Evaluation of Surgical Pleth Index and Analgesia Nociception Index as surrogate pain measures in conscious postoperative
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ORIGINAL RESEARCH
Evaluation of Surgical Pleth Index and Analgesia Nociception Index as surrogate pain measures in conscious postoperative patients: an observational study Joo‑Hyun Lee1 · Byung‑Moon Choi2 · Yu‑Ri Jung2 · Yong‑Hun Lee2 · Ji‑Yeon Bang2 · Gyu‑Jeong Noh2,3 Received: 24 June 2019 / Accepted: 5 October 2019 © Springer Nature B.V. 2019
Abstract We evaluated the performance of the Surgical Plethysmographic Index (SPI) and the Analgesia Nociception Index (ANI) as surrogate pain measures and determined their respective cut-off values for detecting pain in conscious postoperative patients. In total, 192 patients after elective surgery were enrolled. Baseline SPI and ANI data were acquired for 10 min in the operating room prior to surgery when the patients rated their pain as 0 on the numerical rating scale (NRS). Upon arrival in the post-anaesthesia care unit (PACU) after surgery, SPI and ANI data were recorded for 10 min. The means of the recorded data at OR and PACU were defined as the values representing baseline and postoperative pain, respectively. SPI and ANI data obtained from 189 patients were analysed, who were anesthetized with propofol (n = 149) or sevoflurane (n = 40). Remifentanil was continuously infused intraoperatively in all patients. The values of SPI and ANI were significantly different in conscious patients without (NRS = 0) and with pain (NRS > 0). The areas under the receiver operating curves for SPI and ANI were 0.73 (P 0. Cut-off values used for the calculation of sensitivity and specificity were calculated as the “best fit” (highest combined sensitivity and specificity) [21]. Population prediction probability was calculated to determine whether SPI or ANI score can distinguish between pain and no pain [22]. The coefficients of variation of the mean baseline and postoperative values were calculated to determine the stability of SPI and ANI. The relationships between NRS and SPI and between NRS and ANI were analysed using both the Pearson correlation coefficient and linear regression. Data are expressed as mean ± standard deviation (SD) for normally distributed continuous variables, median (25–75%) for nonnormally distributed continuous variables, and counts and percentages for categorical variables. P values
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