Cardiovascular Severe Maternal Morbidity and Mortality at Delivery in the United States
Bibliographic record
Abstract
Cardiovascular conditions are the leading cause of maternal mortality in North America. The purpose of this study was to examine the relationship between cardiovascular severe maternal morbidity (CSMM) and mortality during delivery hospitalization. We performed a cohort study using the Health Care Cost and Utilization Project, Nationwide Inpatient Sample, and identified delivery hospitalizations with CSMM from 1999 to 2015. We described temporal trends in the incidence of CSMM and its associated case-fatality. Among individuals with CSMM, we evaluated the association between participant characteristics and mortality using logistic regression analyses. Of 13,791,605 delivery hospitalizations, 11,152 were complicated by CSMM. Of those, 495 resulted in mortality. The overall incidence of CSMM was 8.09 per 10,000 delivery hospitalizations (95% CI: 7.94-8.24), increasing from 7.76 to 8.38 per 10,000 delivery hospitalizations over 15 years (P < 0.001). The overall case-fatality for CSMM was 4.44 per 100 CSMM (95% CI: 4.06-4.85), decreasing from 6.55 to 2.50 per 100 CSMM events over the study period (P = 0.035). Among participants with CSMM, Black (adjusted odds ratio [aOR]: 1.80; 95% CI: 1.39-2.32) and Hispanic (aOR: 1.44; 95% CI: 1.09-1.90) women and those with Medicaid insurance (aOR: 1.52; 95% CI: 1.22-1.88), postpartum hemorrhage (aOR: 4.06; 95% CI: 3.05-5.41), or systemic lupus erythematosus (aOR: 2.50; 95% CI: 1.31-4.78) were at increased risk of mortality. The incidence of CSMM increased over 15 years, reflecting transformations within the obstetric population. Although it decreased during the study period, case-fatality from CSMM remained elevated. Several factors associated with mortality from CSMM were identified.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".