Open AccessINTEGRATION OF FETAL MID THIGH SOFT TISSUE THICKNESS IN ULTRASOUND BIRTH WEIGHT ESTIMATION FORMULA INCREASES THE ACCURACY OF FETAL WEIGHT ESTIMATION NEAR TERM
Author(s) -
Shripad Hebbar,
Sukriti Malaviya,
Sunanda Bharatnur
Publication year - 2018
Publication title -
asian journal of pharmaceutical and clinical research
Language(s) - English
Resource type - Journals
eISSN - 2455-3891
pISSN - 0974-2441
DOI - 10.22159/ajpcr.2018.v11i4.23776
Subject(s) - medicine , gestational age , fetus , birth weight , ultrasound , linear regression , standard error , regression analysis , obstetrics , coefficient of variation , gestation , fetal weight , stepwise regression , standard deviation , pregnancy , mathematics , statistics , biology , radiology , genetics
Objective: The objective of the study was to find whether incorporation of MTSTT in fetal weight estimation formulae which are traditionally based on biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL) improves birth weight (BW) estimation. Methods: In a prospective observational study, MTSTT was measured within 1 week of delivery in 100 women with term singleton pregnancy along with other standard biometric parameters, i.e. BPD, HC, AC and FL, and MTSTT. Multiple regression analysis was carried out using PHOEBE regression software using different combinations of biometric variables to find out the best fit model of fetal weight estimation. The predicted BW was compared with actual neonatal BW soon after delivery and regression coefficients (R2) were determined for each of prediction models for comparing the accuracies. Results: Mean gestational age at delivery was 38.4±1.08 weeks and the BW of neonates varied between 2.18 kg and 4.38 kg (mean ± standard deviation: 3.07±0.43 kg). By adding MTSTT to BPD, HC, AC, and FL, we obtained the formula Log 10 (BW) = −0.14783+0.00725 *BPD +0.00043 *HC +0.00436 *AC +0.01942 *FL +0.16299 *MTSTT, which had a very good Pearson regression coefficient ((r2: 0.89 p<0.001) compared to conventional models based on standard fetal biometry. All prediction models had better strength of correlation when combined with MTSTT (p<0.001). The routine four parameter formula could identify 45% and 80% of fetuses within 5% and 10% weight range; pick up rate was further increased to 61% and 95% by addition of MTSTT. Conclusion: It is evident that addition of MTSTT to other biometric variables in models of fetal weight estimation improves neonatal BW prediction (r2=0.89).