Prediction of pre-eclampsia at St. Mary’s hospital lacor, a low-resource setting in northern Uganda, a prospective cohort study

BMC Pregnancy and Childbirth, Volume 23, No. 1, Year 2023

Interpretation

Background: Pre-eclampsia is the second leading cause of maternal death in Uganda. However, mothers report to the hospitals late due to health care challenges. Therefore, we developed and validated the prediction models for prenatal screening for pre-eclampsia. Methods: This was a prospective cohort study at St. Mary’s hospital lacor in Gulu city. We included 1,004 pregnant mothers screened at 16–24 weeks (using maternal history, physical examination, uterine artery Doppler indices, and blood tests), followed up, and delivered. We built models in RStudio. Because the incidence of pre-eclampsia was low (4.3%), we generated synthetic balanced data using the ROSE (Random Over and under Sampling Examples) package in RStudio by over-sampling pre-eclampsia and under-sampling non-preeclampsia. As a result, we got 383 (48.8%) and 399 (51.2%) for pre-eclampsia and non-preeclampsia, respectively. Finally, we evaluated the actual model performance against the ROSE-derived synthetic dataset using K-fold cross-validation in RStudio. Results: Maternal history of pre-eclampsia (adjusted odds ratio (aOR) = 32.75, 95% confidence intervals (CI) 6.59—182.05, p = 0.000), serum alkaline phosphatase(ALP) < 98 IU/L (aOR = 7.14, 95% CI 1.76—24.45, p = 0.003), diastolic hypertension ≥ 90 mmHg (aOR = 4.90, 95% CI 1.15—18.01, p = 0.022), bilateral end diastolic notch (aOR = 4.54, 95% CI 1.65—12.20, p = 0.003) and body mass index of ≥ 26.56 kg/m2 (aOR = 3.86, 95% CI 1.25—14.15, p = 0.027) were independent risk factors for pre-eclampsia. Maternal age ≥ 35 years (aOR = 3.88, 95% CI 0.94—15.44, p = 0.056), nulliparity (aOR = 4.25, 95% CI 1.08—20.18, p = 0.051) and white blood cell count ≥ 11,000 (aOR = 8.43, 95% CI 0.92—70.62, p = 0.050) may be risk factors for pre-eclampsia, and lymphocyte count of 800 – 4000 cells/microliter (aOR = 0.29, 95% CI 0.08—1.22, p = 0.074) may be protective against pre-eclampsia. A combination of all the above variables predicted pre-eclampsia with 77.0% accuracy, 80.4% sensitivity, 73.6% specificity, and 84.9% area under the curve (AUC). Conclusion: The predictors of pre-eclampsia were maternal age ≥ 35 years, nulliparity, maternal history of pre-eclampsia, body mass index, diastolic pressure, white blood cell count, lymphocyte count, serum ALP and end-diastolic notch of the uterine arteries. This prediction model can predict pre-eclampsia in prenatal clinics with 77% accuracy. The research was a prospective cohort study at St. Mary's Hospital Lacor. This hospital is a private, not-for-profit hospital founded by the Catholic Church. It is located six kilometres west of Gulu city along Juba Road in Gulu district (Longitude 30 – 32 degrees East and Latitude 02 – 04 degrees North). St. Mary's Hospital Lacor is one of the teaching hospitals of Gulu University with a bed capacity of 482. It is staffed by specialists, medical officers, midwives, nurses, laboratory and radiology staff, and support and administrative staff. The hospital receives about three thousand six hundred antenatal mothers and conducts about six thousand deliveries annually [17]. Some mothers go to the hospital for delivery without prenatal care; others are referred from smaller health units. The mothers pay five thousand Uganda shillings (Ugx 5,000/ =) ($1.5) as the cost per visit. This cost is often waived for most mothers who cannot afford it. Normal labour and delivery cost fifteen thousand (Ugx 15,000/ =) (about $4.50), and Caesarean section costs twenty-five thousand (Ugx 25,000/ =) (about $7.5) Uganda shillings. Using Yamane's 1967 formula [18] for calculating sample size for cohort studies using finite population size: St. Mary's hospital Lacor receives approximately three thousand six hundred antenatal mothers annually. Since my study duration was 24 months, the limited population we could access was about 7,200 mothers. We doubled the sample size to take care of loss to follow-up. We targeted all pregnant women attending antenatal care at St. Mary's Hospital Lacor. In Uganda, the clinical guideline advocates for the first antenatal care to be sought by a pregnant mother up to 20 weeks of gestation [19]. While all expectant mothers attending antenatal care at St. Mary's hospital Lacor were eligible, we included gestational ages of 16 to 24 weeks and those who gave written informed consent to participate in the study. Those whose pregnancies were less than 16 weeks were given a return date for the recruitment, while those above 24 weeks or had molar pregnancies, intrauterine fetal death and anencephaly were excluded. We used consecutive sampling. We informed the mothers about the study during their morning health education meeting given to all mothers on arrival for prenatal care at the hospital. All the women who satisfied the inclusion criteria were approached and requested to provide informed consent. A research assistant administered questionnaire to capture their history and performed a physical examination. Some mothers were asked to give blood samples for full haemogram and liver and renal function tests. A few mothers (after the 1000th mother) did not undergo laboratory tests for logistical reasons. An obstetrician performed the uterine artery Doppler sonography. We recruited 1,285 pregnant mothers at 16–24 weeks from April 2019 to March 2020. All the mothers were of African ancestry at the end of the recruitment period. We followed up with the participants until September 2020. One thousand four (1,004) complete delivery records were obtained at the end of the study period. Seven hundred eighty-two (782) participants had laboratory blood tests (full haemogram, liver and renal function tests) done in addition to blood pressure readings, body mass index calculation and maternal history. Details are in Fig. 1. Flow chart of participants throughout the study The outcome was a combination of a blood pressure ≥ 140/90 mmHg and urine protein ≥ + 1 (pre-eclampsia) by delivery time. The data was pre-processed using Stata® version 15 and built models using RStudio version 4.1.3. Model 1 was built from second-trimester maternal history and physical examination findings, model 2 from the ultrasound and uterine artery Doppler indices, model 3 from a combination of maternal history, physical examination and ultrasound findings, model 4 from maternal laboratory tests, model 5 from a combination of laboratory tests with maternal history, and model 6 from the combination of all models. We included all variables, did a univariate analysis and got unadjusted p-values for every variable collected. Afterwards, we had all variables with p-values ≤ 0.20 or known risk factors for pre-eclampsia in a logistic regression model and removed the non-statistically significant predictors step-wise. Finally, we retained the independent risk factors for pre-eclampsia and used them to build the models of choice, one with the least number of predictors with a higher AUC. Because the incidence of pre-eclampsia was low (4.3%) [20], we generated synthetic balanced data using the ROSE package [21, 22] in RStudio by over-sampling pre-eclampsia and under-sampling non-preeclampsia. We got 383 (48.8%) for those diagnosed with pre-eclampsia and 399 (51.2%) for non-pre-eclampsia. Then, we evaluated the actual model performance against the ROSE-derived synthetic dataset using K-fold cross-validation in RStudio to obtain the AUC's accuracy, sensitivity, specificity and McFadden's pseudo R2. The variables were said to be independent risk factors for pre-eclampsia if their p-value < 0.05 in the model. The models, too, had a good fit if McFadden's value was between 0.2 – 0.4.

