College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Republic of Korea.
College of Pharmacy, Yeungnam University, Gyeongsan, Gyeongbuk, 38541, Republic of Korea.
Sci Rep. 2022 Mar 23;12(1):4974. doi: 10.1038/s41598-022-09095-y.
Opioid overdose can be serious adverse effects of opioid analgesics. Thus, several strategies to mitigate risk and reduce the harm of opioid overdose have been developed. However, despite a marked increase in opioid analgesic consumption in Korea, there have been no tools predicting the risk of opioid overdose in the Korean population. Using the national claims database of the Korean population, we identified patients who were incidentally prescribed non-injectable opioid analgesic (NIOA) at least once from 2017 to 2018 (N = 1,752,380). Among them, 866 cases of opioid overdose occurred, and per case, four controls were selected. Patients were randomly allocated to the development (80%) and validation (20%) cohort. Thirteen predictive variables were selected via logistic regression modelling, and a risk-score was assigned for each predictor. Our model showed good performance with c-statistics of 0.84 in the validation cohort. The developed risk score model is the first tool to identify high-risk patients for opioid overdose in Korea. It is expected to be applicable in the clinical setting and useful as a national level surveillance tool due to the easily calculable and identifiable predictors available from the claims database.
阿片类药物过量是阿片类镇痛药的严重不良影响。因此，已经开发了几种减轻风险和减少阿片类药物过量危害的策略。然而，尽管韩国阿片类镇痛药的消耗量显著增加，但尚未有工具能够预测韩国人群中阿片类药物过量的风险。本研究使用韩国人口的全国索赔数据库，确定了 2017 年至 2018 年至少有一次偶然开出处方非注射用阿片类镇痛药（NIOA）的患者（N=1752380）。其中，866 例发生阿片类药物过量，每例选择 4 例对照。患者被随机分配到开发（80%）和验证（20%）队列。通过逻辑回归模型选择了 13 个预测变量，并为每个预测因子分配了风险评分。我们的模型在验证队列中具有 0.84 的 c 统计量，表现出良好的性能。该风险评分模型是第一个可用于识别韩国阿片类药物过量高危患者的工具。由于索赔数据库中提供了易于计算和识别的预测因子，预计该模型将在临床环境中具有适用性，并可作为国家层面的监测工具。
