Pregnancy outcomes and blood pressure visit-to-visit variability and level in three less-developed countries

Hypertension, Volume 77, No. 5, Year 2021

Interpretation

In pregnancy in well-resourced settings, limited data suggest that higher blood pressure (BP) visit-to-visit variability may be associated with adverse pregnancy outcomes. Included were pregnant women in 22 intervention clusters of the CLIP (Community- Level Interventions for Preeclampsia) cluster randomized trials, who had received at least 2 prenatal contacts from a community health worker, including standardized BP measurement. Mixed-effects adjusted logistic regression assessed relationships between pregnancy outcomes and both BP level (median [interquartile range]) and visit-to-visit variability (SD and average real variability [ARV], adjusted for BP level), among all women and those who became hypertensive. The primary outcome was the CLIP composite of maternal and perinatal mortality and morbidity. Among 17 770 pregnancies, higher systolic and diastolic BP levels were associated with increased odds of the composite outcome per 5 mm Hg increase in BP (odds ratio [OR], 1.05 [95% CI, 1.03-1.07] and OR, 1.08 [1.06-1.11], respectively). Higher BP visit-to-visit variability was associated with increased odds, per a SD increase in BP variability measure, of (1) hypertension (systolic: OR, 2.09 [1.98-2.21] for SD and 1.52 [1.45-1.60] for ARV; diastolic: OR, 2.70 [2.54-2.87] for SD and 1.86 [1.76-1.96] for ARV); and (2) the composite outcome (systolic: OR, 1.10 [1.06-1.14] for SD and 1.06 [1.02-1.10] for ARV; diastolic: OR, 1.07 [1.03-1.11] for SD and 1.06 [1.02-1.09] for ARV). In 3 less-developed countries, higher BP level and visit-to-visit variability predicted adverse pregnancy outcomes, providing an opportunity for high-definition medicine. This was an unplanned secondary analysis of data from the 22 intervention clusters of the CLIP cluster randomized trials, aimed at externally validating findings from the CHIPS trial.12 Data can be accessed through the CLIP trials data access committee (Text S1 in the Data Supplement). The CLIP trials were conducted in 2014 to 2017 in India (N=6 intervention clusters), Pakistan (N=10), and Mozambique (N=6).13–16 The unit of randomization (cluster) was the local administrative unit. All pregnant women aged 15 to 49 years (12–49 years in Mozambique) were identified in their community by trained community health workers. All women provided written informed consent to participate. The trial was unmasked given the nature of the intervention, aimed at addressing the 3 delays in triage, transport, and treatment related to preeclampsia. First, community engagement addressed barriers and facilitators to accessing care. Second, existing cadres of community health workers were trained to task-share pregnancy hypertension-oriented care at CLIP contacts in women’s homes, using the CLIP PIERS (Preeclampsia Integrated Estimate of Risk Score) on-the-Move (POM) digital health application for risk stratification.17 Community health workers (1) responded to emergency conditions, if relevant; (2) took women’s BP and assessed dipstick proteinuria at the first and any hypertensive contact; (3) administered oral methyldopa 750 mg for BP of at least 160/110 mm Hg; (4) administered 10 g intramuscular magnesium sulfate for suspected severe preeclampsia; and (5) and referred to a comprehensive emergency obstetric care facility. Standardized BP measurement by trained community health workers used a semiautomated pregnancy- and preeclampsia-validated oscillometric device (Microlife 3AS1-2).18 Having rested for 5 minutes, women’s BP was measured at least twice; all readings were entered into the POM application, which averaged the first and second readings and if they differed by >10 mm Hg, a third reading was requested and the second and third readings averaged. The planned frequency of prenatal POM-guided CLIP contacts was every 4 weeks, at minimum. Trained surveillance teams conducted regular surveys of households (every 3–6 months), except in India where a prospective population-based surveillance system was established. The primary outcome was a composite of all-cause maternal and perinatal mortality and morbidity. Maternal death or morbidity occurred during or within 42 days of pregnancy; morbidity was one or more life-threatening complications of pregnancy, defined as a serious end-organ complication of preeclampsia (eg, eclampsia), another major cause of maternal mortality/morbidity (ie, obstetric sepsis or vaginal fistula), or receipt of a life-saving intervention. Perinatal death was stillbirth, early or late neonatal mortality, and morbidity a composite of problems that could be ascertained in community (eg, seizure or feeding