A Pilot Study Using Machine Learning Algorithms and Wearable Technology for the Early Detection of Postoperative Complications After Cardiothoracic Surgery

Objective: To evaluate whether a machine learning algorithm (i.e. the “NightSignal” algorithm) can be used for the detection of postoperative complications prior to symptom onset after cardiothoracic surgery. Summary Background Data: Methods that enable the early detection of postoperative complications after cardiothoracic surgery are needed. Methods: This was a prospective observational cohort study conducted from July 2021 to February 2023 at a single academic tertiary care hospital. Patients aged 18 years or older scheduled to undergo cardiothoracic surgery were recruited. Study participants wore a Fitbit watch continuously for at least 1 week preoperatively and up to 90-days postoperatively. The ability of the NightSignal algorithm—which was previously developed for the early detection of Covid-19—to detect postoperative complications was evaluated. The primary outcomes were algorithm sensitivity and specificity for postoperative event detection. Results: A total of 56 patients undergoing cardiothoracic surgery met inclusion criteria, of which 24 (42.9%) underwent thoracic operations and 32 (57.1%) underwent cardiac operations. The median age was 62 (IQR: 51-68) years and 30 (53.6%) patients were female. The NightSignal algorithm detected 17 of the 21 postoperative events a median of 2 (IQR: 1-3) days prior to symptom onset, representing a sensitivity of 81%. The specificity, negative predictive value, and positive predictive value of the algorithm for the detection of postoperative events were 75%, 97%, and 28%, respectively. Conclusions: Machine learning analysis of biometric data collected from wearable devices has the potential to detect postoperative complications—prior to symptom onset—after cardiothoracic surgery.