AI Digest

Study Justification:

– Pre-eclampsia is the second leading cause of maternal death in Uganda.
– Mothers in Uganda often report to hospitals late due to healthcare challenges.
– The study aimed to develop and validate prediction models for prenatal screening for pre-eclampsia.

Study Highlights:

– Prospective cohort study conducted at St. Mary’s Hospital Lacor in Gulu city, Uganda.
– Included 1,004 pregnant mothers screened at 16-24 weeks of gestation.
– Models were built using maternal history, physical examination, uterine artery Doppler indices, and blood tests.
– Synthetic balanced data was generated to account for the low incidence of pre-eclampsia.
– Independent risk factors for pre-eclampsia were identified, including maternal age, nulliparity, maternal history of pre-eclampsia, body mass index, diastolic pressure, white blood cell count, lymphocyte count, serum alkaline phosphatase, and end-diastolic notch of the uterine arteries.
– The combination of these variables predicted pre-eclampsia with 77.0% accuracy.

Recommendations for Lay Reader and Policy Maker:

– Implement prenatal screening for pre-eclampsia using the identified risk factors.
– Improve access to healthcare for pregnant women to ensure early detection and management of pre-eclampsia.
– Provide education and awareness programs to pregnant women about the importance of prenatal care and early detection of pre-eclampsia.
– Allocate resources for training healthcare professionals on the use of the prediction models and appropriate management of pre-eclampsia.

Key Role Players:

– Specialists, medical officers, midwives, nurses, laboratory and radiology staff, and support and administrative staff at St. Mary’s Hospital Lacor.
– Gulu University for collaboration and support in implementing the recommendations.
– Ministry of Health in Uganda for policy guidance and resource allocation.

