Achieving Optimal Gestational Weight Gain, Birth Weight, and Perinatal Outcomes Among Pregnant Women at Risk of Hypertension: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Hypertensive disorders, including preeclampsia, complicate 10% of all pregnancies, causing maternal and fetal morbidity and mortality. In Bangladesh, 24% of all maternal deaths are directly attributed to hypertensive disorders. Conventional antenatal care practices often delay or miss detecting hypertensive disorders in pregnancy, which may allow some women to become vulnerable to the adverse consequences of the hypertensive disorders. Regular self-monitoring of blood pressure and weight gain may improve maternal and fetal outcomes among pregnant women at risk of developing hypertensive disorders during pregnancy through early diagnosis, prompt referral, and timely clinical management; however, to undertake a randomized controlled trial of an intervention to reduce adverse consequences of hypertensive disorders in pregnancy, its feasibility must first be determined. OBJECTIVE: The objectives of this study are to evaluate the accuracy of a wearable blood pressure monitoring device (Health Gauge) in order to test the design and methods of a future definitive randomized controlled trial, and to examine the feasibility, acceptability, and fidelity of an intervention focusing on regular monitoring of weight gain and self-monitoring of blood pressure for pregnant women at risk of developing hypertensive disorders and their associated complications. METHODS: The study is located in Matlab, Bangladesh will be conducted in two phases. First, a wearable blood pressure device (Health Gauge) will be validated in accordance with the European Society of Hypertension International Protocol (revision 2010). Second, a prospective, two-arm, parallel, and nonblinded randomized controlled external pilot trial will be conducted. In the pilot trial, 70 eligible participants will be individually randomized to the intervention arm, in which pregnant women will self-monitor their blood pressure daily using a wearable device (Health Gauge) and be evaluated monthly by trained health workers for weight gain from 20 weeks of gestation until delivery, or the control arm, in which pregnant women will be assessed for weight gain every two months from 20 weeks of gestation until delivery (1:1 intervention to control allocation ratio using a permuted block randomization method with concealment). All women will receive standard antenatal care. RESULTS: A validation study of the wearable blood pressure device has successfully been conducted among the general adult population in Matlab, Bangladesh. As of September 2019, the pilot trial has completed enrollment of women who are pregnant (N=70; intervention: n=35; control: n=35) and follow-up of the participants is ongoing. Data analysis is expected to be completed by June 2020, and results are expected to be submitted for publication in August 2020. CONCLUSIONS: The findings of this study will help us to design a comprehensive, full-scale randomized controlled trial to test the efficacy of regular self-monitoring of blood pressure and weight gain during pregnancy, a simple and inexpensive intervention to help to achieve optimal maternal and fetal outcomes in pregnant women at risk of developing hypertensive disorders and their associated complications during pregnancy. TRIAL REGISTRATION: ClinicalTrials.gov NCT03858595; https://clinicaltrials.gov/ct2/show/NCT03858595. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16676.
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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 | 0.055 | 0.046 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.069 | 0.011 |
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".