Consumer Wearables Could Predict Pediatric Surgery Complications

An estimated 4 million children undergo surgical procedures in U.S. hospitals each year. Postoperative complications, such as infections, can pose significant health risks to children, and timely detection after hospital discharge is often challenging. Traditional methods of monitoring complications rely on subjective reporting from children, who may struggle to articulate their symptoms, or from caregivers, which can delay detection. Furthermore, existing monitoring methods can be time-consuming and are not always effective in picking up complications early. Now, a first-ever study has used consumer wearables to quickly and precisely predict postoperative complications in children, demonstrating potential for facilitating faster treatment and care.

The study by researchers at Northwestern University (Evanston, IL, USA) and the University of Alabama at Birmingham (Birmingham, AL, USA involved 103 children who wore the devices for 21 days post-surgery. Rather than relying on traditional metrics like activity levels or heart rate, the researchers trained an algorithm using new metrics related to circadian rhythms and patterns in activity and heart rate. These metrics proved more sensitive in detecting complications. The study showed that the algorithm could predict complications up to three days before formal diagnosis with 91% sensitivity and 74% specificity.

The research, published in Science Advances, demonstrated the system's potential in providing early warnings of complications. The use of wearables allows for continuous, real-time monitoring without the need for elaborate sample preparation or invasive procedures. The findings suggest that the system could transform postoperative care by allowing faster responses to complications, improving recovery outcomes for pediatric patients. Moving forward, the researchers plan to expand the system into a real-time application that analyzes data automatically and sends alerts to clinical teams, which would further enhance the ability to intervene quickly and effectively.

"Today, consumer wearables are ubiquitous, with many of us relying on them to count our steps, measure our sleep and more," said Arun Jayaraman, senior author of the study, Professor at Northwestern University Feinberg School of Medicine, and a scientist at Shirley Ryan AbilityLab. "Our study is the first to take this widely available technology and train the algorithm using new metrics that are more sensitive in detecting complications. Our results suggest great promise for better patient outcomes and have broad implications for pediatric health monitoring across various care settings.”