Purpose: Predicting improvements in voiding symptoms following deobstructive surgery for male lower urinary tract symptoms/benign prostatic hyperplasia (LUTS/BPH) is challenging when detrusor contractility is impaired. This study aimed to develop an artificial intelligence model that predicts symptom improvement after holmium laser enucleation of the prostate (HoLEP), focusing on changes in maximum flow rate (MFR) and voiding efficiency (VE) 1-month postsurgery. Methods: We reviewed 1,933 patients who underwent HoLEP at Samsung Medical Center from July 2008 to January 2024. The study employed a deep neural network (DNN) for multiclass classification to predict changes in MFR and VE, each divided into 3 categories. For comparison, additional machine learning (ML) models such as extreme gradient boosting, random forest classification, and support vector machine were utilized. To address class imbalance, we applied the least squares method and multitask learning. Results: A total of 1,142 patients with complete data were included in the study, with 992 allocated for model training and 150 for external validation. In predicting MFR, the DNN achieved a microaverage area under the receiver operating characteristic curve (AUC) of 0.884±0.006, sensitivity of 0.783±0.020, and specificity of 0.891±0.010. For VE prediction, the microaverage AUC was 0.817±0.007, with sensitivity and specificity values of 0.660±0.014 and 0.830±0.007, respectively. These results indicate that the DNN's predictive performance was superior to that of other ML models. Conclusions The DNN model provides detailed and accurate predictions for recovery after HoLEP, providing valuable insights for clinicians managing patients with LUTS/BPH.

Purpose: Predicting improvements in voiding symptoms following deobstructive surgery for male lower urinary tract symptoms/benign prostatic hyperplasia (LUTS/BPH) is challenging when detrusor contractility is impaired. This study aimed to develop an artificial intelligence model that predicts symptom improvement after holmium laser enucleation of the prostate (HoLEP), focusing on changes in maximum flow rate (MFR) and voiding efficiency (VE) 1-month postsurgery. Methods: We reviewed 1,933 patients who underwent HoLEP at Samsung Medical Center from July 2008 to January 2024. The study employed a deep neural network (DNN) for multiclass classification to predict changes in MFR and VE, each divided into 3 categories. For comparison, additional machine learning (ML) models such as extreme gradient boosting, random forest classification, and support vector machine were utilized. To address class imbalance, we applied the least squares method and multitask learning. Results: A total of 1,142 patients with complete data were included in the study, with 992 allocated for model training and 150 for external validation. In predicting MFR, the DNN achieved a microaverage area under the receiver operating characteristic curve (AUC) of 0.884±0.006, sensitivity of 0.783±0.020, and specificity of 0.891±0.010. For VE prediction, the microaverage AUC was 0.817±0.007, with sensitivity and specificity values of 0.660±0.014 and 0.830±0.007, respectively. These results indicate that the DNN's predictive performance was superior to that of other ML models. Conclusions The DNN model provides detailed and accurate predictions for recovery after HoLEP, providing valuable insights for clinicians managing patients with LUTS/BPH.

Purpose: Predicting improvements in voiding symptoms following deobstructive surgery for male lower urinary tract symptoms/benign prostatic hyperplasia (LUTS/BPH) is challenging when detrusor contractility is impaired. This study aimed to develop an artificial intelligence model that predicts symptom improvement after holmium laser enucleation of the prostate (HoLEP), focusing on changes in maximum flow rate (MFR) and voiding efficiency (VE) 1-month postsurgery. Methods: We reviewed 1,933 patients who underwent HoLEP at Samsung Medical Center from July 2008 to January 2024. The study employed a deep neural network (DNN) for multiclass classification to predict changes in MFR and VE, each divided into 3 categories. For comparison, additional machine learning (ML) models such as extreme gradient boosting, random forest classification, and support vector machine were utilized. To address class imbalance, we applied the least squares method and multitask learning. Results: A total of 1,142 patients with complete data were included in the study, with 992 allocated for model training and 150 for external validation. In predicting MFR, the DNN achieved a microaverage area under the receiver operating characteristic curve (AUC) of 0.884±0.006, sensitivity of 0.783±0.020, and specificity of 0.891±0.010. For VE prediction, the microaverage AUC was 0.817±0.007, with sensitivity and specificity values of 0.660±0.014 and 0.830±0.007, respectively. These results indicate that the DNN's predictive performance was superior to that of other ML models. Conclusions The DNN model provides detailed and accurate predictions for recovery after HoLEP, providing valuable insights for clinicians managing patients with LUTS/BPH.

