Intraoperative use of the machine learning-derived nociception level monitor results in less pain in the first 90 min after surgery - van der Wal, Imeen; Meijer, Fleur; Fuica, Rivka; Silman, Zmira; Boon, Martijn; Martini, Chris; van Velzen, Monique; Dahan, Albert; Niesters, Marieke; Gozal, Yaacov (Frontiers in Pain Research, 2023)
Severe postoperative pain remains a significant problem and associates with several adverse outcomes. Here, we determined whether the application of a monitor that detects intraoperative nociceptive events, based on machine learning technology, and treatment of such events reduces pain scores in the post-anesthesia care unit (PACU). To that end, we performed a pooled analysis of two trials in adult patients, undergoing elective major abdominal surgery, on the effect of intraoperative nociception level monitor (NOL)-guided fentanyl dosing on PACU pain was performed. Patients received NOL-guided fentanyl dosing or standard care (fentanyl dosing based on hemodynamic parameters). Goal of the intervention was to keep NOL at values that indicated absence of nociception. The primary endpoint of the study was the median pain score obtained in the first 90-min in the PACU. Pain scores were collected at 15-min intervals on an 11-point Likert scale. Data from 125 patients (55 men, 70 women, age range 21-86 years) were analyzed. Sixty-one patients received NOL-guided fentanyl dosing and 64 standard care. Median PACU pain score was 1.5 points (0.8-2.2) lower in the NOL group compared to the standard care; the proportion of patients with severe pain was 70% lower in the NOL group (p = 0.045). The only significant factor associated with increased odds for severe pain was the standard of care compared to NOL treatment (OR 6.0, 95% CI 1.4 -25.9, p = 0.017). The use of a machine learning-based technology to guide opioid dosing during major abdominal surgery resulted in reduced PACU pain scores with less patients in severe pain.