One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.

Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models’ real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics.

Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.

To review the usefulness of the sFlt-1/PlGF ratio to detect adverse pregnancy outcomes related to placental dysfunction in twin pregnancies. A systematic review in Pubmed-Medline, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Web of Science, and National Guideline was performed. Studies were selected if they were published in the last 10 years, included a sample size equal to or greater than 10 twin gestations, determined the sFlt-1/PIGF ratio, and revealed the pregnancy outcome of the included patients. A total of 11 studies were selected. Outcomes related to the association between sFlt-1/PlGF ratio throughout pregnancy and perinatal outcome, particularly related to placental dysfunction (early and late-onset preeclampsia and FGR), were collected. The vast majority of studies showed an increased sFlt-1/PlGF ratio in twin pregnancies complicated with preeclampsia or other adverse perinatal outcomes compared with uneventful pregnancies. The included articles revealed promising results when evaluating the usefulness of the sFlt-1/PlGF ratio to rule out preeclampsia. The scarce available data regarding FGR suggests that the sFlt-1/PlGF ratio is a promising tool for detecting this pregnancy complication. Data concerning other aspects of the sFlt-1/PlGF ratio, such as its evolution during healthy twin pregnancies or variations according to chorionicity, is limited. The sFlt-1/PlGF ratio in twin pregnancies is useful to detect, and particularly to rule out adverse pregnancy outcomes related to placental dysfunction, such as preeclampsia or FGR.

Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th–90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RF_{F1_2} and ELM_{F1_2} provided the highest F1-score values in the validation dataset, (88.17 ± 8.34% and 90.2 ± 4.43%) with the 50th percentile of EHG and obstetric inputs. ELM_{F1_2} outperformed RF_{F1_2} in sensitivity, being similar to those of ELM_Sens (sensitivity optimization). The 10th–90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.

Introduction: The objective was to elucidate if the sFlt-1/PlGF ratio at 24 weeks in twin pregnancies could be useful to select patients who subsequently develop diseases related to placental dysfunction, such as preeclampsia or fetal growth restriction (FGR). Methods: This was a prospective study among all twin pregnancies followed up at a tertiary hospital. The sFlt-1/PlGF ratio was determined at 24 weeks. Results: A total of 108 patients with a twin gestation were included. Pregnant women who developed preeclampsia and/or FGR displayed a significantly higher sFlt-1/PlGF ratio at 24 weeks, compared to those who did not develop these diseases (20.3 vs. 4.3, p = 0.002). The mean sFlt-1/PlGF ratio was not significantly different between patients who subsequently developed preeclampsia compared with those that developed FGR (29.8 vs. 18.45, p = 0.42). A sFlt-1/PlGF ratio ≥17 at 24 weeks is associated with a significant increase in the frequency of preeclampsia (odds ratio, 37.13 [95% confidence interval, 4.78–288.25]; p = 0.002), and FGR (odds ratio, 39.58 [95% confidence interval, 6.31–248.17]; p < 0.001). The addition of maternal characteristics and mean pulsatility index of the uterine arteries to the sFlt-1/PlGF ratio at 24 weeks enhances the identification of patients who develop preeclampsia or FGR. Conclusion: The sFlt-1/PlGF ratio at 24 weeks in twin pregnancies, combined with the mean pulsatility index of the uterine arteries and maternal characteristics, could select patients who develop preeclampsia or FGR. These patients might benefit from a close follow-up in order to avoid maternal-fetal adverse outcomes.