Maternal, delivery and neonatal outcomes in women with peripartum cardiomyopathy. A study of a population database
Bibliographic record
Abstract
IMPORTANCE: Peripartum cardiomyopathy (PPCM) is a rare but potentially life-threatening form of heart failure affecting women during late pregnancy or early postpartum. Its impact on maternal, delivery, and neonatal outcomes remains understudied, highlighting the need for comprehensive research. OBJECTIVE: To determine the effects of PPCM during pregnancy on maternal, delivery, and neonatal outcomes using a large, contemporary nationwide database. DESIGN: Retrospective analysis of a population-based cohort using the Healthcare Cost and Utilisation Project Nationwide Inpatient Sample (HCUP-NIS) database from 2004 to 2014. SETTING: Hospital inpatient stays across 48 states and the District of Columbia in the United States. PARTICIPANTS: 9,096,788 pregnant women who delivered or had a maternal death, including 2127 diagnosed with PPCM. EXPOSURES: Diagnosis of peripartum cardiomyopathy during or after pregnancy, identified using ICD-9 diagnosis code 674.5x. MAIN OUTCOMES AND MEASURES: Women with PPCM had significantly higher rates of pregnancy-induced hypertension, preeclampsia, eclampsia, caesarean delivery, postpartum haemorrhage, and maternal death compared to those without PPCM. Delivery outcomes showed increased rates of preterm delivery, chorioamnionitis, and wound complications among PPCM patients. Neonatal outcomes revealed higher incidences of intrauterine foetal death and congenital anomalies in offspring of women with PPCM. Demographic and clinical characteristics associated with PPCM included advanced maternal age, African American race, obesity, chronic hypertension, previous caesarean section, smoking during pregnancy, and pregestational diabetes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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".