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Pulse Rate and Transit Time Analysis to Predict Hypotension Events After Spinal Anesthesia During Programmed Cesarean Labor

Overview of attention for article published in Annals of Biomedical Engineering, July 2017
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Title
Pulse Rate and Transit Time Analysis to Predict Hypotension Events After Spinal Anesthesia During Programmed Cesarean Labor
Published in
Annals of Biomedical Engineering, July 2017
DOI 10.1007/s10439-017-1864-y
Pubmed ID
Authors

Juan Bolea, Jesús Lázaro, Eduardo Gil, Eva Rovira, José M. Remartínez, Pablo Laguna, Esther Pueyo, Augusto Navarro, Raquel Bailón

Abstract

Prophylactic treatment has been proved to reduce hypotension incidence after spinal anesthesia during cesarean labor. However, the use of pharmacological prophylaxis could carry out undesirable side-effects on mother and fetus. Thus, the prediction of hypotension becomes an important challenge. Hypotension events are hypothesized to be related to a malfunctioning of autonomic nervous system (ANS) regulation of blood pressure. In this work, ANS responses to positional changes of 51 pregnant women programmed for a cesarean labor were explored for hypotension prediction. Lateral and supine decubitus, and sitting position were considered while electrocardiographic and pulse photoplethysmographic signals were recorded. Features based on heart rate variability, pulse rate variability (PRV) and pulse transit time (PTT) analysis were used in a logistic regression classifier. The results showed that PRV irregularity changes, assessed by approximate entropy, from supine to lateral decubitus, and standard deviation of PTT in supine decubitus were found as the combination of features that achieved the best classification results sensitivity of 76%, specificity of 70% and accuracy of 72%, being normotensive the positive class. Peripheral regulation and blood pressure changes, measured by PRV and PTT analysis, could help to predict hypotension events reducing prophylactic side-effects in the low-risk population.

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Geographical breakdown

Country Count As %
Unknown 67 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 15%
Student > Ph. D. Student 10 15%
Researcher 9 13%
Other 5 7%
Student > Doctoral Student 5 7%
Other 13 19%
Unknown 15 22%
Readers by discipline Count As %
Medicine and Dentistry 20 30%
Engineering 6 9%
Psychology 5 7%
Nursing and Health Professions 5 7%
Computer Science 3 4%
Other 7 10%
Unknown 21 31%