2021

Articles


Prediction of Gestational Age: A Comparison of Regression Models

Kalyan Das, Anisha Das, Aysha Sultana, Md. Salauddin Khan, Md. Haider Ali Biswas

International Journal of Emerging Trends in Science and Technology, 2021, 2 August 2021 , Page 01-14

Aim: The main objective is to incorporate the major foetal parameters – biparietal diameter, head circumference, abdominal circumference and femur length for prediction of gestational age through ultrasonography between 10th and 42nd weeks of gestation and try to do a simultaneous comparative study with gestational age predicted by last menstrual period.


Methods: The study has been conducted particularly on the population of Bangladesh. It has been done on 229 Bangladeshi women who had usual singleton foetuses, with evidence of menstrual dates by sonography before fourteen weeks of gestation. Foetal anatomical structures have been scanned and measured at the time of sonographic inspection. For each patient, in addition to the four foetal parameters such as Biparietal Diameter (BPD), Head Circumference (HC), Abdominal Circumference (AC) and Femur Length (FL), the other parameters like Gestational Age (GA) by Last Menstrual Period (LMP) as well as by Ultrasonography (USG) have been recorded. Here we have adopted non-linear regression models in order to predict the response on gestational age. Usually, different modelling methods have been used for this purpose.


 Results: The logarithmic models normally presented better results if gestational age was predicted based on a single parameter than polynomial models whereas if all predictor variables were considered together, then Nernst model may turn out to be the best one. Also, it was seen that the accuracy level of gestational age predicted by ultrasonography was slightly more accurate than that determined by last menstrual period. 


Methods: The study has been conducted particularly on the population of Bangladesh. It has been done on 229 Bangladeshi women who had usual singleton foetuses, with evidence of menstrual dates by sonography before fourteen weeks of gestation. Foetal anatomical structures have been scanned and measured at the time of sonographic inspection. For each patient, in addition to the four foetal parameters such as Biparietal Diameter (BPD), Head Circumference (HC), Abdominal Circumference (AC) and Femur Length (FL), the other parameters like Gestational Age (GA) by Last Menstrual Period (LMP) as well as by Ultrasonography (USG) have been recorded. Here we have adopted non-linear regression models in order to predict the response on gestational age. Usually, different modelling methods have been used for this purpose.


Results: The logarithmic models normally presented better results if gestational age was predicted based on a single parameter than polynomial models whereas if all predictor variables were considered together, then Nernst model may turn out to be the best one. Also, it was seen that the accuracy level of gestational age predicted by ultrasonography was slightly more accurate than that determined by last menstrual period. 


Conclusions: There is a high degree of association among the different foetal parameters. Further, there is a high degree of association between the gestational ages by LMP and that by USG. Prediction of gestational ages by USG technique gives a good degree of accuracy and hence can be a reliable technique for estimation of gestational ages.