Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses-Porto retrospective intrapartum study

Abstract

Introduction: Perinatal asphyxia is one of the most frequent causes of neonatal mortality, affecting approximately four million newborns worldwide each year and causing the death of one million individuals. One of the main reasons for these high incidences is the lack of consensual methods of early diagnosis for this pathology. Estimating risk-appropriate health care for mother and baby is essential for increasing the quality of the health care system. Thus, it is necessary to investigate models that improve the prediction of perinatal asphyxia. Access to the cardiotocographic signals (CTGs) in conjunction with various clinical parameters can be crucial for the development of a successful model.

Objectives: This exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices.

Methods: Single gestations data from a retrospective unicentric study from Centro Hospitalar e Universitário do Porto de São João (CHUSJ) between 2010 and 2018 was probed. The CTGs were acquired and analyzed by Omniview-SisPorto, estimating several fHR features. The clinical variables were obtained from the electronic clinical records stored by ObsCare. Entropy and compression characterized the complexity of the fHR time series. These variables' contribution to the prediction of asphyxia perinatal was probed by binary logistic regression (BLR) and Naive-Bayes (NB) models.

Results: The data consisted of 517 cases, with 15 pathological cases. The asphyxia prediction models showed promising results, with an area under the receiver operator characteristic curve (AUC) >70%. In NB approaches, the best models combined clinical and SisPorto features. The best model was the univariate BLR with the variable compression ratio scale 2 (CR2) and an AUC of 94.93% [94.55; 95.31%].

Conclusion: Both BLR and Bayesian models have advantages and disadvantages. The model with the best performance predicting perinatal asphyxia was the univariate BLR with the CR2 variable, demonstrating the importance of non-linear indices in perinatal asphyxia detection. Future studies should explore decision support systems to detect sepsis, including clinical and CTGs features (linear and non-linear).

Keywords: cardiotocography; fetal heart rate; neonatology; non-linear methods; perinatal asphyxia.

Figure 1

Correlation plot between CTGs indices. CR1, compression ratio for scale 1; CR2, compression ratio for scale 2; MSCslope, slope of the linear regression line best fitting the compressed file size all first five scales; MSCss, product of MSCsum and MSCslope; MSCsum, sum of the compressed file size values of the first five scales; MSCslopeCR, slope of the linear regression line best fitting the compression ratio values over on the first five scales; MSCssCR, product of MSCsumCR and MSCslopeCR; MSCsumCR, sum of the compression ratio values of the first five scales; MSEslope, slope of the linear regression line best fitting the entropy values over on the first five scales; MSEss, product of MSEsum and MSEslope; MSEsum, sum of the entropy values of the first five scales; SampEn1, entropy for scale 1; SampEn2, entropy for scale 2; SC1, compressed file size for scale 1; SC2, compressed file size for scale 2; STV, short-term variability; LTV, long-term variability.