Purpose: This subgroup analysis of the Korean subset of patients in the phase 3 LASER301 trial evaluated the efficacy and safety of lazertinib versus gefitinib as first-line therapy for epidermal growth factor receptor mutated (EGFRm) non–small cell lung cancer (NSCLC). Materials and Methods: Patients with locally advanced or metastatic EGFRm NSCLC were randomized 1:1 to lazertinib (240 mg/day) or gefitinib (250 mg/day). The primary endpoint was investigator-assessed progression-free survival (PFS). Results: In total, 172 Korean patients were enrolled (lazertinib, n=87; gefitinib, n=85). Baseline characteristics were balanced between the treatment groups. One-third of patients had brain metastases (BM) at baseline. Median PFS was 20.8 months (95% confidence interval [CI], 16.7 to 26.1) for lazertinib and 9.6 months (95% CI, 8.2 to 12.3) for gefitinib (hazard ratio [HR], 0.41; 95% CI, 0.28 to 0.60). This was supported by PFS analysis based on blinded independent central review. Significant PFS benefit with lazertinib was consistently observed across predefined subgroups, including patients with BM (HR, 0.28; 95% CI, 0.15 to 0.53) and those with L858R mutations (HR, 0.36; 95% CI, 0.20 to 0.63). Lazertinib safety data were consistent with its previously reported safety profile. Common adverse events (AEs) in both groups included rash, pruritus, and diarrhoea. Numerically fewer severe AEs and severe treatment–related AEs occurred with lazertinib than gefitinib. Conclusion Consistent with results for the overall LASER301 population, this analysis showed significant PFS benefit with lazertinib versus gefitinib with comparable safety in Korean patients with untreated EGFRm NSCLC, supporting lazertinib as a new potential treatment option for this patient population.

Emergency department (ED) triage systems are used to classify the severity and urgency of emergency patients, and Korean medical institutions use the Korean Triage and Acuity Scale (KTAS). During the COVID-19 pandemic, appropriate treatment for emergency patients was delayed due to various circumstances, such as overcrowding of EDs, lack of medical workforce resources, and increased workload on medical staff. The purpose of this study was to evaluate the accuracy of the KTAS in predicting the urgency of emergency patients during the COVID-19 pandemic. Methods This study retrospectively reviewed patients who were treated in the ED during the pandemic period from January 2020 to June 2021. Patients were divided into COVID-19–screening negative (SN) and COVID-19–screening positive (SP) groups. We compared the predictability of the KTAS for urgent patients between the two groups. Results From a total of 107,480 patients, 62,776 patients (58.4%) were included in the SN group and 44,704 (41.6%) were included in the SP group. The odds ratios for severity variables at each KTAS level revealed a more evident discriminatory power of the KTAS for severity variables in the SN group (P<0.001). The predictability of the KTAS for severity variables was higher in the SN group than in the SP group (area under the curve, P<0.001). Conclusion During the pandemic, the KTAS had low accuracy in predicting patients in critical condition in the ED. Therefore, in future pandemic periods, supplementation of the current ED triage system should be considered in order to accurately classify the severity of patients.

Background: The importance of digital technology is increasing among older adults. In this study, the digital health technology utilization status, purpose, and satisfaction of older adults were investigated according to frailty. Methods: A face-to-face survey was conducted among adults aged 65 years or older. Frailty was defined using the Korean version of the fatigue, resistance, ambulation, illnesses, and loss of weight scale. Results: A total of 505 participants completed the survey, with 153 (30.3%) identified as pre-frail or frail and 352 (69.7%) as healthy. All respondents used smartphones; 440 (87.1%) were application users, and 290 (57.4%) were healthcare application users. Wearable devices were used by only 36 patients (7.1%). Pre-frail or frail respondents used social media more frequently than healthy respondents (19.4% vs. 7.4%, P < 0.001). Among the respondents, 319 (63.2%) were not able to install or delete the application themselves, and 277 (54.9%) stated that the application was recommended by their children (or partner). Pre-frail and frail respondents used more healthcare applications to obtain health information (P = 0.002) and were less satisfied with wearable devices (P = 0.02). Conclusion The usage rate of digital devices, including mobile phones among older adults in Korea is high, whereas that of wearable devices is low. There was a notable difference in the services used by pre-frail and frail respondents compared to healthy respondents. Therefore, when developing digital devices for pre-frail and frail older adults, it is crucial to incorporate customized services that meet their unique needs, particularly those services that they frequently use.