difficulty). For detailed definitions, see Table S2 in the Data Supplement. Overall coordination and data management were by the Preeclampsia–Eclampsia Monitoring, Prevention and Treatment research group at the University of British Columbia, Canada. Ethical approvals were granted by the University of British Columbia (H12-03497) and relevant in-country research ethics boards (Aga Khan University, Pakistan, 2590-Obs-ERC-13; KLE University, India, MDC/IECHSR/2011-12/A-4, ICMR 5/7/859/12-RHN; and Centro de Investigação em Saúde de Manhiça (CIBS-CISM/038/14) and Mozambique National Bioethic Committee (219/CNBS/14). The trials are registered at URL: https://www.clinicaltrials.gov (Unique identifier: {“type”:”clinical-trial”,”attrs”:{“text”:”NCT01911494″,”term_id”:”NCT01911494″}}NCT01911494) and the related individual participant data meta-analysis on PROSPERO (CRD42018102564). We included CLIP participants in pregnancy, from enrollment until follow-up for the CLIP primary outcome, who had at least 2 antenatal contacts by community health workers (prerequisite for determining BP variability, see below). Mean systolic and diastolic BP levels were the mean of relevant values taken at all POM-guided CLIP contacts between enrollment and delivery. Within-participant visit-to-visit BP variability was assessed using all POM-guided CLIP contacts after enrollment until delivery. We evaluated 2 measures of BP visit-to-visit variability used outside pregnancy: (1) within-participant SD to reflect dispersion of measurements around mean BP and (2) average real variability (ARV) as the average of the absolute successive difference of all BP values, reflecting changes over short time intervals (so a decrease by 4 mm Hg and then an increase by 6 mm Hg would represent an ARV of 5). We adjusted for mean BP level, as higher levels are associated with more variability. Any correlation was explored between BP variability and number of measurements. In our primary analysis, relationships were explored between each major CLIP outcome and both BP level and visit-to-visit variability, using values before the outcomes: progression to hypertension (systolic BP of at least 140 mm Hg or a diastolic BP of at least 90 mm Hg, based on an average of 2 measurements), composite of maternal or perinatal mortality or morbidity (primary outcome), composite maternal outcome (mortality or morbidity) and composite perinatal outcome (stillbirth, early or late neonatal death, or neonatal morbidity) to evaluate whether the direction of effect on maternal outcomes was the same. In addition, we further examined the relationship between each major CLIP outcome and BP variability only among women who became hypertensive to see if BP variability could add further information to BP level. Data were summarized as median and interquartile range and counts (percentages) for continuous and categorical variables, respectively. The mean BP level-outcome relationship was explored by mixed-effects logistic regression. Adjustment was made for country and cluster (each as a random effect), maternal age at enrollment, maternal education, parity, and gestational age at enrollment. The odds ratio (OR) for each outcome was calculated per 5 mm Hg increase in mean BP from enrollment until delivery. The BP variability-outcome relationship was evaluated for all women, and specifically for women who developed pregnancy hypertension, by mixed-effects logistic regression, adjusted for average BP level (defined as the mean of the BP readings used to define visit-to-visit variability) and the variables described above for BP level analyses. The change in the scale of the OR was calculated per SD increase in both metrics of BP variability to compare the relative importance of one measure with another. Correlation between BP visit-to-visit variability and the number of measurements was assessed by Spearman correlation (r). In sensitivity analyses: (1) for all outcomes, we excluded BP values within 7, 14, 21, and 28 days before delivery to minimize the extent to which BP variability may be an artifact of the outcomes themselves (ie, reverse causality); (2) for all outcomes, we added further adjustment for the final antenatal BP measurement, to account for BP trajectory; (3) for all outcomes, we excluded repeat pregnancies for the same woman; and (4) for progression to hypertension, we incorporated diagnoses based only on trial surveillance data for women who became hypertensive after their last POM-guided visit. A P<0.05 was considered statistically significant, without adjustment for multiple comparisons. All statistical analyses were performed using and R 3.5.3 (R Development Core Team, Vienna, Austria). J. Bone had access to all data and takes responsibility for its integrity and the data analysis.

