Development and validation of a risk-score model for opioid overdose using a national claims database

Abstract

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.

Risk Factors for Emergency Department Presentations after the Initiation of Opioid Analgesics in Non-Cancer Patients in Korea: A Nationwide Study

“…Considering this discrepancy in the identified categories, ED visits in Korea could be lower than those in the United States. This finding is in line with a previous study in Korea, which reported that the incidence of opioid overdoses in NIOA incident users (0.05%) was lower than that in previous studies (0.4–0.6%) [ 24 ].…”

Development and validation of a predictive model for persistent opioid use in new opioid analgesic users via a nationwide claims database

“…Chronic opioid use is also linked to the development of opioid use disorder and abuse 44 . Our previous study also confirmed that the number of NIOA prescriptions was the strongest predictor of opioid overdose 23 . By facilitating the early identification of patients at an elevated risk of transitioning to chronic opioid use, our risk score prediction model enables individualized pain management strategies and informed discussions with patients.…”

“…44 Our previous study also confirmed that the number of NIOA prescriptions was the strongest predictor of opioid overdose. 23 By facilitating the early identification of patients at an elevated risk of transitioning to chronic opioid use, our risk score prediction model enables individualized pain management strategies and informed discussions with patients. Consequently, it may help mitigate chronic opioid use and its associated adverse outcomes.…”

“…We derived the prediction model for chronic NIOA use from a training cohort (80% of overall study patients) and validated it using the test cohort (20% of overall study patients) 18,19 . In accordance with the method used in previous studies, the dataset was divided into training and test cohorts using stratified random selection according to outcome 20–23 . Although the number of participants in this study was considered sufficiently large, a sample size calculation was performed to ensure adequacy.…”

“…18,19 In accordance with the method used in previous studies, the dataset was divided into training and test cohorts using stratified random selection according to outcome. [20][21][22][23] Although the number of participants in this study was considered sufficiently large, a sample size calculation was performed to ensure adequacy. When confirming the sample size using the known incidence (0.05), 24 number of predictors (24), shrinkage factor (0.9), and R 2 Nagelkerke value (0.15), the number of patients in the training cohort exceeded the calculated sample size (4243).…”