Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study
IntroductionPerinatal 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 di...
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1099263/full |
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| author | Maria Ribeiro Maria Ribeiro Inês Nunes Inês Nunes Inês Nunes Luísa Castro Luísa Castro Cristina Costa-Santos Teresa S. Henriques |
| author_facet | Maria Ribeiro Maria Ribeiro Inês Nunes Inês Nunes Inês Nunes Luísa Castro Luísa Castro Cristina Costa-Santos Teresa S. Henriques |
| author_sort | Maria Ribeiro |
| collection | DOAJ |
| description | IntroductionPerinatal 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.ObjectivesThis exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices.MethodsSingle 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.ResultsThe 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%].ConclusionBoth 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). |
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| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-09T22:31:43Z |
| publishDate | 2023-03-01 |
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| spelling | doaj.art-68f146192c854da4b39da18deded6db02023-03-22T13:07:39ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-03-011110.3389/fpubh.2023.10992631099263Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum studyMaria Ribeiro0Maria Ribeiro1Inês Nunes2Inês Nunes3Inês Nunes4Luísa Castro5Luísa Castro6Cristina Costa-Santos7Teresa S. Henriques8Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), Porto, PortugalComputer Science Department, Faculty of Sciences, University of Porto, Porto, PortugalInstitute of Biomedical Sciences Abel Salazar, University of Porto, Porto, PortugalCentro Materno-Infantil do Norte—Centro Hospitalar e Universitário do Porto, Porto, PortugalCentre for Health Technology and Services Research (CINTESIS), Faculty of Medicine University of Porto, Porto, PortugalCINTESIS@RISE, MEDCIDS, Faculty of Medicine, University of Porto, Porto, PortugalSchool of Health of Polytechnic of Porto, Porto, PortugalCINTESIS@RISE, MEDCIDS, Faculty of Medicine, University of Porto, Porto, PortugalCINTESIS@RISE, MEDCIDS, Faculty of Medicine, University of Porto, Porto, PortugalIntroductionPerinatal 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.ObjectivesThis exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices.MethodsSingle 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.ResultsThe 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%].ConclusionBoth 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).https://www.frontiersin.org/articles/10.3389/fpubh.2023.1099263/fullnon-linear methodsneonatologyfetal heart ratecardiotocographyperinatal asphyxia |
| spellingShingle | Maria Ribeiro Maria Ribeiro Inês Nunes Inês Nunes Inês Nunes Luísa Castro Luísa Castro Cristina Costa-Santos Teresa S. Henriques Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study Frontiers in Public Health non-linear methods neonatology fetal heart rate cardiotocography perinatal asphyxia |
| title | Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study |
| title_full | Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study |
| title_fullStr | Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study |
| title_full_unstemmed | Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study |
| title_short | Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study |
| title_sort | machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses porto retrospective intrapartum study |
| topic | non-linear methods neonatology fetal heart rate cardiotocography perinatal asphyxia |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1099263/full |
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