AI Digest

Study Justification:

The study aimed to investigate the association between blood pressure (BP) visit-to-visit variability and level during pregnancy and adverse pregnancy outcomes in three less-developed countries. This research was conducted to validate previous findings and provide insights into the potential impact of BP variability on pregnancy outcomes in resource-limited settings.

Study Highlights:

– The study included pregnant women from 22 intervention clusters in India, Pakistan, and Mozambique.
– Data was collected through the CLIP (Community-Level Interventions for Preeclampsia) cluster randomized trials.
– The primary outcome was a composite of maternal and perinatal mortality and morbidity.
– Higher systolic and diastolic BP levels were associated with increased odds of adverse pregnancy outcomes.
– Higher BP visit-to-visit variability was also associated with increased odds of hypertension and the composite outcome.
– The findings suggest that BP level and variability can predict adverse pregnancy outcomes in less-developed countries.

Recommendations for Lay Readers:

– Pregnant women should be aware of the importance of maintaining healthy blood pressure levels during pregnancy.
– Regular monitoring of blood pressure is crucial to identify any potential risks and take appropriate measures.
– Healthcare providers should consider the variability of blood pressure readings as an additional factor in assessing pregnancy outcomes.
– Further research is needed to explore interventions that can help manage blood pressure variability and improve pregnancy outcomes in resource-limited settings.

Recommendations for Policy Makers:

– Policies should be implemented to ensure access to regular prenatal care and standardized blood pressure measurement for pregnant women in less-developed countries.
– Training programs should be established to educate healthcare workers on the importance of monitoring blood pressure variability and its impact on pregnancy outcomes.
– Investment in digital health applications and technologies can facilitate accurate and efficient blood pressure monitoring in community settings.
– Collaborative efforts between healthcare providers, researchers, and policymakers are needed to develop and implement interventions that can reduce adverse pregnancy outcomes associated with blood pressure variability.

Key Role Players:

– Community health workers: Trained individuals responsible for conducting prenatal visits, measuring blood pressure, and providing basic care.
– Healthcare providers: Medical professionals involved in the management and treatment of pregnant women, including obstetricians, midwives, and nurses.
– Researchers: Individuals conducting studies and analyzing data to generate evidence-based recommendations.
– Policy makers: Government officials and organizations responsible for developing and implementing healthcare policies and guidelines.
– Community engagement teams: Groups working to address barriers and facilitate access to care for pregnant women.

Cost Items for Planning Recommendations:

– Training programs for community health workers and healthcare providers.
– Procurement of semiautomated pregnancy-validated oscillometric devices for blood pressure measurement.
– Development and implementation of digital health applications for risk stratification and data management.
– Research funding for data collection, analysis, and dissemination.
– Community engagement initiatives to raise awareness and promote access to care.
– Infrastructure and resources for comprehensive emergency obstetric care facilities.
– Monitoring and evaluation systems to assess the effectiveness of interventions.
Please note that the provided cost items are general suggestions and may vary depending on the specific context and requirements of each country or region.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides detailed information about the study design, methodology, and statistical analysis. However, it does not mention the sample size or provide specific results for each outcome. To improve the evidence, the abstract could include the sample size and summarize the main findings for each outcome measure.

