Evidence Rating Level: 3 (Average)
1. Machine learning combined with hormone levels including endocannabinoid anandamide (AEA), progesterone (P4) and b-human chorionic gonadotrophin (b-hCG) may help predict threatened miscarriage risk
2. AEA was positively correlated with threatened miscarriage and P4 was negatively correlated with threatened miscarriage
Approximately 11% of women will experience threatened miscarriage, which is diagnosed by vaginal bleeding, with 50% of these pregnancies resulting in an inevitable miscarriage. This study aimed to combine the levels of hormone including endocannabinoid anandamide (AEA), progesterone (P4) and b-human chorionic gonadotrophin (b-hCG) with machine learning tools to predict the risk of threatened miscarriage. This case control study recruited 119 normal pregnancy women in their first trimester and 96 women with threatened miscarriage, with 58 cases with ongoing pregnancy and 38 with inevitable miscarriage. Inclusion criteria were: single intrauterine pregnancy <13 weeks gestational age, pregnancy related vaginal bleeding in the threatened miscarriage group, and no pregnancy related vaginal bleeding in the normal pregnancy group, and age >20 years. b-hCG levels, P4 levels and AEA levels were detected. Six different machine learning tools were used to predict threatened miscarriage. These tools were: logistic regression (LR) model, random forest (RF) model, extreme gradient boosting (XGboost) model, k-nearest neighbors classifier (KNN) model, multilayer perceptron (MLP) neural network model and support vector machine (SVM) model. In the two groups, there were no significant differences in age, BMI, AEA and b-hCG. P4 levels were lower in the ongoing pregnancy group. AEA was found to be strongly positively correlated with threatened miscarriage (r = 0.38, p < 0.0001), while P4 was found to be negatively correlated with threatened miscarriage (r = − 0.23, p < 0.001). The LR model was found to have the highest accuracy and precision. Limitations to this study included small sample size, unbalance sample size, and the ability to detect hormone levels only in the first trimester, not in the second ior third. Future studies on using machine learning tools with biomarkers may help predict disease and health outcomes.
Click to read the study in BMC Pregnancy and Childbirth
Image: PD
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