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

Research

Christopher Garimoi Orach¹  Benard Abola²  Silvia Awor³  Dan Kabonge Kaye⁴  Annettee Nakimuli⁴  Paul Kiondo⁴  Jasper Ogwal-Okeng⁵  Rosemary Byanyima⁶ 

[1] Department of Community Health, School of Public Health, College of Health Sciences, Makerere University, Kampala City, Uganda;Department of Mathematics, Faculty of Science, Gulu University, P.O.Box 166, Gulu, Uganda;Department of Obstetrics and Gynaecology, Faculty of Medicine, Gulu University, P.O.Box 166, Gulu, Uganda;Department of Obstetrics and Gynaecology, School of Medicine, College of Health Sciences, Makerere University, P.O.Box 7062, Kampala, Uganda;Department of Pharmacology, Lira University, Lira, Uganda;Department of Radiology, Mulago National Referral Hospital, PO Box 7051, Kampala, Uganda;

关键词: Risk prediction;    Uterine artery Doppler indices;    Maternal history;    Blood tests;    Pre-eclampsia;    Uganda;    Africa;   

DOI  :  10.1186/s12884-023-05420-z

 received in 2022-08-23, accepted in 2023-02-01,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundPre-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.MethodsThis 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.ResultsMaternal 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).ConclusionThe 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.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 3