Hyesung Lee

Chimeric antigen receptor T-cell (CAR-T) therapies are a paradigm-shifting therapeutic in patients with hematological malignancies. However, some concerns remain that they may cause serious cardiovascular adverse events (AEs), for which data are scarce. In this study, gradient boosting machine algorithm-based model was fitted to identify safety signals of serious cardiovascular AEs reported for tisagenlecleucel in the World Health Organization Vigibase up until February 2024. Input dataset, comprised of positive and negative controls of tisagenlecleucel based on its labeling information and literature search, was used to train the model. Then, we implemented the model to calculate the predicted probability of serious cardiovascular AEs defined by preferred terms included in the important medical event list from European Medicine Agency. There were 467 distinct AEs from 3,280 safety cases reports for tisagenlecleucel, of which 363 (77.7%) were classified as positive controls, 66 (14.2%) as negative controls, and 37 (7.9%) as unknown AEs. The prediction model had area under the receiver operating characteristic curve of 0.76 in the test dataset application. Of the unknown AEs, six cardiovascular AEs were predicted as the safety signals: bradycardia (predicted probability 0.99), pleural effusion (0.98), pulseless electrical activity (0.89), cardiotoxicity (0.83), cardio-respiratory arrest (0.69), and acute myocardial infarction (0.58). Our findings underscore vigilant monitoring of acute cardiotoxicities with tisagenlecleucel therapy.

Background: Lazertinib is a third-generation tyrosine kinase inhibitor of epidermal growth factor receptor (EGFR-TKI) that selectively inhibit common EGFR mutation and T790M mutation in non-small-cell lung cancer (NSCLC) patients. No previous studies have compared lazertinib to platinum-based chemotherapy. We have compared lazertinib with platinum-based chemotherapy in EGFR-mutated NSCLC patients after previous EGFR-TKI therapy. Methods: We retrospectively compared 200 patients from LASER201, LASER301, and LASER-PMS studies to 334 patients who were treated with platinum-based chemotherapy after previous EGFR-TKI from the Samsung Medical Center. After propensity score matching (PSM), we selected 156 patients from each group. The primary outcome was progression-free survival (PFS), with overall survival (OS), objective response rate (ORR), and time to treatment discontinuation (TTD) as secondary outcomes. Results: The median follow-up of PFS was 15.61 months in the lazertinib group and 21.67 months in the external control group. The PFS was significantly longer in patients who were treated with lazertinib than those treated with platinum-based chemotherapy (10.97 months vs. 5.10 months; adjusted hazard ratio (HR) 0.40; 95% confidence interval (CI), 0.29–0.55; p < 0.01) after PSM. Lazertinib showed superior OS (32.23 months vs. 18.73 months; adjusted HR 0.45; 95% CI, 0.29–0.69; p < 0.001), ORR (64.1% vs. 47.4%), and TTD (11.66 months vs. 6.73 months; adjusted HR 0.54; 95% CI, 0.39–0.75; p < 0.001) compared to platinum-based chemotherapy. Conclusion: Based on this retrospective, external control study, lazertinib has demonstrated significantly better efficacy compared with platinum-based chemotherapy. The external controls provide important context to evaluate efficacy in single-arm studies.

Purpose: To assess the association between domperidone and adverse cardiovascular events.Methods: We conducted nested case‐control and case‐time‐control studies using Korea's healthcare database (2002‐2015). We identified patients without history of hospitalization, cancer, or cardiovascular diseases in 2002. From our cohort, those diagnosed with an adverse cardiovascular event (case), composite of arrhythmia, hypertension, or acute myocardial infarction were matched to two controls using risk‐set sampling on various sociodemographic variables. Exposure was assessed in the 1 to 7 days, or in the 1 to 7 days (hazard period) and 91 to 97 days (control period) prior to index date, in nested case‐control and case‐time control studies, respectively. We compared domperidone to metoclopramide or non‐use and estimated odds ratios (OR) with 95% confidence intervals (CI) using conditional logistic regression.Results: From 627 799 patients, we identified 71 555 cases and 141 833 controls. In the nested case‐control study, while the risk of cardiovascular events was increased with domperidone (OR 1.38, 95% CI 1.28‐1.47) compared to non‐use, the risk was reduced (0.64, 0.57‐0.72) compared to metoclopramide. In the case‐time‐control study, similar increased risk was found when compared to non‐use (1.40, 1.29‐1.52) but a reduced risk as compared with metoclopramide (0.63, 0.54‐0.72). Risk of myocardial infarction associated with domperidone was highest (nested case‐control: 1.94, 1.33‐2.83; case‐time‐control: 1.91, 1.01‐3.62) when compared to non‐use but did not indicate an increased risk when compared to metoclopramide (nested case‐control: 0.60, 0.32‐1.13; case‐time‐control: 0.70, 0.25‐1.98).Conclusion: Our findings support a positive association between domperidone and adverse cardiovascular events. However, domperidone may have a safer cardiovascular profile than metoclopramide.

