Evaluating the Dawes-Redman Algorithm: Insights into Fetal Wellbeing Monitoring

Fetal heart rate (FHR) monitoring is a cornerstone of modern obstetric care, enabling clinicians to assess fetal health and anticipate potential complications. Despite its widespread use, traditional methods, such as the Dawes-Redman algorithm, have raised questions about their effectiveness in high-risk pregnancies. In a recent study conducted at the University of Oxford, we undertook the first large-scale evaluation of the algorithm to determine its performance in identifying fetal wellbeing and adverse outcomes at term.

Our analysis, which examined over 4,000 FHR traces from singleton pregnancies, provided significant insights. The Dawes-Redman algorithm demonstrated high sensitivity, reliably identifying fetuses in a state of wellbeing in more than 90% of cases. This reaffirms its effectiveness in low-risk scenarios, where the majority of pregnancies result in healthy outcomes. However, the algorithm’s specificity—its ability to detect adverse outcomes—was considerably lower, highlighting its limitations in high-risk pregnancies. These findings underscore the importance of using the algorithm for its intended purpose rather than as a diagnostic tool for identifying at-risk pregnancies.

This research emphasises the importance of clinical context in interpreting FHR traces. The Dawes-Redman algorithm excels in confirming fetal wellbeing, but traces that fail to meet its criteria should always prompt further evaluation by experienced clinicians. These results also highlight the need for new technologies designed specifically for high-risk pregnancies. As we strive to advance fetal monitoring, the focus must remain on developing tools that combine accuracy and reliability to improve outcomes for mothers and babies alike.

Read the article here.