Cost Items for Planning Recommendations:

– Training programs for healthcare professionals on the use of prediction models and pre-eclampsia management.
– Development and implementation of education and awareness programs for pregnant women.
– Resources for prenatal screening, including equipment, supplies, and laboratory tests.
– Infrastructure improvements to enhance access to healthcare services for pregnant women.
– Monitoring and evaluation of the implementation of the recommendations.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study design is a prospective cohort study, which is generally considered to provide reliable evidence. The sample size is adequate, and the study includes a diverse range of variables. However, there are a few limitations to consider. First, the study was conducted in a single hospital, which may limit the generalizability of the findings. Second, the authors used synthetic balanced data to address the low incidence of pre-eclampsia, which may introduce bias. Third, the statistical analysis could be improved by using more advanced techniques, such as multivariable regression. To improve the strength of the evidence, future studies could consider conducting multicenter studies to increase generalizability, using larger sample sizes, and employing more robust statistical methods.

Abstract

The research was a prospective cohort study at St. Mary's Hospital Lacor. This hospital is a private, not-for-profit hospital founded by the Catholic Church. It is located six kilometres west of Gulu city along Juba Road in Gulu district (Longitude 30 – 32 degrees East and Latitude 02 – 04 degrees North). St. Mary's Hospital Lacor is one of the teaching hospitals of Gulu University with a bed capacity of 482. It is staffed by specialists, medical officers, midwives, nurses, laboratory and radiology staff, and support and administrative staff. The hospital receives about three thousand six hundred antenatal mothers and conducts about six thousand deliveries annually [17]. Some mothers go to the hospital for delivery without prenatal care; others are referred from smaller health units. The mothers pay five thousand Uganda shillings (Ugx 5,000/ =) ($1.5) as the cost per visit. This cost is often waived for most mothers who cannot afford it. Normal labour and delivery cost fifteen thousand (Ugx 15,000/ =) (about $4.50), and Caesarean section costs twenty-five thousand (Ugx 25,000/ =) (about $7.5) Uganda shillings. Using Yamane's 1967 formula [18] for calculating sample size for cohort studies using finite population size: St. Mary's hospital Lacor receives approximately three thousand six hundred antenatal mothers annually. Since my study duration was 24 months, the limited population we could access was about 7,200 mothers. We doubled the sample size to take care of loss to follow-up. We targeted all pregnant women attending antenatal care at St. Mary's Hospital Lacor. In Uganda, the clinical guideline advocates for the first antenatal care to be sought by a pregnant mother up to 20 weeks of gestation [19]. While all expectant mothers attending antenatal care at St. Mary's hospital Lacor were eligible, we included gestational ages of 16 to 24 weeks and those who gave written informed consent to participate in the study. Those whose pregnancies were less than 16 weeks were given a return date for the recruitment, while those above 24 weeks or had molar pregnancies, intrauterine fetal death and anencephaly were excluded. We used consecutive sampling. We informed the mothers about the study during their morning health education meeting given to all mothers on arrival for prenatal care at the hospital. All the women who satisfied the inclusion criteria were approached and requested to provide informed consent. A research assistant administered questionnaire to capture their history and performed a physical examination. Some mothers were asked to give blood samples for full haemogram and liver and renal function tests. A few mothers (after the 1000th mother) did not undergo laboratory tests for logistical reasons. An obstetrician performed the uterine artery Doppler sonography. We recruited 1,285 pregnant mothers at 16–24 weeks from April 2019 to March 2020. All the mothers were of African ancestry at the end of the recruitment period. We followed up with the participants until September 2020. One thousand four (1,004) complete delivery records were obtained at the end of the study period. Seven hundred eighty-two (782) participants had laboratory blood tests (full haemogram, liver and renal function tests) done in addition to blood pressure readings, body mass index calculation and maternal history. Details are in Fig. 1. Flow chart of participants throughout the study The outcome was a combination of a blood pressure ≥ 140/90 mmHg and urine protein ≥ + 1 (pre-eclampsia) by delivery time. The data was pre-processed using Stata® version 15 and built models using RStudio version 4.1.3. Model 1 was built from second-trimester maternal history and physical examination findings, model 2 from the ultrasound and uterine artery Doppler indices, model 3 from a combination of maternal history, physical examination and ultrasound findings, model 4 from maternal laboratory tests, model 5 from a combination of laboratory tests with maternal history, and model 6 from the combination of all models. We included all variables, did a univariate analysis and got unadjusted p-values for every variable collected. Afterwards, we had all variables with p-values ≤ 0.20 or known risk factors for pre-eclampsia in a logistic regression model and removed the non-statistically significant predictors step-wise. Finally, we retained the independent risk factors for pre-eclampsia and used them to build the models of choice, one with the least number of predictors with a higher AUC. Because the incidence of pre-eclampsia was low (4.3%) [20], we generated synthetic balanced data using the ROSE package [21, 22] in RStudio by over-sampling pre-eclampsia and under-sampling non-preeclampsia. We got 383 (48.8%) for those diagnosed with pre-eclampsia and 399 (51.2%) for non-pre-eclampsia. Then, we evaluated the actual model performance against the ROSE-derived synthetic dataset using K-fold cross-validation in RStudio to obtain the AUC's accuracy, sensitivity, specificity and McFadden's pseudo R2. The variables were said to be independent risk factors for pre-eclampsia if their p-value < 0.05 in the model. The models, too, had a good fit if McFadden's value was between 0.2 – 0.4.