Abstract

This was an unplanned secondary analysis of data from the 22 intervention clusters of the CLIP cluster randomized trials, aimed at externally validating findings from the CHIPS trial.12 Data can be accessed through the CLIP trials data access committee (Text S1 in the Data Supplement). The CLIP trials were conducted in 2014 to 2017 in India (N=6 intervention clusters), Pakistan (N=10), and Mozambique (N=6).13–16 The unit of randomization (cluster) was the local administrative unit. All pregnant women aged 15 to 49 years (12–49 years in Mozambique) were identified in their community by trained community health workers. All women provided written informed consent to participate. The trial was unmasked given the nature of the intervention, aimed at addressing the 3 delays in triage, transport, and treatment related to preeclampsia. First, community engagement addressed barriers and facilitators to accessing care. Second, existing cadres of community health workers were trained to task-share pregnancy hypertension-oriented care at CLIP contacts in women’s homes, using the CLIP PIERS (Preeclampsia Integrated Estimate of Risk Score) on-the-Move (POM) digital health application for risk stratification.17 Community health workers (1) responded to emergency conditions, if relevant; (2) took women’s BP and assessed dipstick proteinuria at the first and any hypertensive contact; (3) administered oral methyldopa 750 mg for BP of at least 160/110 mm Hg; (4) administered 10 g intramuscular magnesium sulfate for suspected severe preeclampsia; and (5) and referred to a comprehensive emergency obstetric care facility. Standardized BP measurement by trained community health workers used a semiautomated pregnancy- and preeclampsia-validated oscillometric device (Microlife 3AS1-2).18 Having rested for 5 minutes, women’s BP was measured at least twice; all readings were entered into the POM application, which averaged the first and second readings and if they differed by >10 mm Hg, a third reading was requested and the second and third readings averaged. The planned frequency of prenatal POM-guided CLIP contacts was every 4 weeks, at minimum. Trained surveillance teams conducted regular surveys of households (every 3–6 months), except in India where a prospective population-based surveillance system was established. The primary outcome was a composite of all-cause maternal and perinatal mortality and morbidity. Maternal death or morbidity occurred during or within 42 days of pregnancy; morbidity was one or more life-threatening complications of pregnancy, defined as a serious end-organ complication of preeclampsia (eg, eclampsia), another major cause of maternal mortality/morbidity (ie, obstetric sepsis or vaginal fistula), or receipt of a life-saving intervention. Perinatal death was stillbirth, early or late neonatal mortality, and morbidity a composite of problems that could be ascertained in community (eg, seizure or feeding difficulty). For detailed definitions, see Table S2 in the Data Supplement. Overall coordination and data management were by the Preeclampsia–Eclampsia Monitoring, Prevention and Treatment research group at the University of British Columbia, Canada. Ethical approvals were granted by the University of British Columbia (H12-03497) and relevant in-country research ethics boards (Aga Khan University, Pakistan, 2590-Obs-ERC-13; KLE University, India, MDC/IECHSR/2011-12/A-4, ICMR 5/7/859/12-RHN; and Centro de Investigação em Saúde de Manhiça (CIBS-CISM/038/14) and Mozambique National Bioethic Committee (219/CNBS/14). The trials are registered at URL: https://www.clinicaltrials.gov (Unique identifier: {“type”:”clinical-trial”,”attrs”:{“text”:”NCT01911494″,”term_id”:”NCT01911494″}}NCT01911494) and the related individual participant data meta-analysis on PROSPERO (CRD42018102564). We included CLIP participants in pregnancy, from enrollment until follow-up for the CLIP primary outcome, who had at least 2 antenatal contacts by community health workers (prerequisite for determining BP variability, see below). Mean systolic and diastolic BP levels were the mean of relevant values taken at all POM-guided CLIP contacts between enrollment and delivery. Within-participant visit-to-visit BP variability was assessed using all POM-guided CLIP contacts after enrollment until delivery. We evaluated 2 measures of BP visit-to-visit variability used outside pregnancy: (1) within-participant SD to reflect dispersion of measurements around mean BP and (2) average real variability (ARV) as the average of the absolute successive difference of all BP values, reflecting changes over short time intervals (so a decrease by 4 mm Hg and then an increase by 6 mm Hg would represent an ARV of 5). We adjusted for mean BP level, as higher levels are associated with more variability. Any correlation was explored between BP variability and number of measurements. In our primary analysis, relationships were explored between each major CLIP outcome and both BP level and visit-to-visit variability, using values before the outcomes: progression to hypertension (systolic BP of at least 140 mm Hg or a diastolic BP of at least 90 mm Hg, based on an average of 2 measurements), composite of maternal or perinatal mortality or morbidity (primary outcome), composite maternal outcome (mortality or morbidity) and composite perinatal outcome (stillbirth, early or late neonatal death, or neonatal morbidity) to evaluate whether the direction of effect on maternal outcomes was the same. In addition, we further examined the relationship between each major CLIP outcome and BP variability only among women who became hypertensive to see if BP variability could add further information to BP level. Data were summarized as median and interquartile range and counts (percentages) for continuous and categorical variables, respectively. The mean BP level-outcome relationship was explored by mixed-effects logistic regression. Adjustment was made for country and cluster (each as a random effect), maternal age at enrollment, maternal education, parity, and gestational age at enrollment. The odds ratio (OR) for each outcome was calculated per 5 mm Hg increase in mean BP from enrollment until delivery. The BP variability-outcome relationship was evaluated for all women, and specifically for women who developed pregnancy hypertension, by mixed-effects logistic regression, adjusted for average BP level (defined as the mean of the BP readings used to define visit-to-visit variability) and the variables described above for BP level analyses. The change in the scale of the OR was calculated per SD increase in both metrics of BP variability to compare the relative importance of one measure with another. Correlation between BP visit-to-visit variability and the number of measurements was assessed by Spearman correlation (r). In sensitivity analyses: (1) for all outcomes, we excluded BP values within 7, 14, 21, and 28 days before delivery to minimize the extent to which BP variability may be an artifact of the outcomes themselves (ie, reverse causality); (2) for all outcomes, we added further adjustment for the final antenatal BP measurement, to account for BP trajectory; (3) for all outcomes, we excluded repeat pregnancies for the same woman; and (4) for progression to hypertension, we incorporated diagnoses based only on trial surveillance data for women who became hypertensive after their last POM-guided visit. A P<0.05 was considered statistically significant, without adjustment for multiple comparisons. All statistical analyses were performed using and R 3.5.3 (R Development Core Team, Vienna, Austria). J. Bone had access to all data and takes responsibility for its integrity and the data analysis.

