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Record W2964265037 · doi:10.1002/ajh.25581

IgA‐mediated autoimmune hemolytic anemia

2019· review· en· W2964265037 on OpenAlexaff
Eri Kawata, Ian Chin‐Yee, Cyrus C. Hsia, Ziad Solh

Bibliographic record

VenueAmerican Journal of Hematology · 2019
Typereview
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsAutoimmune hemolytic anemiaMedicineCoombs testHaptoglobinInternal medicineImmunologyCold AgglutininHemolytic anemiaSpherocytosisHemolysisHereditary spherocytosisAnemiaGastroenterologyAntibodySpleenSplenectomy

Abstract

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An eight-month-old previously healthy girl presented with a one-week history of fever, cough, rhinorrhea, and poor appetite. Initial laboratory findings revealed hemoglobin (Hb) 4.5 g/dL, hematocrit 12%, and mean cell volume (MCV) 121.8 fL with evidence of hemolysis; lactate dehydrogenase was elevated at 497 U/L (<300 U/L), total bilirubin 2.44 mg/dL, haptoglobin <10 mg/dL and absolute reticulocyte count 186 × 109/L (normal 10-100), reticulocyte percentage 23.3%. Peripheral blood film showed spherocytes and erythrocyte agglutination (Figure 1A, Wright-Giemsa stain, original magnification ×50), which falsely elevated the MCV.1-3 Direct antiglobulin test (DAT) was negative for IgG and C3d. The cold agglutinin screen and Donath-Landsteiner test were negative. Hb electrophoresis showed a normal pattern, and the screening test for hereditary spherocytosis was also negative. On ultrasound, her spleen was normal in size for age at 6.3 cm.4 As the blood film strongly suggested an immune mediated hemolytic etiology, further DAT investigation was carried out for IgM and IgA antibodies. It was strongly positive for IgA antiglobulin (Figure 1B), confirming a diagnosis of IgA-mediated autoimmune hemolytic anemia (AIHA). The patient was treated with prednisone to which she responded well, and her Hb improved significantly. The DAT plays a central role in diagnosing AIHA, detecting IgG and C3d on the surface of the red blood cells (RBC). Approximately 3-11% of patients with AIHA are estimated to be DAT-negative.5 There are three main causes of DAT-negative AIHA: (1) RBC-bound IgG immunoglobulin is below the detection limit of conventional DAT reagents, which contain anti-IgG and anti-C3d; (2) RBC-bound IgA or IgM immunoglobulin; and (3) low-affinity IgG autoantibodies.5-7 Although our investigations were sufficient, an eluate using monospecific antisera may detect IgA autoantibodies that are missed by DAT.8 Pure IgA-AIHA is a rare phenomenon and accounts for 0.03% to 4% of DAT-negative AIHA cases.4-7 Reported cases of IgA-AIHA vary in age, etiology, and severity.7, 9-11 Management strategies are similar to other types of AIHA: corticosteroids for the initial therapy with intravenous immunoglobulins, immunosuppressive drugs, rituximab, and splenectomy for refractory cases. In this case, the blood film findings of spherocytes and RBC agglutination were the initial clue to an immune mediated hemolytic mechanism, despite the initial DAT being negative. This case highlights the importance of the blood film interpretation in the investigation of DAT-negative hemolysis. The authors declare no potential conflict of interest. EK, ICY, CCH, and ZS collected and analyzed the data. EK and ZS wrote the manuscript. ICY and CCH reviewed the manuscript. We sincerely thank Jeff Kinney and Laura Aseltine for providing Figure 1B. All authors have reviewed and approved the manuscript.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.323
Teacher spread0.299 · 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 teacher head, not a consensus.

Study designOther design
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

Citations3
Published2019
Admission routes1
Has abstractyes

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