Management of Preeclampsia in Low- and Middle-Income Countries: Lessons to Date, and Questions Arising, from the PRE-EMPT and Related Initiatives
Bibliographic record
Abstract
Abstract Preeclampsia remains associated with an increased risk of maternal and perinatal morbidity and mortality, and the burden of that excess risk is largely borne by pregnant women and their families in low- and middle-income countries (LMICs). Therefore, the Bill & Melinda Gates Foundation funded the PREeclampsia – Eclampsia Monitoring, Prevention, and Treatment (PRE-EMPT) initiative to accelerate progress. From PRE-EMPT, and related activity, have come a number of impactful findings. First, there is increasing global support for broadening the definition of preeclampsia to include women with hypertension and either significant proteinuria or evidence of target organ damage or fetoplacental compromise (including evidence angiogenic imbalance). Second, using blood pressure (BP) data from the Community-Level Interventions for Preeclampsia trials in India, Mozambique, and Pakistan, acquired on validated-for-pregnancy, semi-automated, low-cost BP devices, there are now population-level, rather than facility-based, estimates for the burden of pregnancy hypertension (sub-categorized into preeclampsia (4%–6%), gestational hypertension (7%–12%), and chronic hypertension (0.3%–0.6%)). Third, there is an identified need to understand biological pathways that underlie the causation of preeclampsia in LMICs. Fourth, the Community-Level Interventions for Preeclampsia trials have shown that providing at least eight antenatal contacts, in this case using digital health-supported community health workers, cost-effectively reduces the burden of maternal (by 60%), fetal (60%), and neonatal (40%) mortality. Fifth, what is the utility and cost-effectiveness of routine proteinuria screening of normotensive pregnant women? Sixth, clinical risk factor-based prediction of preeclampsia remains most relevant for most women in LMICs; calcium replacement (≥1 g/day) and low-dose aspirin (100–175 mg/day) are the most useful directly preventative interventions. However, achieving sustainable development goals (SDGs) not directly related to health are more likely to reduce the global burden of preeclampsia and its consequences. Seventh, should a woman develop preeclampsia, personalized maternal time-of-disease risk estimates are available through the PIERS (Preeclampsia Integrated Estimate of RiSk) models, either with (fullPIERS) or without (miniPIERS) access to laboratory testing. Assessment of perinatal risks in LMICs is largely driven by gestational age; however, evidence of significant angiogenic imbalance may identify risk of intrauterine fetal death. Eighth, Control of Hypertension in Pregnancy Study trial data show that women with non-severe pregnancy hypertension (systolic BP 140–159 mmHg or diastolic BP (dBP) 90–109 mmHg) should receive an antihypertensive medication for a target dBP of 85 mmHg. Ninth, for women with severe pregnancy hypertension (systolic BP ≥160 mmHg or dBP ≥110 mmHg), oral antihypertensive management with either nifedipine, labetalol, or, less so, methyldopa will lower BP into the non-severe hypertension range. Tenth, magnesium sulfate remains the sole agent of choice for preventing and treating eclamptic seizures. Eleventh, corticosteroids should be administered to women at risk of delivery <35 +0 weeks’ gestation. Twelfth, although delivery of the placenta initiates resolution of the maternal syndrome of preeclampsia, decisions to initiate delivery should be guided by gestational age and maternal and fetal status. Many women will experience significant postpartum deterioration; delivery should not be equated with “cure”. Thirteenth, whether the development of preeclampsia identifies women at increased risk for early-onset cardiovascular disease in LMICs must be determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".