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

Article scientifique 2023 Anglais

Résumé

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: (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.

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Awor, S., Abola, B., Byanyima, R., Orach, C., Kiondo, P., Kaye, D., Ogwal‐Okeng, J., Nakimuli, A. (2023). Prediction of pre-eclampsia at St. Mary's hospital lacor, a low-resource setting in northern Uganda, a prospective cohort study. https://doi.org/10.1186/s12884-023-05420-z

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Auteur(s)

Silvia Awor Benard Abola Rosemary Byanyima Christopher Garimoi Orach Paul Kiondo Dan K. Kaye Jasper Ogwal‐Okeng Annettee Nakimuli

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