Automated analysis of respiratory behavior for the prediction of apnea in infants following general anesthesia.

Abstract

Infants recovering from general anesthesia are at risk of postoperative apnea (POA), a potentially life threatening event. There is no accurate way to identify which infants will experience POA, and thus all infants with postmenstrual age <; 60 weeks are monitored for apnea in hospital postoperatively. Using a comprehensive, automated analysis of the postoperative breathing patterns, we identified the occurrence of respiratory pauses in 24 infants at age risk for POA. We determined the POA category for each infant by using K-medoids to cluster the duration of the longest respiratory pause. Two clusters were identified, corresponding to APNEA and NO-APNEA, with a threshold of 14.6 s, a value consistent with the clinically accepted threshold of 15 s. K-medoids derived POA labels were used to evaluate the predictive ability of demographic and anesthetic management variables. Weight and the intraoperative doses of atropine, propofol, and opioids discriminated between the APNEA and NO-APNEA groups. A linear Gaussian discriminant analysis classifier provided a very good classification with a probability of detection PD = 0.73 and a probability of false alarm PFA = 0.22. This approach provides a promising tool for the systematic, objective study of infants at risk of POA.