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

Abstract

OBJECTIVE: To evaluate whether a machine-learning algorithm (ie, the "NightSignal" algorithm) can be used for the detection of postoperative complications before symptom onset after cardiothoracic surgery. BACKGROUND: 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 the inclusion criteria, of which 24 (42.9%) underwent thoracic operations and 32 (57.1%) underwent cardiac operations. The median age was 62 (Interquartile range: 51-68) years and 30 (53.6%) patients were female. The NightSignal algorithm detected 17 of the 21 postoperative events at a median of 2 (Interquartile range: 1-3) days before 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-before symptom onset-after cardiothoracic surgery.