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Record W4381435688 · doi:10.1111/aji.13737

Intravenous immunoglobulin use in patients with unexplained recurrent pregnancy loss

2023· article· en· W4381435688 on OpenAlexaffabout
Shorooq Banjar, Einav Kadour, Rabea Khoudja, Shaonie Ton-Leclerc, Coralie Beauchamp, Marc Beltempo, Michael H. Dahan, Phil Gold, Isaac Jacques Kadoch, Wael Jamal, Carl A. Laskin, Neal Mahutte, Shauna Reinblatt, Camille Sylvestre, William Buckett, Geneviève Genest

Bibliographic record

VenueAmerican Journal of Reproductive Immunology · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsOttawa Fertility CentreCReATe Fertility CentreUniversity of TorontoMontreal General HospitalMontreal Children's HospitalMcGill UniversityUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsMedicinePregnancyCohortGestationRetrospective cohort studyPediatricsLive birthCohort studyObstetricsInternal medicine

Abstract

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PROBLEM: Recurrent pregnancy loss (RPL) affects up to 4% of couples attempting to conceive. RPL is unexplained in over 50% of cases and no effective treatments exist. Due to the immune system's pivotal role during implantation and pregnancy, immune-mediated RPL may be suspected and immunomodulatory treatments like intravenous immunoglobulin (IVIg) have been administered but remain controversial. The goal of our study was to evaluate our center's 6 year-outcomes and to develop a framework for IVIg use in RPL. METHOD OF THE STUDY: , non-smoker and having had ≥3 consecutive RPL despite previous treatment with aspirin and progesterone. IVIg 0.6-0.8 g/kg was given prior to conception and monthly during pregnancy until 16-20 weeks' gestation. We compared IVIg treated patient's outcomes to a separate "natural history cohort". This cohort was composed of patients consulting at the McGill recurrent pregnancy loss clinic and the MRIC over a 2-year period (January 2020 to December 2021) with similar inclusion criteria as the treatment cohort but did not receive IVIg or other immunomodulatory treatments. The association of IVIg with outcomes (compared to no IVIg) was evaluated among the groups of patients with primary RPL and secondary RPL. The primary outcome was live birth rate (LBR), secondary outcomes included IVIg safety, obstetrical, and neonatal complications. RESULTS: Among 169 patients with unexplained RPL that were included in the study, 111 had primary RPL (38 exposed to IVIg and 83 controls) and 58 had secondary RPL (nine exposed to IVIG and 49 controls). Among patients with primary RPL (n = 111), the LBR was 64.3% (18/28) among patient exposed to IVIg compared to 43.4% (36/83) in controls (p = 0.079); regression analysis adjusting for BMI and number of previous miscarriages showed benefit favoring the use of IVIg (OR = 3.27, CI 95% (1.15-10.2), p = 0.03) when evaluating for live birth. In the subgroup of patients with ≥5 previous RPL and primary RPL (n = 31), IVIg was associated with higher LBR compared to control (10/15 (66.7%) vs. 3/16 (18.8%); p = 0.0113) but not the in the sub-group of patients with <5 miscarriages and primary RPL (8/13 (61.5%) vs. 33/67 (49.3%); p = 0.548). IVIG treatment did not improve LBR in patients with secondary RPL in our study (3/9 (33.3%) vs. 23/49 (47%); p = 0.495). There were no serious adverse events in the IVIg treatment group, obstetrical/neonatal complications were similar between groups. CONCLUSION: IVIg may be an effective treatment for patients with RPL if appropriately used in specific groups of patients. IVIg is a blood product and subject to shortages especially with unrestricted off-label use. We propose considering IVIg in well-selected patients with high order RPL who have failed standard medical therapy. Further mechanistic studies are needed to understand immune-mediated RPL and IVIg's mode of action. This will enable further refinement of treatment criteria and the development of standardized protocol for its use in RPL.

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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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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 designObservational
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".

Quick stats

Citations21
Published2023
Admission routes2
Has abstractyes

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