Platelet Count Response to Obstetrical Use of IVIG for Maternal Thrombocytopenia: A 14-Year Retrospective Study
Bibliographic record
Abstract
Introduction: Thrombocytopenia affects approximately 10% of pregnant individuals with Gestational Thrombocytopenia (GT) accounting for 80% of cases and Immune Thrombocytopenia (ITP) representing 3% of cases. Distinguishing between GT and ITP is challenging, leading to many pregnant individuals with moderate thrombocytopenia receiving treatment for ‘possible ITP’ with Intravenous Immunoglobulin (IVIG) in attempt to raise platelet counts (PLT) for safe labor and delivery and to potentially allow for neuraxial anesthesia. However, evidence on the impact of IVIG on PLT during pregnancy remains limited, and predictive factors for IVIG response are lacking. We aim to investigate the effect of IVIG on PLT in pregnant individuals with moderate thrombocytopenia (PLT < 100 x 10 9/L). The specific objectives are to assess the incremental response in maternal PLT following IVIG administration, the proportion of PLT exceeding 80 x 10 9/L after IVIG administration, the proportion of incremental increase in PLT by more than 20 x 10 9/L following IVIG administration, and to identify factors associated with these outcomes. Methods: We conducted a retrospective cohort study using administrative and chart review data between 2007-2020. We included all pregnant individuals who received IVIG for moderate thrombocytopenia. Data collected encompassed parameters such as age, gestational age at delivery, platelet counts at various timepoints, highest immature platelet fraction (IPF) during pregnancy, IVIG dose and frequency, hemoglobin levels, transfusions, and corticosteroid use. Data were analyzed using descriptive statistics. Paired t-test was used to compare means of PLT pre- and post-IVIG administration. Simple logistic regression was used to assess predictors of PLT ≥ 80 x10 9/L and PLT increment ≥ 20 x10 9/L post-IVIG. Results: Out of 651 deliveries with moderate thrombocytopenia, 79 (12.1%) received IVIG for a total of 4,527.50 grams and a median dose of 60 g (IQR: 30 - 77.5). Excluding 22 pregnancies due to other interventions such as platelet transfusions (n=12), corticosteroids (n=7), or both (n=3), 57 remained for analysis. The median incremental response in PLT following IVIG administration was 27 x10 9/L (IQR: 3-94). Pre-IVIG PLT median was 65 x10 9/L (IQR: 48-73), and post-IVIG PLT median was 98 x10 9/L (IQR: 69-142). PLT increment ≥ 20 x 10 9/L following IVIG was observed in 54.4% (31/57) of deliveries, and 57.9% (33/57) of deliveries achieved PLT ≥ 80 x 10 9/L following IVIG administration. Mean PLT post-IVIG (M = 112.51 x10 9/L, SD 55.88) were significantly greater than pre-IVIG PLT (M = 59 x10 9/L, SD = 19.71) by 53.51 points, p = < .001. Nadir PLT < 30 in pregnancy was associated with a statistically significant increase in PLT ≥ 80 x10 9/L following IVIG (odds ratio (OR) = 6.29, 95% confidence interval (CI) = 1.25-31.51, p < 0.05) and PLT increment ≥ 20 x10 9/L (OR = 7.58, 95% CI = 1.51-38.05, p < 0.05). An IPF < 16% was also associated with a statistically significant increase in PLT ≥ 80 x10 9/L (OR = 4.85, 95% CI = 1.08-21.76, p < 0.05) and PLT increment ≥ 20 x10 9/L (OR = 4.85, 95% CI = 1.08 - 21.76, p < 0.05). Moreover, a post-IVIG PLT increment ≥ 20 x10 9/L was positively associated with pre-IVIG PLT below 50 x10 9/L (OR = 8.67, 95% CI = 1.73 - 43.33, p < 0.05), but negatively associated with PLT 70-100 x10 9/L (OR = 0.18, 95% CI = 0.05 - 0.57, p < 0.05). For post-IVIG PLT ≥ 80 x10 9/L, there were no statistically significant relationships with gestational age, maternal age, pre-pregnant PLT or pre-IVIG PLT. Similarly, for post-IVIG PLT increment ≥ 20 x10 9/L, there were no statistically significant relationships with GA, maternal age, pre-pregnant PLT, or a pre-IVIG PLT between 50-69 x10 9/L. Conclusions: IVIG administration shows promising results for pregnant individuals with moderate thrombocytopenia. The study highlights the importance of nadir PLT in pregnancy, IPF levels, and pre-IVIG PLT as potential factors influencing the response to IVIG treatment. Our study is limited by its retrospective and single-centered design, and small sample size. Nevertheless, these findings generate hypotheses and could inform future prospective multicenter studies to optimize IVIG administration in this setting, considering its effectiveness and potential risks while minimizing costs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".