Determinants of adverse neonatal outcome in vaginal deliveries complicated by suspected intraamniotic infection

An intelligent adverse delivery outcomes prediction model based on the fusion of multiple obstetric clinical data

Adverse delivery outcomes is a major re-productive health problem that affects the physical and mental health of pregnant women. Obviously, obstetric clinical data has periodically time series characteristics. This paper proposed a three stage adverse delivery outcomes prediction model via the fusion of multiple time series clinical data. The first stage is data aggregation, in which the data set is collected from the obstetric clinical data and divided based on time series features. In the second stage, a multi-channel gated cycle unit is used to solve the calculation error caused by irregular sampling of time series data. The hidden layer feature vector is connected with the fully connected layer, reshaped into a new one-dimensional feature, and fused with the non-time series data into a new data set. The third stage is the prediction stage of adverse delivery outcomes. By connecting the multi-channel gated cycle unit with the extreme gradient lift, the data transmitted in the corresponding channel is used in the feature extraction stage, in which the weighted entropy-based feature extraction is adopted. With the help of the extracted features, a hybrid artificial neural network architecture (MGRU-XGB) was developed to predict adverse delivery outcomes. The experimental results showed that the hybrid model had the best prediction performance for adverse delivery outcomes compared with other single models in terms of sensitivity, specificity, AUC and other evaluation indexes.

Chorioamnionitis and adverse outcomes in low-risk pregnancies: a population-based study

OBJECTIVE

To compare the composite neonatal and maternal adverse outcomes among low-risk pregnancies with versus without chorioamnionitis.

METHODS

We conducted a retrospective cohort study using U.S. Vital Statistics Data. The study population was restricted to full term, low-risk, singleton pregnancies. Pregnancies were categorized into those affected and unaffected by chorioamnionitis. The primary outcome was composite neonatal adverse outcome and the secondary outcome was composite maternal adverse outcome. Multivariable Poisson regression models with robust error variance were used to examine the factors associated with chorioamnionitis and to evaluate the association between chorioamnionitis and adverse outcomes [using adjusted relative risk (aRR) and 95% confidence interval (CI)].

RESULTS

Of 19.7 million live births, 59.4% met inclusion criteria; among them, 1.7% were complicated by chorioamnionitis. The risk of composite neonatal adverse outcome was higher in newborns delivered by women with chorioamnionitis (aRR = 3.40; 95% CI = 3.30-3.49). Compared to women without chorioamnionitis, those with chorioamnionitis had a higher risk of composite maternal adverse outcome (aRR = 2.42; 95% CI = 2.31-2.55). Infant mortality was also higher in affected pregnancies (aRR = 1.23; 95% CI = 1.09-1.38).

CONCLUSION

Among low-risk pregnancies, chorioamnionitis is associated with a higher risk of composite neonatal and maternal adverse outcomes. Infant death is also increased.