Key result
Perioperative sleep disorders are linked to ~4-point lower postoperative PNN50 following cardiac surgery.
Why the study?
The correlation between perioperative sleep quality and heart rate variability in patients undergoing cardiac surgery was not well established.
Does perioperative sleep disorder reduce heart rate variability in patients undergoing cardiac surgery?
Cohort (n=95)
No
Does perioperative sleep disorder reduce heart rate variability in patients undergoing cardiac surgery?
Mean Difference: -4.03
Absolute Event Rate: 6.31% vs 10.34%
p-value: p=<0.001
Perioperative sleep disorders in cardiac surgery patients are strongly correlated with suppressed parasympathetic heart rate variability, which could serve as an objective physiological biomarker for early identification of autonomic dysfunction.
Sleep disturbance associated with lower postoperative HRV in cardiac surgery; hypothesis-generating for HRV monitoring of autonomic dysfunction, needs prospective validation.
Objective: To explore the correlation between sleep quality (SQ) during the perioperative period of patients undergoing cardiac surgery and heart rate variability (HRV). Methods: This prospective observational cohort study included 95 patients undergoing cardiac surgery. SD was defined as a Pittsburgh Sleep Quality Index (PSQI) total score > 7, and all participants were categorized into an SD group and a non-SD group. HRV parameters were collected and compared at four predefined time points: 1 day preoperatively (T0), 1 day postoperatively (T1), 2 days postoperatively (T2), and 3 days postoperatively (T3). Repeated-measures ANOVA analyzed dynamic changes in HRV parameters within the SD group. Lasso regression screened the main HRV parameters associated with SD, followed by logistic regression to identify significantly related to SD. Point-biserial correlation analysis was used to test the positive or negative correlation between HRV parameters and SD. Results: No significant differences in clinical data were found between the two groups ( P > 0.05). Compared with the non-SD group, the SD group showed significantly lower HF, SDNN, SDANN, PNN50, and RMSSD at all time points (P< 0.05), while LF, LF/HF, and SDNNI showed no differences (P> 0.05). In the SD group, all HRV parameters exhibited a significant time effect ( P < 0.05); LF, HF, LF/HF, SDNN, SDNNI, PNN50, and RMSSD showed significant group effect ( P < 0.05); SDANN and RMSSD demonstrated significant time×group interaction ( P < 0.05). Lasso and logistic regression identified HF (T2), SDANN (T1), SDANN (/T2), PNN50 (T0/T1/T3), RMSSD (T0/T1/T2) as related to SD (OR< 1, P < 0.001). Point-biserial correlation revealed that all included HRV parameters were significantly negatively correlated with SD (correlation coefficient range: − 0.40 to − 0.78); PNN50 measured at T1 had the strongest negative correlation with SD (r= − 0.78). Conclusion: Perioperative SQ in cardiac surgery patients is closely related to HRV. HF, SDANN, PNN50, and RMSSD are HRV parameters significantly correlated with SD, suggesting that HRV monitoring may aid in early identification of autonomic dysfunction and guide individualized sleep interventions.
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Lu et al. (2026) conducted a cohort in Perioperative sleep disorders in cardiac surgery (n=95). Perioperative sleep disorder (PSQI > 7) vs. No sleep disorder (PSQI ≤ 7) was evaluated on Intergroup difference in PNN50 at 1 day postoperatively (T1) (p=<0.001). Patients with perioperative sleep disorders undergoing cardiac surgery exhibited significantly lower parasympathetic heart rate variability, notably a PNN50 at 1 day postoperatively of 6.31% compared to 10.34% in those without sleep disorders (p<0.001).
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