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 (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.