There is ongoing debate as to whether angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin-receptor blockers (ARBs) use is associated with poor prognosis of coronavirus disease-2019 (COVID-19). We sought to investigate the association between ACEI/ARB use and risk of poor clinical outcomes from COVID-19. We identified 1,290 patients with hypertension, of which 682 had recorded ACEI/ARB use and 608 without the use during 30 days preceding the date of COVID-19 diagnosis in completely enumerated COVID-19 cohort in South Korea. Our primary endpoint was the clinical outcomes comprised of all-cause mortality, use of mechanical ventilation, intensive care unit (ICU) admission, and sepsis. We used inverse probability of treatment weighting (IPTW) to mitigate selection bias, and Poisson regression model to estimate the relative risks (RR) and 95% confidence intervals (CI) to compare outcomes in ACEI/ARB users with non-users. Compared to non-use, ACEI/ARB use was associated with lower clinical outcomes (IPTW adjusted RR, 0.60; 95% CI, 0.42-0.85; p=0.0046). When assessed by individual outcomes, ACEI/ARB use was not associated with all-cause mortality (IPTW adjusted RR, 0.62; 95% CI, 0.35-1.09; p=0.0973) and respiratory events (IPTW adjusted RR, 0.99; 95% CI, 0.84-1.17; p=0.9043). Subgroup analysis showed a trend toward protective role of ACEIs and ARBs against overall outcomes in men (IPTW adjusted RR, 0.84; 95% CI, 0.69-1.03; p-for-interaction=0.008) and with pre-existing respiratory disease (IPTW adjusted RR, 0.74; 95% CI, 0.60-0.92; p-for-interaction=0.0023). We present clinical evidence to support continuing ACE/ARB use in completely enumerated hypertensive COVID-19 cohort in South Korea.

Introduction: Diverse algorithms for signal detection exist. However, inconsistent results are often encountered among the algorithms due to different levels of specificity used in defining the adverse events (AEs) and signal threshold. We aimed to explore potential safety signals for two pneumococcal vaccines in a spontaneous reporting database and compare the results and performances among the algorithms. Methods: Safety surveillance was conducted using the Korea national spontaneous reporting database from 1988 to 2017. Safety signals for pneumococcal vaccine and its subtypes were detected using the following the algorithms: disproportionality methods comprising of proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC); empirical Bayes geometric mean (EBGM); and tree-based scan statistics (TSS). Moreover, the performances of these algorithms were measured by comparing detected signals with the known AEs or pneumococcal vaccines (reference standard). Results: Among 10,380 vaccine-related AEs, 1135 reports and 101 AE terms were reported following pneumococcal vaccine. IC generated the most safety signals for pneumococcal vaccine (40/101), followed by PRR and ROR (19/101 each), TSS (15/101), and EBGM (1/101). Similar results were observed for its subtypes. Cellulitis was the only AE detected by all algorithms for pneumococcal vaccine. TSS showed the best balance in the performance: the highest in accuracy, negative predictive value, and area under the curve (70.3%, 67.4%, and 64.2%). Conclusion: Discrepancy in the number of detected signals was observed between algorithms. EBGM and TSS calibrated noise better than disproportionality methods, and TSS showed balanced performance. Nonetheless, these results should be interpreted with caution due to a lack of a gold standard for signal detection.