European journal of obstetrics, gynecology, and reproductive biology · 2006
The computerized fetal heart rate analysis in post-term pregnancy identifies patients at risk for fetal distress in labour.
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Valensise Herbert, Facchinetti Fabio, Vasapollo Barbara, Giannini Flavio, Monte Ilaria Di, Arduini Domenico
Аннотация
OBJECTIVE: To ascertain the diagnostic ability of a computerized fetal heart rate (FHR) analysis system in the identification of patients at risk of fetal distress in labour.
STUDY DESIGN: Three hundred and two healthy post-term pregnancies were enrolled in a retrospective, cross-sectional study and subdivided into two groups, with (n=42) or without (n=260) fetal distress in labour. The last computerized FHR recording before onset of labour was analyzed.
RESULTS: The two groups showed a significant difference only in FHR baseline and in percentage of small accelerations on total. The multivariate analysis showed that only the percentage of small accelerations was significantly related to the labour outcome. A higher diagnostic accuracy was obtained with use of neural network analysis, which allowed a sensitivity of 56%, specificity 91%, positive predictive value 53% and negative predictive value 92% with an overall accuracy of 86%.
CONCLUSIONS: The increase in FHR baseline and in small FHR accelerations can be major factors in the prediction of subsequent fetal distress in healthy term fetuses. Use of neural networks seems to further improve the ability of computerized FHR analysis in the prediction of intrapartum distress.
STUDY DESIGN: Three hundred and two healthy post-term pregnancies were enrolled in a retrospective, cross-sectional study and subdivided into two groups, with (n=42) or without (n=260) fetal distress in labour. The last computerized FHR recording before onset of labour was analyzed.
RESULTS: The two groups showed a significant difference only in FHR baseline and in percentage of small accelerations on total. The multivariate analysis showed that only the percentage of small accelerations was significantly related to the labour outcome. A higher diagnostic accuracy was obtained with use of neural network analysis, which allowed a sensitivity of 56%, specificity 91%, positive predictive value 53% and negative predictive value 92% with an overall accuracy of 86%.
CONCLUSIONS: The increase in FHR baseline and in small FHR accelerations can be major factors in the prediction of subsequent fetal distress in healthy term fetuses. Use of neural networks seems to further improve the ability of computerized FHR analysis in the prediction of intrapartum distress.