Purpose: In men with metastatic castration-resistant prostate cancer (mCRPC), new bone lesions are sometimes not properly categorized through a confirmatory bone scan, and clinical significance of the test itself remains unclear. This study aimed to demonstrate the performance rate of confirmatory bone scans in a real-world setting and their prognostic impact in enzalutamide-treated mCRPC. Materials and Methods: Patients who received oral enzalutamide for mCRPC during 2014-2017 at 14 tertiary centers in Korea were included. Patients lacking imaging assessment data or insufficient drug exposure were excluded. The primary outcome was overall survival (OS). Secondary outcomes included performance rate of confirmatory bone scans in a real-world setting. Kaplan-Meier analysis and multivariate Cox regression analysis were performed. Results: Overall, 520 patients with mCRPC were enrolled (240 [26.2%] chemotherapy-naïve and 280 [53.2%] after chemotherapy). Among 352 responders, 92 patients (26.1%) showed new bone lesions in their early bone scan. Confirmatory bone scan was performed in 41 patients (44.6%), and it was associated with prolonged OS in the entire population (median, 30.9 vs. 19.7 months; p < 0.001), as well as in the chemotherapy-naïve (median, 47.2 vs. 20.5 months; p=0.011) and post-chemotherapy sub-groups (median, 25.5 vs. 18.0 months; p=0.006). Multivariate Cox regression showed that confirmatory bone scan performance was an independent prognostic factor for OS (hazard ratio 0.35, 95% confidence interval, 0.18 to 0.69; p=0.002). Conclusion Confirmatory bone scan performance was associated with prolonged OS. Thus, the premature discontinuation of enzalutamide without confirmatory bone scans should be discouraged.

Purpose: This study aimed to compare the short-term outcomes and safety profiles of androgen-deprivation therapy (ADT)+abiraterone/prednisone with those of ADT+docetaxel in patients with de novo metastatic hormone-sensitive prostate cancer (mHSPC). Materials and Methods: A web-based database system was established to collect prospective cohort data for patients with mHSPC in Korea. From May 2019 to November 2022, 928 patients with mHSPC from 15 institutions were enrolled. Among these patients, data from 122 patients who received ADT+abiraterone/prednisone or ADT+docetaxel as the primary systemic treatment for mHSPC were collected. The patients were divided into two groups: ADT+abiraterone/prednisone group (n=102) and ADT+docetaxel group (n=20). We compared the demographic characteristics, medical histories, baseline cancer status, initial laboratory tests, metastatic burden, oncological outcomes for mHSPC, progression after mHSPC treatment, adverse effects, follow-up, and survival data between the two groups. Results: No significant differences in the demographic characteristics, medical histories, metastatic burden, and baseline cancer status were observed between the two groups. The ADT+abiraterone/prednisone group had a lower prostate-specific antigen (PSA) progression rate (7.8% vs. 30.0%; p=0.011) and lower systemic treatment discontinuation rate (22.5% vs. 45.0%; p=0.037). No significant differences in adverse effects, oncological outcomes, and total follow-up period were observed between the two groups. Conclusions ADT+abiraterone/prednisone had lower PSA progression and systemic treatment discontinuation rates than ADT+docetaxel. In conclusion, further studies involving larger, double-blinded randomized trials with extended follow-up periods are necessary.

Background: Before the omicron era, health care workers were usually vaccinated with either the primary 2-dose ChAdOx1 nCoV-19 (Oxford-AstraZeneca) series plus a booster dose of BNT162b2 (Pfizer-BioNTech) (CCB group) or the primary 2-dose BNT162b2 series plus a booster dose of BNT162b2 (BBB group) in Korea. Methods: The two groups were compared using quantification of the surrogate virus neutralization test for wild type severe acute respiratory syndrome coronavirus 2 (SVNT-WT), the omicron variant (SVNT-O), spike-specific IgG, and interferon-gamma (IFN-γ), as well as the omicron breakthrough infection cases. Results: There were 113 participants enrolled in the CCB group and 51 enrolled in the BBB group. Before and after booster vaccination, the median SVNT-WT and SVNT-O values were lower in the CCB (SVNT-WT [before-after]: 72.02–97.61%, SVNT-O: 15.18–42.29%) group than in the BBB group (SVNT-WT: 89.19–98.11%, SVNT-O: 23.58–68.56%; all P < 0.001). Although the median IgG concentrations were different between the CCB and BBB groups after the primary series (2.677 vs. 4.700 AU/mL, respectively, P < 0.001), they were not different between the two groups after the booster vaccination (7.246 vs. 7.979 AU/mL, respectively, P = 0.108). In addition, the median IFN-γ concentration was higher in the BBB group than in the CCB group (550.5 and 387.5 mIU/mL, respectively, P = 0.014). There was also a difference in the cumulative incidence curves over time (CCB group 50.0% vs. BBB group 41.8%; P = 0.045), indicating that breakthrough infection occurred faster in the CCB group. Conclusion The cellular and humoral immune responses were low in the CCB group so that the breakthrough infection occurred faster in the CCB group than in the BBB group.

Despite the accuracy of nucleic acid amplification tests (NAATs), rapid antigen tests (RATs) for severe acute respiratory syndrome coronavirus-2 are widely used as point-of-care tests. A total of 282 pairs of reverse transcription-polymerase chain reaction and Standard Q COVID-19 Ag tests were serially conducted for 68 patients every 3–4 days until their discharge. Through a field evaluation of RATs using direct nasopharyngeal swabs, the sensitivities were 84.6% and 87.3% for E and RNA-dependent RNA polymerase (RdRp) genes, respectively, for specimens with cycle thresholds (Cts) < 25. The Ct values of E and RdRp genes for 95% detection rates by RATs were 16.9 and 18.1, respectively. The sensitivity of RAT was 48.4% after the onset of symptoms, which was not sufficient. RAT positivity gradually decreased with increased time after symptom onset and had continuously lower sensitivity than NAATs.

