringer Ingelheim: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees; CSL Behring: Speakers Bureau; Leo Pharma: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Merck: Consultancy; Octapharma: Consultancy, Membership on an entity’s Board of Directors or advisory committees; Pfizer: Consultancy, Honoraria, Research Funding; Sanofi-Aventis: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees, Research Funding; Viropharma: Membership on an entity’s Board of Directors or advisory committees.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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 designCase report
Domainnot available
GenreEmpirical

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".

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Published2013
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