The effect of adolescent and advanced-age pregnancies on maternal and early neonatal clinical data

Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data

Purpose

Cardiotocography (CTG), which measures uterine contraction (UC) and fetal heart rate (FHR), is a crucial tool for assessing fetal health during pregnancy. However, traditional computerized cardiotocography (cCTG) approaches have non-negligible calibration errors in feature extraction and heavily rely on the expertise and prior experience to define diagnostic features from CTG or FHR signals. Although previous works have studied deep learning methods for extracting CTG or FHR features, these methods still neglect the clinical information of pregnant women.

Methods

In this paper, we proposed a multimodal deep learning architecture (MMDLA) for intelligent antepartum fetal monitoring that is capable of performing automatic CTG feature extraction, fusion with clinical data and classification. The multimodal feature fusion was achieved by concatenating high-level CTG features, which were extracted from preprocessed CTG signals via a convolution neural network (CNN) with six convolution layers and five fully connected layers, and the clinical data of pregnant women. Eventually, light gradient boosting machine (LGBM) was implemented as fetal status assessment classifier. The effectiveness of MMDLA was evaluated using a dataset of 16,355 cases, each of which includes FHR signal, UC signal and pertinent clinical data like maternal age and gestational age.

Results

With an accuracy of 90.77% and an area under the curve (AUC) value of 0.9201, the multimodal features performed admirably. The data imbalance issue was also effectively resolved by the LGBM classifier, with a normal-F1 value of 0.9376 and an abnormal-F1 value of 0.8223.

Conclusion

In summary, the proposed MMDLA is conducive to the realization of intelligent antepartum fetal monitoring.

Risk factors for missed abortion: retrospective analysis of a single institution's experience

OBJECTIVE

To explore the risk factors including the difference between mean gestational sac diameter and crown-rump length for missed abortion.

METHODS

Hospitalized patients with missed abortion and patients with continuing pregnancy to the second trimester from Chengdu Women's and Children's Central Hospital from June 2018 to June 2021 were retrospectively analyzed. The best cut-off value for age and difference between mean gestational sac diameter and crown-rump length (mGSD-CRL) were obtained by x-tile software. Univariate and multivariate logistic regression analysis were adopted to identify the possible risk factors for missed abortion.

RESULTS

Age, gravidity, parity, history of cesarean section, history of recurrent abortion (≥ 3 spontaneous abortions), history of ectopic pregnancy and overweight or obesity (BMI > 24 kg/m²) were related to missed abortion in univariate analysis. However, only age (≥ 30 vs < 30 years: OR = 1.683, 95%CI = 1.017-2.785, P = 0.043, power = 54.4%), BMI (> 24 vs ≤ 24 kg/m²: OR = 2.073, 95%CI = 1.056-4.068, P = 0.034, power = 81.3%) and mGSD-CRL (> 20.0vs ≤ 11.7 mm: OR = 2.960, 95% CI = 1.397-6.273, P = 0.005, power = 98.9%; 11.7 < mGSD-CRL ≤ 20.0vs > 20.0 mm: OR = 0.341, 95%CI = 0.172-0.676, P = 0.002, power = 84.8%) were identified as independent risk factors for missed abortion in multivariate analysis.

CONCLUSION

Patients with age ≥ 30 years, BMI > 24 kg/m² or mGSD-CRL > 20 mm had increasing risk for missed abortion, who should be more closely monitored and facilitated with necessary interventions at first trimester or even before conception to reduce the occurrence of missed abortion to have better clinical outcomes.