Prediction of lung maturity through quantitative ultrasound analysis of fetal lung texture in women with diabetes during pregnancy.

OBJECTIVES: The present study aimed to evaluate the performance of QuantusFLM® software, which performs quantitative ultrasound analysis of fetal lung texture, in predicting lung maturity in fetuses of diabetic mothers. METHODS: The patients included in this study were between 34 and 38 weeks and 6 days gestation and were divided into two groups: (1) patients with diabetes on medication and (2) control. The ultrasound images were performed up to 48 h prior to delivery and analyzed using QuantusFLM® software, which classified each fetus as high or low risk for neonatal respiratory morbidity based on lung maturity or immaturity. RESULTS: A total of 111 patients were included in the study, being 55 in diabetes and 56 in control group. The pregnant women with diabetes had significantly higher body mass index (27.8 kg/m2 vs. 25.9 kg/m2, respectively, p=0.02), increased birth weight (3,135 g vs. 2,887 g, respectively, p=0.002), and a higher rate of labor induction (63.6 vs. 30.4 %, respectively, p<0.001) compared to the control group. QuantusFLM® software was able to predict lung maturity in diabetes group with 96.4 % accuracy, 96.4 % sensitivity and 100 % positive predictive value. Considering the total number of patients, the software demonstrated accuracy, sensitivity, specificity, positive predictive value and negative predictive value of 95.5 , 97.2, 33.3, 98.1 and 25 %, respectively. CONCLUSIONS: QuantusFLM® was an accurate method for predicting lung maturity in normal and DM singleton pregnancies and has the potential to aid in deciding the timing of delivery for pregnant women with DM.