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BackgroundOral anticoagulants (OACs) are essential for the prevention and treatment of thromboembolic disorders, but bleeding, a major complication, can have a fatal impact on the patient’s treatment.ObjectivesWe aimed to estimate the nationwide, real-world incidence rate of bleeding in patients taking OACs and confirm the incidence by indications and risk factors.MethodsThis cross-sectional study identified OAC users from April 1 to December 31, in both 2019 and 2020, using the HIRA-NPS database. The primary outcome variables were the incidence rate of major bleeding events during OAC treatment and within 30 days of treatment discontinuation. We estimated the adjusted incidence rate ratio (aIRR) in subpopulations.ResultsAmong 18,822 OAC users, the incidence rate of major bleeding was 27.9 (95% CI: 24.6–31.5) per 1,000 person-years. The incidence rate of major bleeding was higher in patients with a bleeding history, with an aIRR of 11.51; those at high bleeding risk (HAS-BLED score ≥3), with an aIRR of 1.51; those with high CCI scores ≥3, with an aIRR of 1.88; and those with liver disease, with an aIRR of 1.41. For indications, compared to patients with nonvalvular atrial fibrillation (NVAF), the aIRR of major bleeding was significantly higher at an aIRR of 2.35 in patients undergoing VTE treatment. Patients with ischemic stroke showed a higher incidence of major bleeding with an aIRR of 2.13 than NVAF patients. The aIRR of major bleeding in the oral anticoagulant group, compared to the matched control group, was 2.25 (95% CI: 1.93–2.63).ConclusionThese findings may be useful for implementing strategies to improve the evaluation and management of anticoagulation-related bleeding.

Background: The rising global incidence of cancer has increased the demand for chemotherapy, which is a crucial treatment modality. Recent advancements in cancer treatment, including targeted agents and immunotherapy, have introduced complications owing to their specific mechanisms. However, comprehensive studies of the combined complications of these approaches are lacking. Objectives: This study aimed to comprehensively assess and analyze the overall incidence of anticancer drug-related complications in a nationwide patient cohort, utilizing a customized National Health Insurance Sharing Service database in Korea. Design: Retrospective cohort study. Methods: We included patients who were prescribed anticancer drugs (excluding endocrine agents) and diagnosed with cancer. For the type of cancer classification, the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) was used and anticancer drugs were classified based on the Anatomical Therapeutic Chemical code. We classified cancer into 18 types based on the ICD-10 code and delineated cancer-related complications into 12 categories. Complications included hematological, gastrointestinal, infectious, cardiovascular, major bleeding, endocrine, neurotoxic, nephrotoxic, dermatological, pulmonary, musculoskeletal, and hepatotoxic effects. Result: We included 294,544 patients diagnosed with cancer and administered anticancer drugs between 2016 and 2018, with follow-up continuing until 2021. We identified 486,929 anticancer drug-related complications, with an incidence of 1843.6 per 1000 person-years (PY). Anemia was the most common complication, with a rate of 763.7 per 1000 PY, followed by febrile neutropenia (295.7) and nausea/vomiting (246.9). Several complications peaked during the first months following the initiation of anticancer drug therapy; however, herpes, skin infection, heart failure, and peripheral neuropathy peaked at 6–12 months. Among major cancers, breast cancer had the lowest overall incidence of complications. Targeted therapies revealed lower complication rates than cytotoxic chemotherapy; however, they also required careful monitoring of rash. Conclusion: This study highlights the importance of the proactive management of anticancer drug-related complications for patient care improvement.

Abstract This study developed and validated a risk-scoring model, with a particular emphasis on medication-related factors, to predict emergency department (ED) visits among older Korean adults (aged 65 and older) undergoing anti-neoplastic therapy. Utilizing national claims data, we constructed two cohorts: the development cohort (2016–2018) with 34,642 patients and validation cohort (2019) with 10,902 patients. The model included a comprehensive set of predictors: demographics, cancer type, comorbid conditions, ED visit history, and medication use variables. We employed the least absolute shrinkage and selection operator (LASSO) regression to refine and select the most relevant predictors. Out of 120 predictor variables, 12 were integral to the final model, including seven related to medication use. The model demonstrated acceptable predictive performance in the validation cohort with a C-statistic of 0.76 (95% CI 0.74–0.77), indicating reasonable calibration. This risk-scoring model, after further clinical validation, has the potential to assist healthcare providers in the effective management and care of older patients receiving anti-neoplastic therapy.

Abstract Background Falls impact over 25% of older adults annually, making fall prevention a critical public health focus. We aimed to develop and validate a machine learning-based prediction model for serious fall-related injuries (FRIs) among community-dwelling older adults, incorporating various medication factors. Methods Utilizing annual national patient sample data, we segmented outpatient older adults without FRIs in the preceding three months into development and validation cohorts based on data from 2018 and 2019, respectively. The outcome of interest was serious FRIs, which we defined operationally as incidents necessitating an emergency department visit or hospital admission, identified by the diagnostic codes of injuries that are likely associated with falls. We developed four machine-learning models (light gradient boosting machine, Catboost, eXtreme Gradient Boosting, and Random forest), along with a logistic regression model as a reference. Results In both cohorts, FRIs leading to hospitalization/emergency department visits occurred in approximately 2% of patients. After selecting features from initial set of 187, we retained 26, with 15 of them being medication-related. Catboost emerged as the top model, with area under the receiver operating characteristic of 0.700, along with sensitivity and specificity rates around 65%. The high-risk group showed more than threefold greater risk of FRIs than the low-risk group, and model interpretations aligned with clinical intuition. Conclusion We developed and validated an explainable machine-learning model for predicting serious FRIs in community-dwelling older adults. With prospective validation, this model could facilitate targeted fall prevention strategies in primary care or community-pharmacy settings.

