Prediction of acute postoperative pain based on intraoperative nociception level (NOL) index values: the impact of machine learning-based analysis

The objective of the ancillary analysis of the NOLGYN study was to evaluate the ability of a machine-learning aglorithm to predict moderate to severe acute postoperative pain based on intraoperative NOL values. Our study uses the data from the NOLGYN study, a randomized con trolled trial that evaluated the impact of NOL-guided intraoperative administration of fentanyl on overall fentanyl consumption compared to standard of care. Seventy patients (ASA class I–III, aged 18–75 years) scheduled for gynecological laparoscopic surgery were enrolled. Variables included b aseline demographics, NOL reaction to incision or intubation, median NOL during surgery, NOL time-weighted average (TWA) above or under manufacturers’ recommended thresholds (10–25), and percentage of surgical time spent with NOL> 25 or< 10. We evaluated different machine learning algorithms to predict postoperative pain. Performance was assessed using cross-validated area under the ROC curve (CV-AUC). Of the 66 patients analyzed, 42 (63.6%) experienced moderate to severe pain. NOL post-intubation (42.8 (31.8 –50.6) vs. 34.8 (25.6–41.3), p = 0.05), median NOL during surgery (13 (11–15) vs. 11 (8–13), p = 0.027), percentage of surgical time spent with NOL> 25 (23% (18 –18) vs. 20% (15–24), p = 0.036), NOL TWA< 10 (2.54 (2.1 –3.0) vs. 2.86 (2.48–3.62), p = 0.044) and percentage of surgical time spent with NOL< 10 (41% (36 –47) vs. 47% (40–55), p = 0.022) were associated with...
Source: Journal of Clinical Monitoring and Computing - Category: Information Technology Source Type: research