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Heart rate variability (HRV) can identify patients at risk for postoperative sleep disorders (PSD). This study used HRV to develop a model for early PSD detection, aiding surgical recovery.

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Area of Science:

  • Cardiology
  • Sleep Medicine
  • Surgical Recovery

Background:

  • Postoperative sleep disorders (PSD) are common surgical complications.
  • Early PSD identification is crucial for reducing surgical stress and enhancing recovery.
  • Current methods for PSD risk assessment are limited.

Purpose of the Study:

  • To investigate heart rate variability (HRV) as a digital biomarker for PSD.
  • To develop a predictive model for early PSD identification using HRV.
  • To assess autonomic nervous system regulation in patients with and without PSD.

Main Methods:

  • Assessed 120-hour continuous HRV in 51 gastrointestinal cancer surgery patients.
  • Compared HRV parameters between patients with and without PSD.
  • Developed a risk factor prediction model using HRV parameters.

Main Results:

  • Patients with PSD showed lower parasympathetic tone and disrupted autonomic circadian rhythms.
  • The predictive model achieved an AUC of 0.815, sensitivity of 0.909, and specificity of 0.621.
  • HRV parameters effectively differentiated patients with and without PSD.

Conclusions:

  • HRV is a valuable non-invasive tool for diagnosing PSD risk.
  • HRV monitoring can improve perioperative sleep quality management.
  • This approach holds potential for expanded use in the perioperative period.