High Burden of Cardiac Disease in Pregnancy at a National Referral Hospital in Western Kenya
Bibliographic record
Abstract
Background: Cardiac disease is a leading cause of non-obstetric maternal death worldwide, but little is known about its burden in sub-Saharan Africa. Objectives and Methods: We conducted a retrospective case-control study of pregnant women admitted to a national referral hospital in western Kenya between 2011-2016. Its purpose was to define the burden and spectrum of cardiac disease in pregnancy and assess the utility of the CARPREG I and modified WHO (mWHO) clinical risk prediction tools in this population. Results: Of the 97 cases of cardiac disease in pregnancy, rheumatic heart disease (RHD) was the most common cause (75%), with over half complicated by severe mitral stenosis or pulmonary hypertension. Despite high rates of severe disease and nearly universal antenatal care, late diagnosis of cardiac disease was common, with one third (38%) of all cases newly diagnosed after 28 weeks gestational age and 17% diagnosed after delivery. Maternal mortality was 10-fold higher among cases than controls. Cases had significantly more cardiac (56% vs. 0.4%) and neonatal adverse events (61% vs. 27%) compared to controls (p < 0.001). Observed rates of adverse cardiac events were higher than predicted by both CARPREG I and mWHO risk scores, with high cardiac event rates despite low or intermediate risk scores. Conclusions: Cardiac disease is associated with significant maternal and neonatal morbidity and mortality among pregnant women in western Kenya. Existing clinical tools used to predict risk underestimate adverse cardiac events in pregnancy and may be of limited utility given the unique spectrum and severity of disease in this population.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".