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

Autor: Ho CN; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan.; Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan.; Department of Hospital and Health Care Administration, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan, Taiwan.; Southern Taiwan University of Science and Technology, Tainan, Taiwan., Fu PH; Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan., Hung KC; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan.; Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan.; Department of Hospital and Health Care Administration, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan, Taiwan., Wang LK; Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan.; Department of Hospital and Health Care Administration, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan, Taiwan., Lin YT; Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan.; Department of Hospital and Health Care Administration, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan, Taiwan., Yang AC; Institute of Brain Science/Digital Medicine Center, National Yang Ming Chial Tung University, Taipei, Taiwan.; Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan., Ho CH; Department of Medicine Research, Chi Mei Medical Center, Tainan, Taiwan., Chang JH; Department of Medicine Research, Chi Mei Medical Center, Tainan, Taiwan., Chen JY; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan.; Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan.
Jazyk: angličtina
Zdroj: Pain practice : the official journal of World Institute of Pain [Pain Pract] 2024 Jan; Vol. 24 (1), pp. 82-90. Date of Electronic Publication: 2023 Aug 24.
DOI: 10.1111/papr.13288
Abstrakt: 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.
(© 2023 World Institute of Pain.)
Databáze: MEDLINE
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