Beyond Surgery: Using the Analgesia Nociception Index for Postoperative Pain Prediction

This study analyzed the utility of the Analgesia Nociception Index (ANI) in postoperative pain assessment as an objective pain indicator. Electrocardiogram (ECG) recordings from 30 patients in the Post Anesthesia Care Unit were considered, with a total of 52 pain reports registered during the recovery period, of which 32 were valid for the study. R peaks series (RR) were extracted from the ECG, and the ANI series were extracted from the RR series with a sampling frequency of 0.2 Hz. For each pain report, 11 statistical features were computed from cumulative paired windows of the ANI series before and after the pain report. Statistical tests were performed based on the distribution of ANI features, revealing that for the group of 5-minute windows, there were statistically significant differences in Median, Maximum, Mean, and Average Power. Several machine learning models to predict pain were trained using different datasets, for instance, (1) with the four above mentioned features, and (2) with all 11 features. The models resorted to the K-Nearest Neighbors (KNN) and Decision Tree (DT) algorithms. The best prediction models achieved an acceptable area under the Receiver Operating Characteristic curve of 0.74. Hence, the KNN model fitted with the dataset (1) could be considered the best due to its higher computational efficiency and greater specificity, which is preferable for avoiding unnecessary administration of analgesia. However, the model fitted with the dataset (2) presented greater sensitivity, which is preferable for avoiding the neglect of real pain cases.