Key result
A Support Vector Machine classifier incorporating the Analgesia Nociception Index and hemodynamic data predicted opioid dose changes with 86.21% accuracy and an AUC of 0.89 (95% CI 0.87-0.90).
Why the study?
Measuring analgesia to tailor opioid infusions remains challenging due to the absence of a specific measure quantifying nociception, prompting evaluation of the Analgesia Nociception Index to guide dose modifications.
Does the inclusion of the Analgesia Nociception Index (ANI) in machine learning models improve the prediction of opioid dose changes during general anesthesia in patients undergoing cholecystectomy?
Observational (n=17)
Does the inclusion of the Analgesia Nociception Index (ANI) in machine learning models improve the prediction of opioid dose changes during general anesthesia in patients undergoing cholecystectomy?
Effect estimate: AUC 0.89 (95% CI 0.87-0.90)
Including the Analgesia Nociception Index in machine learning models improves the prediction of opioid dose changes during general anesthesia compared to traditional hemodynamic signs alone.
Measuring the level of analgesia to adapt the opioids infusion during anesthesia to the real needs of the patient is still a challenge. This is a consequence of the absence of a specific measure capable of quantifying the nociception level of the patients. Unlike existing proposals, this paper aims to evaluate the suitability of the Analgesia Nociception Index (ANI) as a guidance variable to replicate the decisions made by the experts when a modification of the opioid infusion rate is required. To this end, different machine learning classifiers were trained with several sets of clinical features. Data for training were captured from 17 patients undergoing cholecystectomy surgery. Satisfactory results were obtained when including information about minimum values of ANI for predicting a change of dose. Specifically, a higher efficiency of the Support Vector Machine (SVM) classifier was observed compared with the situation in which the ANI index was not included: accuracy: 86.21% (83.62%-87.93%), precision: 86.11% (83.78%-88.57%), recall: 91.18% (88.24%-91.18%), specificity: 79.17% (75%-83.33%), AUC: 0.89 (0.87-0.90) and kappa index: 0.71 (0.66-0.75). The results of this research evidenced that including information about the minimum values of ANI together with the hemodynamic information outperformed the decisions made regarding only non-specific traditional signs such as heart rate and blood pressure. In addition, the analysis of the results showed that including the ANI monitor in the decision making process may anticipate a dose change to prevent hemodynamic events. Finally, the SVM was able to perform accurate predictions when making different decisions commonly observed in the clinical practice.
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González-Cava et al. (2020) conducted an observational in general anesthesia during cholecystectomy surgery (n=17). Analgesia Nociception Index (ANI) combined with hemodynamic information vs. hemodynamic information alone (heart rate and blood pressure) was evaluated on prediction of opioid dose change (AUC 0.89, 95% CI 0.87-0.90). A Support Vector Machine classifier incorporating the Analgesia Nociception Index and hemodynamic data predicted opioid dose changes with 86.21% accuracy and an AUC of 0.89 (95% CI 0.87-0.90).
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