1Department of Anesthesiology, Chi Mei Medical Center, Tainan City, Taiwan
2Department of the Senior Citizen Service Management, Chia Nan University of Pharmacy and Science, Tainan, Taiwan.
§Chun-Ning Ho and Jen-Yin Chen are co-corresponding authors
Outline
Background
Postoperative pain is distressful, and it imposes adverse effects on multi-systems. Early intervention and effective postoperative pain management had always been major concerns of clinical anesthesiologists. For pain is subjective, psychological factors had been taken into considerations to make predictions in several studies. Temporal changes of heart rate variability (HRV) across the perioperative period, which reflects the dynamic activities of the autonomic nervous system (ANS), is another important part we want to incorporate into the prediction model. Our goal was to develop a better prediction model of pain severity based on both the demographic factors and intraoperative indices.
Method
We enrolled 80 women ≧ 20 years of age scheduled for gynecological surgeries under general anesthesia. All participants were American Society of Anesthesiologists classification of physical status 1 to 3 without using drugs
affecting HRV. Questionnaires including Insomnia Severity Index (ISI) and Beck Depression Inventory-II (BDI-II) were used to evaluate participants’ sleep qualities and severity of depression, respectively. Physiological signals were recorded perioperatively. After surgery, the numeric rating scale (NRS) for pain was measured as a patient’s arrival at the postanesthesia care unit (PACU). The HRV indices of frequency-domain and nonlinear-domain were computed and analyzed offline.The demographic factors and introperative indices were included to build a prediction model of postoperative pain severity by using the stepwise linear
regression.
Pain, Postoperative;Sleep;Heart Rate Variability; Anesthesia,General
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