Prediction of early postoperative pain using sleep quality and heart rate variability

Abstract Purpose Accurate predictions of postoperative pain intensity are necessary for customizing analgesia plans. Insomnia is a risk factor for severe postoperative pain. Moreover, heart rate variability (HRV) can provide information on the sympathetic–parasympathetic balance in response to noxious stimuli. We developed a prediction model that uses the insomnia severity index (ISI), HRV, and other demographic factors to predict the odds of higher postoperative pain. Methods We recruited gynecological surgery patients classified as American Society of Anesthesiologists class 1–3. An ISI questionnaire was completed 1 day before surgery. HRV was calculated offline using intraoperative electrocardiogram data. Pain severity at the postanesthesia care unit (PACU) was assessed with the 0–10 numerical rating scale (NRS). The primary outcome was the model's predictive ability for moderate‐to‐severe postoperative pain. The secondary outcome was the relationship between individual risk factors and opioid consumption in the PACU. Results Our study enrolled 169 women. Higher ISI scores ( p = 0.001), higher parasympathetic activity (rMSSD, pNN50, HF; p < 0.001, p < 0.001, p < 0.001), loss of fractal dynamics (SD2, alpha 1; p = 0.012, p = 0.039) in HRV analysis before the end of surgery were associated with higher NRS scores, while laparoscopic surgery ( p = 0.031) was associated with lower NRS scores. We constructed a multiple logistic model (area under the curve = 0.852) to predict higher NRS scores at PACU arrival. The five selected predictors were age (OR: 0.94; p = 0.020), ISI score (OR: 1.14; p = 0.002), surgery type (laparoscopic or open; OR: 0.12; p < 0.001), total power (OR: 2.02; p < 0.001), and alpha 1 (OR: 0.03; p < 0.001). Conclusion We employed a multiple logistic regression model to determine the likelihood of moderate‐to‐severe postoperative pain upon arrival at the PACU. Physicians could personalize analgesic regimens based on a deeper comprehension of the factors that contribute to postoperative pain.