Background: During the coronavirus disease 2019 (COVID-19) pandemic, the incidence of rhinovirus (RV) is inversely related to the intensity of non-pharmacological interventions (NPIs), such as universal mask wearing and physical distancing. Methods: Using RV surveillance data, changes in the effect of NPIs were investigated in South Korea during the pandemic. The time to the first visible effect of NPIs after the onset of NPIs (T1), time to the maximum effect (T2), and duration of the maximum effect (T3) were measured for each surge. For each week, the RVdiff [(RV incidence during the pandemic) − (RV incidence within 5 years before the pandemic)] was calculated, and number of weeks for RVdiff to be below zero after NPIs (time to RVdiff ≤ 0) and number of weeks RVdiff remains below zero after NPIs (duration of RVdiff ≤ 0) were measured for each surge. Results: During the study period, four surges of COVID-19 were reported. As the pandemic progressed, T1 and T2 increased, but T3 decreased. Additionally, the “time to RVdiff of ≤ 0” increased and “duration of RVdiff of ≤ 0” decreased. These changes became more pronounced during the third surge (mid-November 2020), before the introduction of the COVID-19 vaccine, and from the emergence of the delta variant. Conclusion The effect of NPIs appears slower, the duration of the effect becomes shorter, and the intensity also decreases less than a year after the onset of the pandemic owing to people’s exhaustion in implementing NPIs. These findings suggest that the COVID-19 response strategy must be completely overhauled

Background: There has been no comparison of the determinants of admission route between acute ischemic stroke (AIS) and acute myocardial infarction (AMI). We examined whether factors associated with direct versus transferred-in admission to regional cardiocerebrovascular centers (RCVCs) differed between AIS and AMI. Methods: Using a nationwide RCVC registry, we identified consecutive patients presenting with AMI and AIS between July 2016 and December 2018. We explored factors associated with direct admission to RCVCs in patients with AIS and AMI and examined whether those associations differed between AIS and AMI, including interaction terms between each factor and disease type in multivariable models. To explore the influence of emergency medical service (EMS) paramedics on hospital selection, stratified analyses according to use of EMS were also performed. Results: Among the 17,897 and 8,927 AIS and AMI patients, 66.6% and 48.2% were directly admitted to RCVCs, respectively. Multivariable analysis showed that previous coronary heart disease, prehospital awareness, higher education level, and EMS use increased the odds of direct admission to RCVCs, but the odds ratio (OR) was different between AIS and AMI (for the first 3 factors, AMI > AIS; for EMS use, AMI < AIS). EMS use was the single most important factor for both AIS and AMI (OR, 4.72 vs. 3.90). Hypertension and hyperlipidemia increased, while living alone decreased the odds of direct admission only in AMI;additionally, age (65–74 years), previous stroke, and presentation during non-working hours increased the odds only in AIS. EMS use weakened the associations between direct admission and most factors in both AIS and AMI. Conclusions Various patient factors were differentially associated with direct admission to RCVCs between AIS and AMI. Public education for symptom awareness and use of EMS is essential in optimizing the transportation and hospitalization of patients with AMI and AIS.

Background: Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM.Material and Methods: For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machinelearning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. Results: The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machinelearning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03). Conclusion The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.

Background: and Purpose: Blood pressure (BP) control is strongly recommended, but BP control rate has not been well studied in patients with stroke. We evaluated the BP control rate with fimasartan-based antihypertensive therapy initiated in patients with recent cerebral ischemia. Methods: This multicenter, prospective, single-arm trial involved 27 centers in South Korea. Key inclusion criteria were recent cerebral ischemia within 90 days and high BP [systolic blood pressure (SBP) >140 mm Hg or diastolic blood pressure (DBP) >90 mm Hg]. BP lowering was initiated with fimasartan. BP management during the follow-up was at the discretion of the responsible investigators. The primary endpoint was the target BP goal achievement rate (<140/90 mm Hg) at 24 weeks. Key secondary endpoints included achieved BP and BP changes at each visit, and clinical events (ClinicalTrials.gov Identifier: NCT03231293). Results: Of 1,035 patients enrolled, 1,026 were included in the safety analysis, and 951 in the efficacy analysis. Their mean age was 64.1 years, 33% were female, the median time interval from onset to enrollment was 10 days, and the baseline SBP and DBP were 162.3±16.0 and 92.2±12.4 mm Hg (mean±SD). During the study period, 55.5% of patients were maintained on fimasartan monotherapy, and 44.5% received antihypertensive therapies other than fimasartan monotherapy at at least one visit. The target BP goal achievement rate at 24-week was 67.3% (48.6% at 4-week and 61.4% at 12-week). The mean BP was 139.0/81.8±18.3/11.7, 133.8/79.2±16.4/11.0, and 132.8/78.5±15.6/10.9 mm Hg at 4-, 12-, and 24-week. The treatment-emergent adverse event rate was 5.4%, including one serious adverse event. Conclusions Fimasartan-based BP lowering achieved the target BP in two-thirds of patients at 24 weeks, and was generally well tolerated.

Background: Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM.Material and Methods: For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machinelearning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. Results: The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machinelearning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03). Conclusion The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.