Key result
An ε-recurrence network approach applied to cardiovascular time series predicted preeclampsia with a sensitivity of 91.7% and a specificity of 68.1%.
Why the study?
Can ε-recurrence networks analysis of cardiovascular time series predict preeclampsia?
Population
Pregnant women (healthy and preeclamptic patients)
Design
Other
Authors
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May facilitate preeclampsia prediction from routine monitoring; leaves open prospective validation before clinical adoption.
Can ε-recurrence networks analysis of cardiovascular time series predict preeclampsia?
Effect estimate: Sensitivity 91.7%, Specificity 68.1%
Recurrence network analysis of heart rate and blood pressure variability shows promise for the early prediction of preeclampsia.
Ramírez-Ávila et al. (2013) studied Preeclampsia. ε-recurrence networks analysis of cardiovascular time series vs. Healthy patients was evaluated on Prediction of preeclampsia (Sensitivity 91.7%, Specificity 68.1%). An ε-recurrence network approach applied to cardiovascular time series predicted preeclampsia with a sensitivity of 91.7% and a specificity of 68.1%.
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