Conclusions: Pharmacist-led BPMH significantly improved medication history accuracy compared to conventional methods, which had inaccuracies in over 70%. It is important to be careful when obtaining BPMH for older adults, particularly those with complex medication regimens, nonoral medications, or PRN medications, to enhance patient safety and reduce medication-related risks.

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.

Introduction: The aim of this study was to investigate trends in the prevalence of potentially inappropriate opioid prescribing (PIOP) and identify potential risk factors among Korean noncancer patients. Methods: We conducted a cross-sectional study of annual national patient sample data from the Korean Health Insurance Review and Assessment Service (HIRA-NPS) for the period 2012–2018. Noncancer patients who were prescribed non-injectable opioid analgesics (NIOAs) at least once were included. The proportion of patients with at least one PIOP in terms of concurrent use of benzodiazepines or gabapentinoids, substance use disorder, treatment duration, and dosage was evaluated. Multivariable logistic regression was performed to identify the risk factors associated with PIOP. Results: Of the 9,772,503 noncancer patients, 1,583,444 (16.2%) were prescribed NIOAs at least once. Among them, 15.7% were exposed to PIOP, and the prevalence was much higher (31.6%) in the elderly group (age: ⩾65 years). The prevalence of PIOP increased 1.1-fold over 7 years (14.8–16.8%) among the total NIOA users and was more pronounced in non-tramadol NIOA users (a 1.5-fold increase, from 13.2% to 19.4%). Multivariable logistic regression indicated that older age, beneficiaries of medical aid or national meritorious service, exposure to polypharmacy, psychological disorder, chronic pain indication, and concomitant sedative use were independently associated with higher odds of PIOP. Discussion and Conclusion: We found that the prevalence of PIOP was 15.7% among Korean noncancer patients, and it increased over the 7-year study period. This increasing trend is alarming because it was more drastic with non-tramadol NIOAs compared with that with tramadol. Several patient-level risk factors associated with PIOP would be useful in targeted management strategies for the safe use of opioids. Plain Language Summary Potentially inappropriate opioid prescribing and related risk factors among noncancer patients prescribed non-injectable opioids in Korea In Korea, the prevalence of non-injectable opioid analgesic (NIOA) use in noncancer patients steadily increased from 15.3% in 2012 to 17.1% in 2018. Also, the prevalence of potentially inappropriate opioid prescribing (PIOP) increased from 14.8% in 2012 to 16.8% in 2018. The following factors were associated with a markedly increased risk of PIOP: age, beneficiaries of medical aid or national meritorious service, polypharmacy, psychological disorder, chronic pain, and concomitant medications.

Background and Objectives: Opioid use in Korea is lower than in other developed countries. However, recent studies have reported an increase in opioid prescriptions and the number of chronic opioid users. The current status of adverse events (AEs) associated with opioid analgesics in Korea is unclear. This nested case–control study aimed to evaluate the influence of opioid analgesic use patterns on all emergency department (ED) visits and opioid-related ED visits after opioid analgesic initiation using the national claims database. Materials and Methods: Adult non-cancer patients who initiated non-injectable opioid analgesics (NIOA) between January 2017 and June 2018 were included. We defined the case group as patients who visited the ED within six months of opioid initiation, and the control group was selected in a 1:1 ratio using an exact matching method. Results: A total of 97,735 patients (13.58%) visited the ED within six months of NIOA initiation. Nearly 32% of cases were linked to opioid-related AEs. The most frequent AEs were falls and fractures (61.27%). After adjusting for covariates, opioid initiation at the ED was associated with all-cause or opioid-related ED visits (adjusted odds ratio (aOR) = 3.19, 95% confidence interval (CI) = 3.09–3.29; aOR = 3.82, 95% CI = 3.62–4.04, respectively). Chronic NIOA use was associated with all-cause and opioid-related ED visits (aOR = 1.32, 95% CI = 1.23–1.40; aOR = 1.56, 95% CI = 1.39–1.76, respectively). Conclusion: This study found that 13% of non-cancer patients visited the ED within six months of NIOA initiation. In addition, the NIOA use pattern was significantly associated with all-cause and opioid-related ED visits.

Background and objectives: We aimed to describe medication-related incidents or medication errors (MEs) reported by community pharmacists and analyze the prevalent medications involved. Materials and Methods: We extracted ME reports from databases comprising patient safety incidents reported to the Korean Pharmaceutical Association between January 2013 and June 2021. Medications were analyzed according to the second (therapeutic subgroup) and fifth (chemical substance) levels of the Anatomical Therapeutic Chemical classification. Results: A total of 9046 MEs were identified, most of which were near miss reports (88.3%). Among the errors that reached the patients (521 cases), harmful incidents accounted for 76.8%. Most MEs occurred during prescription (89.5%), while harmful MEs occurred mainly during dispensing (73.3%). In the prescription step, wrong drugs (44.8%), dosing errors (27.0%), and wrong durations (14.0%) were common. Anti-inflammatory and anti-rheumatic products (M01), drugs for acid-related disorders (A02), and antihistamines for systemic use (R06) were the most frequently reported medication classes involved. Harmful incidents were most common for dosing errors (31.0%) and wrong drugs (26.8%) and were common with warfarin, levothyroxine, and glimepiride. Conclusions: The MEs reported by community pharmacists were mainly prescribing errors, most of which were rectified before reaching patients. The prevalent medications involved in harmful errors include anti-diabetic, anti-thrombotic, and anti-inflammatory agents.