Conference abstract
Socio-economic and anthropometric risk prediction factors of hypertensive disorders in pregnant women at the District Hospital of Dschang, Cameroon
Pan African Medical Journal - Conference Proceedings. 2023:18(56).03
Oct 2023.
doi: 10.11604/pamj-cp.2023.18.56.2001
Archived on: 03 Oct 2023
Contact the corresponding author
Keywords: Hypertension, pregnant women, risk factors, Dschang, Cameroon
Poster
Socio-economic and anthropometric risk prediction factors of hypertensive disorders in pregnant women at the District Hospital of Dschang, Cameroon
Tiotsia Tsapi Armand1,&, Zogning Makemjio Émeline1, Colizzi Vittorio2, Défo Tamgno Eric1
1Faculty of Sciences and Technology, Evangelical University of Cameroon, Bandjoun, Cameroon, 2Department of Experimental Medicine and surgery, “Tor Vergata” University of Rome, Italy
&Corresponding author
Introduction: hypertensive disorders of pregnancy (HDP) are the third leading cause of maternal death worldwide and account for 24.4% in Cameroon. In a limited resource setting, a risk-predictive model of HDP would be an asset to improve the follow-up of pregnant women and provide a better maternal-fetal prognosis. This study aimed to develop a model to predict the risk of HDP in the first trimester of pregnancy based on socioeconomic and anthropometric factors.
Méthods: a retrospective case-control study, using a closed questionnaire, was conducted at the Dschang District Hospital in West Cameroon. 436 participants aged 18-48 years were included in the study, 209 controls and 227 cases. Data relating to their first antenatal consultation between 6 and 15 weeks of amenorrhea were collected. The data were collected in an Excel 2019 file and analysed using SAS software (version 9.4). A predictive equation for the risk probability of HDP was established. The quality of the model was investigated by plotting a Receiver Operating Characteristic (ROC) curve and calculating the Area Under the Curve (AUC).
Results: the HDP prediction model which was obtained by multivariate binary regression contained fasting blood glucose, systolic blood pressure, and body mass index. This model has an area under a curve of 0.67 and a 95% confidence interval of 0.61 to 0.72, giving it a low discriminatory capacity.
Conclusion: this study allowed the establishment of a predictive model for HDP in the first trimester of pregnancy using socio-economic and anthropometric information of the pregnant woman. In a context of limited health resources, it is necessary to develop affordable and reasonable alternatives to improve health monitoring. Further studies are needed to develop other models with better discriminatory capacity.
Socio-economic and anthropometric risk prediction factors of hypertensive disorders in pregnant women at the District Hospital of Dschang, Cameroon
Tiotsia Tsapi Armand1,&, Zogning Makemjio Émeline1, Colizzi Vittorio2, Défo Tamgno Eric1
1Faculty of Sciences and Technology, Evangelical University of Cameroon, Bandjoun, Cameroon, 2Department of Experimental Medicine and surgery, “Tor Vergata” University of Rome, Italy
&Corresponding author
Introduction: hypertensive disorders of pregnancy (HDP) are the third leading cause of maternal death worldwide and account for 24.4% in Cameroon. In a limited resource setting, a risk-predictive model of HDP would be an asset to improve the follow-up of pregnant women and provide a better maternal-fetal prognosis. This study aimed to develop a model to predict the risk of HDP in the first trimester of pregnancy based on socioeconomic and anthropometric factors.
Méthods: a retrospective case-control study, using a closed questionnaire, was conducted at the Dschang District Hospital in West Cameroon. 436 participants aged 18-48 years were included in the study, 209 controls and 227 cases. Data relating to their first antenatal consultation between 6 and 15 weeks of amenorrhea were collected. The data were collected in an Excel 2019 file and analysed using SAS software (version 9.4). A predictive equation for the risk probability of HDP was established. The quality of the model was investigated by plotting a Receiver Operating Characteristic (ROC) curve and calculating the Area Under the Curve (AUC).
Results: the HDP prediction model which was obtained by multivariate binary regression contained fasting blood glucose, systolic blood pressure, and body mass index. This model has an area under a curve of 0.67 and a 95% confidence interval of 0.61 to 0.72, giving it a low discriminatory capacity.
Conclusion: this study allowed the establishment of a predictive model for HDP in the first trimester of pregnancy using socio-economic and anthropometric information of the pregnant woman. In a context of limited health resources, it is necessary to develop affordable and reasonable alternatives to improve health monitoring. Further studies are needed to develop other models with better discriminatory capacity.