Materials

https://efashare.b-cdn.net/materials/74b5ed0f854e4a83dd2c1ed7f870a4e3f27feaa4eff4f852325dfecd0db22048/hyp-77-1714-s001.pdf

Innovations

Based on the provided description, it seems that the study is focused on exploring the relationship between blood pressure (BP) visit-to-visit variability and pregnancy outcomes in less-developed countries. The study aims to validate findings from previous trials and identify potential opportunities for improvement in maternal health.

While the description does not explicitly mention innovations, here are some potential recommendations that could be considered to improve access to maternal health based on the study’s findings:

1. Mobile Health (mHealth) Applications: Develop and implement mobile health applications that can be used by community health workers to track and monitor blood pressure levels during pregnancy. These applications can provide real-time feedback and reminders for pregnant women to attend prenatal visits and adhere to recommended care plans.

2. Telemedicine: Utilize telemedicine technologies to enable remote consultations between pregnant women and healthcare providers. This can help overcome geographical barriers and improve access to specialized care, especially in rural areas where healthcare facilities may be limited.

3. Training and Capacity Building: Invest in training and capacity building programs for community health workers to enhance their knowledge and skills in managing hypertension during pregnancy. This can include training on accurate blood pressure measurement techniques, identification of high-risk pregnancies, and appropriate referral pathways.

4. Community Engagement and Education: Implement community engagement and education programs to raise awareness about the importance of regular prenatal care and the management of hypertension during pregnancy. This can involve community workshops, health campaigns, and the distribution of educational materials in local languages.

