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

Jorind Beqari1, Joseph R Powell2,3,4, Jacob Hurd1

  • 1Department of Surgery, Massachusetts General Hospital, Boston, MA.

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|March 14, 2024
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Summary

A machine-learning algorithm, NightSignal, shows promise in detecting postoperative complications early in cardiothoracic surgery patients. This wearable device data analysis could identify issues before symptoms appear, improving patient outcomes.

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Area of Science:

  • Cardiothoracic Surgery
  • Medical Machine Learning
  • Wearable Technology

Background:

  • There is a critical need for advanced methods to facilitate the early identification of postoperative complications following cardiothoracic procedures.
  • Current monitoring may not always provide timely detection of adverse events, potentially delaying necessary interventions.

Purpose of the Study:

  • To assess the efficacy of the NightSignal machine-learning algorithm in detecting postoperative complications before the onset of clinical symptoms in patients undergoing cardiothoracic surgery.
  • To evaluate the sensitivity and specificity of the algorithm for identifying postoperative events using data from wearable devices.

Main Methods:

  • A prospective observational cohort study involved 56 adult patients scheduled for cardiothoracic surgery.
  • Participants continuously wore a Fitbit device for at least one week preoperatively and up to 90 days postoperatively.
  • The NightSignal algorithm, initially developed for COVID-19 detection, was adapted and evaluated for its ability to detect postoperative complications.

Main Results:

  • The NightSignal algorithm demonstrated a sensitivity of 81% in detecting 17 out of 21 postoperative events.
  • Detection occurred at a median of 2 days prior to symptom onset.
  • The algorithm achieved a specificity of 75%, a negative predictive value of 97%, and a positive predictive value of 28%.

Conclusions:

  • Machine-learning analysis of biometric data from wearable sensors holds significant potential for the early detection of postoperative complications after cardiothoracic surgery.
  • This technology may enable proactive medical intervention by identifying complications before they become clinically apparent.