Hereditary thrombophilia and low -molecular -weight heparin in women: useful determinants, including thyroid dysfunction, incorporating the management of treatment and outcomes of the entity

OBJECTIVE

Our study purposed to examine the complex relationship between low-molecular-weight heparin therapy, multiple pregnancy determinants, and adverse pregnancy outcomes during the third trimester in women with inherited thrombophilia.

METHODS

Patients were selected from a prospective cohort of 358 pregnant patients recruited between 2016 and 2018 at the Clinic for Obstetrics and Gynecology, University Clinical Centre of Serbia, Belgrade.

RESULTS

Gestational age at delivery (β=-0.081, p=0.014), resistance index of the umbilical artery (β=0.601, p=0.039), and D-dimer (β=0.245, p<0.001) between 36th and 38th weeks of gestation presented the direct predictors for adverse pregnancy outcomes. The model fit was examined using the root mean square error of approximation 0.00 (95%CI 0.00-0.18), the goodness-of-fit index was 0.998, and the adjusted goodness-of-fit index was 0.966.

CONCLUSION

There is a need for the introduction of more precise protocols for the assessment of hereditary thrombophilias and the need for the introduction of low-molecular-weight heparin.

Interpreting the role of nuchal fold for fetal growth restriction prediction using machine learning

The objective of the study is to investigate the effect of Nuchal Fold (NF) in predicting Fetal Growth Restriction (FGR) using machine learning (ML), to explain the model's results using model-agnostic interpretable techniques, and to compare the results with clinical guidelines. This study used second-trimester ultrasound biometry and Doppler velocimetry were used to construct six FGR (birthweight < 3rd centile) ML models. Interpretability analysis was conducted using Accumulated Local Effects (ALE) and Shapley Additive Explanations (SHAP). The results were compared with clinical guidelines based on the most optimal model. Support Vector Machine (SVM) exhibited the most consistent performance in FGR prediction. SHAP showed that the top contributors to identify FGR were Abdominal Circumference (AC), NF, Uterine RI (Ut RI), and Uterine PI (Ut PI). ALE showed that the cutoff values of Ut RI, Ut PI, and AC in differentiating FGR from normal were comparable with clinical guidelines (Errors between model and clinical; Ut RI: 15%, Ut PI: 8%, and AC: 11%). The cutoff value for NF to differentiate between healthy and FGR is 5.4 mm, where low NF may indicate FGR. The SVM model is the most stable in FGR prediction. ALE can be a potential tool to identify a cutoff value for novel parameters to differentiate between healthy and FGR.