Materials

N/A

Innovations

The recommendation from the study is to use prediction models for prenatal screening to improve access to maternal health, specifically for the early detection of pre-eclampsia. Pre-eclampsia is a leading cause of maternal death in Uganda, and mothers often report to hospitals late due to healthcare challenges. The prediction models were developed and validated in a low-resource setting at St. Mary’s Hospital Lacor in northern Uganda.

The study used various factors such as maternal history, physical examination, uterine artery Doppler indices, and blood tests to predict the risk of pre-eclampsia. The models showed promising results, with a combination of all the variables predicting pre-eclampsia with 77% accuracy, 80.4% sensitivity, 73.6% specificity, and 84.9% area under the curve (AUC).

Implementing these prediction models in prenatal clinics can help healthcare providers identify pregnant women at risk of pre-eclampsia earlier, allowing for timely interventions and improved maternal outcomes. By identifying high-risk pregnancies, healthcare resources can be allocated more efficiently, and appropriate care can be provided to those who need it the most.

This innovation can contribute to improving access to maternal health by ensuring that pregnant women receive the necessary care and interventions in a timely manner, reducing the risk of complications and maternal mortality associated with pre-eclampsia.

AI Innovations Description

This innovation can contribute to improving access to maternal health by ensuring that pregnant women receive the necessary care and interventions in a timely manner, reducing the risk of complications and maternal mortality associated with pre-eclampsia.

AI Innovations Methodology

To simulate the impact of the main recommendations of this abstract on improving access to maternal health, you could consider the following methodology:

1. Identify target prenatal clinics: Select a representative sample of prenatal clinics in Uganda, particularly those in low-resource settings where access to maternal health is a challenge. These clinics should have a similar patient population as St. Mary’s Hospital Lacor.

2. Training and implementation: Provide training to healthcare providers at the selected clinics on the use of prediction models for prenatal screening. This training should include instructions on how to collect and interpret the various factors used in the models, such as maternal history, physical examination, uterine artery Doppler indices, and blood tests.

3. Data collection: Implement the use of prediction models in the selected clinics. Healthcare providers should collect the necessary data from pregnant women attending antenatal care, including the factors used in the models.

4. Model application: Apply the prediction models to the collected data to assess the risk of pre-eclampsia for each pregnant woman. The models should provide a risk score or probability for each patient.

5. Risk stratification: Stratify pregnant women into low, moderate, and high-risk categories based on the risk scores obtained from the models. This stratification will help healthcare providers prioritize resources and interventions for those at higher risk.

6. Timely interventions: Based on the risk stratification, healthcare providers should implement appropriate interventions for pregnant women at higher risk of pre-eclampsia. These interventions may include closer monitoring, more frequent prenatal visits, specialized care, and early referral to higher-level healthcare facilities if necessary.

7. Monitoring and evaluation: Continuously monitor the implementation of the prediction models and the impact on improving access to maternal health. Collect data on the number of pregnant women identified as high-risk, the interventions provided, and the outcomes of those interventions.

8. Assess outcomes: Evaluate the impact of implementing the prediction models on maternal health outcomes, such as the incidence of pre-eclampsia, maternal mortality, and other complications related to pre-eclampsia. Compare these outcomes with historical data or a control group to assess the effectiveness of the prediction models in improving access to maternal health.

9. Feedback and improvement: Regularly communicate with healthcare providers to gather feedback on the use of the prediction models. Identify any challenges or areas for improvement and make necessary adjustments to optimize the implementation and effectiveness of the models.

10. Scale-up and dissemination: If the simulation demonstrates positive results in improving access to maternal health, consider scaling up the implementation of the prediction models to more prenatal clinics in Uganda. Additionally, share the findings and lessons learned from the simulation with relevant stakeholders, such as policymakers, healthcare organizations, and researchers, to promote wider adoption and dissemination of this approach.

By following this methodology, you can assess the impact of implementing prediction models for prenatal screening on improving access to maternal health in low-resource settings, specifically for the early detection of pre-eclampsia.

Author

Dima Health

Identifiers:

DOI: 10.1186/s12884-023-05420-z

Research Areas:

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

Study Countries:

Uganda

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female