Cardiovascular Events During Pregnancy: Implications for Adverse Pregnancy Outcomes in Individuals With Autoimmune and Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: This study examined maternal cardiovascular (CV) events relative to adverse pregnancy outcomes (APOs) among individuals with autoimmune rheumatic diseases (ARDs), primary antiphospholipid syndrome (APS), and those with neither. METHODS: Using a California population-based birth cohort (2005-2020), we identified those with CV events (CVEs), ARDs, and APS through International Classification of Diseases, 9th and 10th revisions, Clinical Modification codes in maternal discharge records. Selected APOs identified from birth certificates were preterm birth (PTB; < 37 weeks' gestation), small-for-gestational-age infants (SGA; birth weight < 10th percentile for age and sex), and a composite of either outcome. Adjusted risk ratios (aRRs) for adverse outcomes and their 95% CIs were calculated. RESULTS: CVEs occurred more frequently in individuals with ARDs (265 of 19,340 [1.4%]) and primary APS (428 of 7758 [5.5%]) than those without (17,130 of 7,004,334 [0.3%]). The presence vs absence of CVEs was associated with a greater incidence of adverse outcomes in ARD (53.2% vs 26.6%), APS (30.6% vs 20.7%), and non-ARD/APS pregnancies (28.2% vs 15.2%). CVEs were associated with increased risks of SGA in all groups (aRRs 1.2-1.5) and PTB in ARD (aRR 1.6, 95% CI 1.3-2.0) and non-ARD/APS (aRR 1.7, 95% CI 1.7-1.8) pregnancies. CONCLUSION: CVEs were associated with modestly increased risks (20-70%) for PTB, SGA, or both across the groups. Notably, > 50% of ARD pregnancies with CVEs experienced APOs. Given that ARD and APS pregnancies have higher (although still low) rates of CVEs and have higher baseline risks of APOs than the general population, the additional burden conferred by CVEs is clinically important.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".