A risk factor prediction model established by continuous heart rate variability parameters successfully identified patients at risk for postoperative sleep disorders with an AUC of 0.815.
Cohort (n=51)
No
Can continuous HRV monitoring using a smart patch predict postoperative sleep disorders in patients undergoing radical surgery for gastrointestinal cancer?
Continuous HRV monitoring using a smart patch can effectively predict postoperative sleep disorders in patients undergoing gastrointestinal cancer surgery.
Effect estimate: AUC 0.815 (95% CI 0.701-0.929)
p-value: p=<0.0001
BACKGROUND: Early identification of patients at risk of developing postoperative sleep disorders (PSD), which is a common complication after surgery, is an essential step in reducing surgical stress and is an important part of enhanced recovery after surgery. OBJECTIVE: In this study, we used a smart patch to explore heart rate variability (HRV), reflecting autonomic nervous system regulation, as potential PSD digital biomarkers and develop a prognostic model for the early identification of PSD. METHODS: We assessed 120 h of continuous HRV in a separate sample of 51 patients who underwent radical surgery for gastrointestinal cancer with and without PSD. RESULTS: By analyzing the 120-h HRV data of the two groups, we found that patients with PSD exhibited lower parasympathetic tone and longer dysregulated autonomic circadian rhythms. The area under the curve of the risk factor prediction model established by HRV parameters was 0.815; sensitivity was 0.909; specificity was 0.621; and the Youden index was 0.530. CONCLUSION: This research supports the utility of HRV as a non-invasive diagnostic tool, emphasizing its importance in perioperative management of sleep quality and potential to expand its use during the perioperative period.
Fang et al. (Tue,) conducted a cohort in Postoperative sleep disorder (PSD) in gastrointestinal cancer surgery (n=51). Heart rate variability (HRV) monitoring via smart patch vs. Normal sleep (PSQI ≤ 5) was evaluated on Prediction of postoperative sleep disorder using a multi-factor HRV parameter model (AUC 0.815, 95% CI 0.701-0.929, p=<0.0001). A risk factor prediction model established by continuous heart rate variability parameters successfully identified patients at risk for postoperative sleep disorders with an AUC of 0.815.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: