# Association of heart rate variability with preoperative acute insomnia in patients scheduled for elective surgery

## TL;DR

This study found that heart rate variability is linked to preoperative acute insomnia, suggesting it could help predict and manage this common issue in patients before surgery.

## Contribution

The study is the first to investigate the relationship between heart rate variability and preoperative acute insomnia.

## Key findings

- 78.5% of patients met the criteria for preoperative acute insomnia.
- HRV characteristics like low-frequency and high-frequency power were positively associated with insomnia.
- A predictive model using HRV metrics showed good accuracy in identifying preoperative acute insomnia.

## Abstract

Heart rate variability (HRV), which reflects the balance of the sympathetic and parasympathetic systems, is associated with insomnia. However, its relationship with preoperative acute insomnia has not yet been investigated. This study aimed to assess the associations of HRV characteristics with preoperative acute insomnia.

This study enrolled 563 patients who were scheduled for elective surgery. Preoperative clinical characteristics, including demographics, the apnea–hypopnea index (AHI), HRV characteristics, and sleep quality data, were recorded.

Among the 563 patients included, 78.5% met the criteria for insomnia. Age (P = 0.005), AHI score (P < 0.001), and AHI stage (P < 0.001) were positively associated, whereas education level (P = 0.004) was negatively associated with preoperative acute insomnia. In terms of HRV characteristics, low-frequency (LF) (P = 0.012) and high-frequency (HF) (P = 0.011) were positively associated with preoperative acute insomnia. Multivariate logistic regression analyses screened out the variables associated with preoperative acute insomnia, including education level [P = 0.028, odds ratio (OR) = 0.603], AHI score (P < 0.001, OR = 1.068), standard deviation of all normal NN intervals (SDNN) (P = 0.004, OR = 0.956), the root mean square of the successive differences (rMSSD) (P= 0.001, OR = 1.130), NN50 count divided by the total number of all NN intervals (pNN50) (P = 0.006, OR = 0.893), ultra-low-frequency (ULF) (P = 0.003, OR = 1.000), LF/HF ratio (P = 0.018, OR = 0.608), and HF ratio (P = 0.072, OR = 0.953). Receiver operating characteristic analysis revealed that the combination of these variables had good predictive value for preoperative acute insomnia, with an area under the curve of 0.750.

Preoperative acute insomnia is a prevalent issue and is associated with an imbalance in the sympathetic/parasympathetic system. A predictive model based on HRV characteristics may improve the management of preoperative acute insomnia.