Prediction formulas for individual opioid analgesic requirements based on genetic polymorphism analyses.

Abstract

BackgroundThe analgesic efficacy of opioids is well known to vary widely among individuals, and various factors related to individual differences in opioid sensitivity have been identified. However, a prediction model to calculate appropriate opioid analgesic requirements has not yet been established. The present study sought to construct prediction formulas for individual opioid analgesic requirements based on genetic polymorphisms and clinical data from patients who underwent cosmetic orthognathic surgery and validate the utility of the prediction formulas in patients who underwent major open abdominal surgery.MethodsTo construct the prediction formulas, we performed multiple linear regression analyses using data from subjects who underwent cosmetic orthognathic surgery. The dependent variable was 24-h postoperative or perioperative fentanyl use, and the independent variables were age, gender, height, weight, pain perception latencies (PPL), and genotype data of five single-nucleotide polymorphisms (SNPs). To examine the utility of the prediction formulas, we performed simple linear regression analyses using subjects who underwent major open abdominal surgery. Actual 24-h postoperative or perioperative analgesic use and the predicted values that were calculated using the multiple regression equations were incorporated as dependent and independent variables, respectively.ResultsMultiple linear regression analyses showed that the four SNPs, PPL, and weight were retained as independent predictors of 24-h postoperative fentanyl use (R2 = 0.145, P = 5.66 × 10-10) and the two SNPs and weight were retained as independent predictors of perioperative fentanyl use (R2 = 0.185, P = 1.99 × 10-15). Simple linear regression analyses showed that the predicted values were retained as an independent predictor of actual 24-h postoperative analgesic use (R2 = 0.033, P = 0.030) and perioperative analgesic use (R2 = 0.100, P = 1.09 × 10-4), respectively.ConclusionsWe constructed prediction formulas, and the possible utility of these prediction formulas was found in another type of surgery.