5. Task-Sharing and Collaborative Care: Foster collaboration between community health workers and healthcare providers to ensure a continuum of care for pregnant women. This can involve task-sharing models where community health workers work closely with healthcare providers to monitor blood pressure, provide basic care, and facilitate referrals when necessary.

6. Integration of Digital Health Technologies: Integrate digital health technologies, such as electronic health records and data analytics, to improve the collection, analysis, and sharing of maternal health data. This can help identify trends, monitor outcomes, and inform evidence-based decision-making for improved maternal health services.

It is important to note that these recommendations are based on the general understanding of the study’s focus on blood pressure variability and pregnancy outcomes. Further research and consultation with experts in the field would be necessary to develop specific and contextually appropriate innovations for improving access to maternal health in less-developed countries.

AI Innovations Description

Based on the provided description, the recommendation to improve access to maternal health would be to implement a high-definition medicine approach. This approach involves monitoring blood pressure (BP) visit-to-visit variability and level in pregnant women in less-developed countries. By using standardized BP measurement and digital health applications, community health workers can identify women at risk of adverse pregnancy outcomes, such as preeclampsia. The community health workers can then provide appropriate interventions, including medication administration and referral to emergency obstetric care facilities. This approach has been shown to be effective in predicting and preventing adverse pregnancy outcomes in well-resourced settings and can be adapted to improve access to maternal health in less-developed countries.

AI Innovations Methodology

Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Strengthen Community-Based Care: Expand the role of community health workers in providing prenatal care and monitoring blood pressure levels of pregnant women. This can be done by training and equipping community health workers to conduct regular visits and provide necessary interventions.

2. Digital Health Applications: Utilize digital health applications, such as the CLIP PIERS on-the-Move (POM) application mentioned in the description, to improve risk stratification and monitoring of pregnant women. These applications can help in early identification of high-risk cases and facilitate timely interventions.

3. Task-Sharing and Training: Implement task-sharing strategies to train existing cadres of healthcare workers to provide pregnancy hypertension-oriented care. This can help in addressing the shortage of skilled healthcare professionals and ensure that pregnant women receive appropriate care.

4. Community Engagement: Engage with the community to identify and address barriers to accessing maternal health services. This can involve raising awareness, addressing cultural beliefs, and improving transportation and referral systems.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the Simulation Parameters: Determine the specific variables and indicators that will be used to measure the impact of the recommendations. This could include indicators such as the number of prenatal visits, blood pressure control rates, maternal and perinatal mortality rates, and access to emergency obstetric care.

2. Collect Baseline Data: Gather data on the current state of maternal health access and outcomes in the target population. This can include data on healthcare infrastructure, availability of trained healthcare workers, maternal health indicators, and access to prenatal care.

3. Develop a Simulation Model: Create a mathematical or computational model that simulates the impact of the recommendations on the defined indicators. This model should take into account factors such as population size, geographical distribution, and healthcare resources.

4. Input Data and Run Simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. This can involve varying parameters such as the coverage of community-based care, the effectiveness of digital health applications, and the level of community engagement.

5. Analyze Results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health. This can include assessing changes in maternal and perinatal mortality rates, improvements in blood pressure control, and increased utilization of prenatal care services.

6. Refine and Validate the Model: Refine the simulation model based on the analysis of results and feedback from experts in the field. Validate the model by comparing the simulated outcomes with real-world data, if available.

7. Communicate Findings: Present the findings of the simulation study to relevant stakeholders, policymakers, and healthcare providers. Use the results to advocate for the implementation of the recommended interventions and to guide decision-making in improving access to maternal health.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data.

Author

Dima Health

Identifiers:

DOI: 10.1161/HYPERTENSIONAHA.120.16851

ISSN: 0194911X

Research Areas:

Community Interventions, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Technology and Innovations, Workforce

Study Countries:

Mozambique

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study, randomised-control-trial

Study Approach:

Systematic Review

Participants Gender:

Female