Health Outcomes of 215 Mothers of Children With Autoimmune Congenital Heart Block: Analysis of the French Neonatal Lupus Syndrome Registry
Bibliographic record
Abstract
Objective Transplacental passage of maternal anti-SSA and anti-SSB antibodies, potentially associated with maternal autoimmune diseases, can cause neonatal lupus syndrome. Given the paucity of data in this setting, we report short- and long-term outcomes of mothers of offspring with congenital heart block (CHB). Methods This retrospective study included anti-SSA/SSB antibody–positive mothers of fetuses with high-degree CHB and focused on their health status before pregnancy, at CHB diagnosis, and thereafter. Results We analyzed 215 women with at least 1 pregnancy with CHB. Prior to this diagnosis, only 52 (24%) mothers had been diagnosed with an autoimmune disease, mainly systemic lupus erythematosus (SLE; n = 26, 12%) and Sjögren syndrome (SS; n = 16, 7%). Six more were diagnosed with an autoimmune disease during the index pregnancy. Of the 157 mothers (73%) with no such diagnosis at childbirth, 77 (49%) developed one after a median follow-up of 11 years (range: 21 days to 54 years). By the end of follow-up, 135 women (63%) had an autoimmune disease diagnosis, mainly SLE (n = 54, 25%) and SS (n = 72, 33%). Three patients with SLE had renal involvement, and only 6 (3%) had required an immunosuppressive drug at any point. The symptoms best predicting autoimmune disease development were arthralgia and myalgia (P < 0.001), dry syndrome (P = 0.01), and parotid swelling (P = 0.05). Conclusion One-quarter of the patients had an autoimmune disease diagnosis at the time of the fetal CHB diagnosis. Nearly half of those without an initial diagnosis progressed during follow-up, most without severe manifestations. Severe diseases such as lupus nephritis were rarely seen, and immunosuppressive drugs were rarely required.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".