AI Article Synopsis

  • * Researchers analyzed data from around 41,000 term births, comparing 374 cases of HIE, 3,056 with fetal acidosis, and 37,546 healthy infants, using a random forest classifier for prediction.
  • * The system showed improved detection rates for HIE (61.8%) and fetal acidosis (48.3%) without increasing false positives in healthy infants, allowing for potential early clinical interventions.

Article Abstract

The objective of this work was to evaluate the utility of using intrapartum fetal heart rate (FHR) and uterine pressure (UP) events to detect infants at risk of hypoxic-ischemic encephalopathy (HIE). We analyzed data from 40,976 term births from three groups: 374 infants that developed HIE, 3,056 that developed fetal acidosis without HIE, and 37,546 healthy infants. We counted the transitions between FHR events and the length of FHR and UP events. Then, we used these features to train a random forest classifier to discriminate between the healthy and the pathological (acidosis or HIE) groups. Compared to the Caesarean delivery rates for each group, our system detected 6.9% more HIE cases (54.9% vs 61.8%, p<0.001) and 10.7% more acidosis cases (37.6% vs 48.3%, p<0.001), with no increase in the false positive rates in the healthy group (38.9% vs 38.8%, p=0.26). Importantly, over 3/4 of the HIE detections were made 3 hours or more before delivery. It is reasonable to expect that this would be enough lead time to permit clinical intervention to improve the outcome of birth.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11448469PMC
http://dx.doi.org/10.22489/cinc.2023.380DOI Listing

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