Objectives: This study evaluated the effectiveness of government epidemic control policies centered on diagnostic testing and examined their impact on the in vitro diagnostics (IVD) industry. It also analyzed the complex interplay among policy interventions, epidemic dynamics, and the IVD industry’s value chain to identify key leverage points for managing future public health crises. Methods: A system dynamics (SD) model calibrated using national data from the Republic of Korea simulated the interactions between epidemic progression and the IVD value chain.We conducted a scenario analysis encompassing 6 policy interventions: research and development (R&D) investment, public–private collaboration, regulatory easing, diagnostic test performance, testing intensity, and social distancing. Results: Policies promoting investment, public–private collaboration, and regulatory easing accelerated the market entry of diagnostics, thereby reducing infections and deaths. However, these interventions were associated with lower overall industry revenue, attributable toincreased market competition and a reduced patient population. A critical trade-off was noted:although regulatory speed is advantageous, using low-sensitivity diagnostics substantially worsened public health outcomes. Aggressive testing strategies and stringent social distancingwere also confirmed to be effective in reducing both infections and mortality. Conclusion This study provides a strategic framework for understanding interactions between pandemic control policies and the IVD industry. Sustained pre-crisis investment in R&D,public–private networks, and public health infrastructure is essential for effective pandemicpreparedness. During a crisis, policymakers must carefully manage the critical trade-off between regulatory speed and diagnostic quality to ensure that rapid responses do not compromise public health outcomes.

Purpose: Radiotherapy is used to reduce the risk of breast cancer recurrence after surgery, but it is a potential cause of secondary cancer. We validated the risk of secondary cancer in primary breast cancer who received radiotherapy compared with those who did not from a matched cohort using a large-scale observational study of the Observational Health Data Sciences and Informatics (OHDSI) data network. Materials and Methods: A retrospective comparative cohort study using propensity score-matched cohorts was performed using two Observational Medical Outcome Partnership common data model databases, from tertiary general hospitals in South Korea. Among female patients who underwent surgery after the diagnosis of breast cancer, the risk of secondary primary malignant occurrence after 1:1 matching was analyzed. Results: Among 27,078 patients with breast cancer, there was no significant difference in the risk of secondary cancer following radiotherapy in 4,426 patients after 1:1 propensity-score matching. Further, there were no significant differences in the sensitivity analysis according to age, latency period, and number of radiation treatments. Conclusion There was no difference in the risk of secondary cancer in the patients diagnosed with breast cancer depending on whether or not radiotherapy was performed after surgery. In the future, it is necessary to analyze including data generated during cancer treatment.

Background: Dipeptidyl peptidase-4 (DPP4) inhibitors are frequently prescribed for patients with type 2 diabetes; however, their cost can pose a significant barrier for those with impaired kidney function. This study aimed to estimate the economic benefits of substituting non-renal dose-adjusted (NRDA) DPP4 inhibitors with renal dose-adjusted (RDA) DPP4 inhibitors in patients with both impaired kidney function and type 2 diabetes. Methods: This retrospective cohort study was conducted from January 1, 2012 to December 31, 2018, using data obtained from common data models of five medical centers in Korea. Model 1 applied the prescription pattern of participants with preserved kidney function to those with impaired kidney function. In contrast, model 2 replaced all NRDA DPP4 inhibitors with RDA DPP4 inhibitors, adjusting the doses of RDA DPP4 inhibitors based on individual kidney function. The primary outcome was the cost difference between the two models. Results: In total, 67,964,996 prescription records were analyzed. NRDA DPP4 inhibitors were more frequently prescribed to patients with impaired kidney function than in those with preserved kidney function (25.7%, 51.3%, 64.3%, and 71.6% in patients with estimated glomerular filtration rates [eGFRs] of ≥60, <60, <45, and <30 mL/min/1.73 m2, respectively). When model 1 was applied, the cost savings per year were 7.6% for eGFR <60 mL/min/1.73 m2 and 30.4% for eGFR <30 mL/min/1.73 m2. According to model 2, 15.4% to 51.2% per year could be saved depending on kidney impairment severity. Conclusion Adjusting the doses of RDA DPP4 inhibitors based on individual kidney function could alleviate the economic burden associated with medical expenses.

This retrospective cohort study aimed to compare coronavirus disease 2019 (COVID-19)-related clinical outcomes between patients with and without gout. Electronic health recordbased data from two centers (Seoul National University Hospital [SNUH] and Boramae Medical Center [BMC]), from January 2021 to April 2022, were mapped to a common data model. Patients with and without gout were matched using a large-scale propensityscore algorithm based on population-level estimation methods. At the SNUH, the risk for COVID-19 diagnosis was not significantly different between patients with and without gout (hazard ratio [HR], 1.07; 95% confidence interval [CI], 0.59–1.84). Within 30 days after COVID-19 diagnosis, no significant difference was observed in terms of hospitalization (HR, 0.57; 95% CI, 0.03–3.90), severe outcomes (HR, 2.90; 95% CI, 0.54–13.71), or mortality (HR, 1.35; 95% CI, 0.06–16.24). Similar results were obtained from the BMC database, suggesting that gout does not increase the risk for COVID-19 diagnosis or severe outcomes.

Purpose: Urothelial carcinoma has various molecular subtypes, each with different tumor characteristics. Although it is known that molecular changes occur during tumor progression, little is known about the specifics of these changes. In this study, we performed transcriptional analysis to understand the molecular changes during tumor progression. Materials and Methods: Formalin-fixed, paraffin-embedded tumor tissues were obtained from 12 patients with muscle-invasive bladder cancer (MIBC). The invasive and non-invasive papillary areas were identified in papillary urothelial carcinoma specimens.Immunohistochemistry (IHC) and mRNA sequencing were performed for each tumor area. Results: Patients with CK5/6-negative and CK20-positive non-invasive papillary areas were selected and classified into the IHC switch subgroup (CK5/6-positive and CK20-negative in the invasive area) and the IHC unchanged subgroup (CK5/6-negative and CK20-positive in the invasive area) according to the IHC results of the invasive area. We identified differences in the mRNA expression between the non-invasive papillary and invasive areas of the papillary MIBC tissue samples. In both the non-invasive papillary and invasive areas, the IHC switch subgroup showed basal subtype gene expression, while the IHC unchanged subgroup demonstrated luminal subtype gene expression. Conclusions The non-invasive papillary area showed a gene expression pattern similar to that of the invasive area. Therefore, even if the non-invasive papillary area exhibits a luminal phenotype on IHC, it can have a basal subtype gene expression depending on the invasive area.

Objectives: The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. Methods: A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable timeseries model. Results: The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. Conclusions Implementing a multicenter-based timeseries classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.