Predictors of platelet count response following intravenous immunoglobulin use for maternal thrombocytopenia
Bibliographic record
Abstract
Background Thrombocytopenia in pregnancy may require administering intravenous immunoglobulin (IVIG), particularly when immune thrombocytopenia is suspected. However, the effectiveness of IVIG is not well-defined, creating a gap in optimal treatment strategies. This study aims to evaluate the efficacy of IVIG and identify predictors of platelet response in pregnant persons with moderate-to-severe thrombocytopenia, aiming to optimize clinical decisions and resource use. Methods We conducted a single-center retrospective cohort study of 79 pregnant persons with moderate-to-severe thrombocytopenia (platelets [PLT] <100 ×10 9 /L) who received IVIG between 2007 and 2020. Data on maternal demographics, PLT counts, immature platelet fraction (IPF), and IVIG administration were collected. Logistic regression identified predictors of achieving a PLT ≥ 80 × 10 9 /L and an increment ≥ 20 × 10 9 /L following IVIG administration. Results The median incremental PLT response following IVIG administration was 16 × 10 9 /L, with 49.4 % achieving PLT ≥ 80 × 10 9 /L and 46.8 % achieving an increment ≥ 20 × 10 9 /L. Predictors of a favorable response included nadir PLT < 30 × 10 9 /L (OR = 6.29), IPF < 16 % (OR = 4.85), and pre-IVIG PLT < 50 × 10 9 /L (OR = 8.67). Higher pre-IVIG PLT counts (70–100 ×10 9 /L) were associated with lower odds of achieving a significant PLT increment. Discussion IVIG effectively increases PLT counts in pregnant persons with severe thrombocytopenia, especially in those with a nadir PLT < 30 × 10 9 /L, IPF < 16 %, or pre-IVIG PLT < 50 × 10 9 /L. This study highlights the importance of careful patient selection for IVIG to enhance outcomes and conserve resources. Future research should focus on prospective studies to refine treatment guidelines and resource stewardship of IVIG for maternal thrombocytopenia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".