Background: This study aimed to identify changes in counting error prevention awareness and patient safety perception through counting error prevention education to operating room nurses. Methods: This was a one-group, pretest-posttest, quasi-experimental study. The participants were operating room nurses. A total of 46 nurses were recruited, and data was collected between October 1 and 31, 2022. Measures used for this study assessed counting error prevention awareness and patient safety perception. The data were analyzed using paired t-tests. Results: The counting error prevention awareness of the participants increased significantly from 3.68 to 3.95 points before and after education, respectively (t=-5.07, P<0.001), while patient safety perception significantly increased from 3.95 to 4.20 points before and after training, respectively (t=-2.68, P=0.010). Conclusions Counting error prevention awareness and patient safety perception of operating room nurses prevent fatal damage to patients with surgeries and lower mortality. The results of this study suggest the necessity of various education methods to reduce medical accidents among surgical patients and to raise patient safety perception for operating room nurses.

The objectives of fusion surgery for spinal deformities include decompressing neural elements and achieving balanced spinal alignment. Particularly, in cases where spinal deformities coexist with osteoporosis, successful surgery requires careful consideration due to the susceptibility to fixation failure and non-union. Various efforts are being made to restore spinal alignment through surgery in osteoporotic patients. The administration of osteoporosis medications before and after surgery is effective for bony union. Additionally, appropriate selection of fusion range, rigid internal fixation, and utilization of bone substitutes play significant roles in successful fusion surgery. Although surgical treatment for spinal deformities accompanied by osteoporosis remains still challenging, we can anticipate successful outcomes with effective strategies and ongoing advancements in the future.

Objective: Mental health promotion programs using virtual reality (VR) technology have been developed in various forms. This study aimed to investigate the subjective experience of a VR-assisted mental health promotion program for the community population, which was provided in the form of VR experience on a bus to increase accessibility. Methods: Ninety-six people participated in this study. The relationship between the subjective experience and mental health states such as depression, anxiety, perceived stress, and quality of life was explored. The subjective experience on depression and stress before and after VR program treatment was compared using the Wilcoxon signed-rank test. The satisfaction with the VR-assisted mental health promotion program was examined after using the VR program. Results: The VR-assisted mental health promotion program on a bus significantly improved subjective symptoms such as depression (p=0.036) and perceived stress (p=0.010) among all the participants. Among the high-risk group, this VR program significantly relieved subjective depressive feeling score (p=0.033), and subjective stressful feeling score (p=0.035). In contrast, there were no significant changes in subjective depressive feelings (p=0.182) and subjective stressful feelings (p=0.058) among the healthy group. Seventy-two percent of the participants reported a high level of satisfaction, scoring 80 points or more. Conclusion The findings of this study suggest that the VR-assisted mental health promotion program may effectively improve the subjective depressive and stressful feelings. The use of VR programs on buses to increase of accessibility for the community could be a useful approach for promoting mental health among the population.

Background/Aims: This study compared the effectiveness and safety of glecaprevir/pibrentasvir (GLE/PIB) and sofosbuvir/ledipasvir (SOF/LDV) in real-life clinical practice. Methods: The data from genotype 1 or 2 chronic hepatitis C patients treated with GLE/PIB or sofosbuvir + ribavirin or SOF/LDV in South Korea were collected retrospectively. The analysis included the treatment completion rate, sustained virologic response at 12 weeks (SVR12) test rate, treatment effectiveness, and adverse events. Results: Seven hundred and eighty-two patients with genotype 1 or 2 chronic hepatitis C who were treated with GLE/PIB (n=575) or SOF/LDV (n=207) were included in this retrospective study. The baseline demographic and clinical characteristics revealed significant statistical differences in age, genotype, ascites, liver cirrhosis, and hepatocellular carcinoma between the GLE/PIB and SOF/LDV groups. Twenty-two patients did not complete the treatment protocol. The treatment completion rate was high for both regimens without statistical significance (97.7% vs. 95.7%, p=0.08). The overall SVR12 of intention-to-treat analysis was 81.2% vs. 80.7% without statistical significance (p=0.87). The overall SVR12 of per protocol analysis was 98.7% vs. 100% without statistical significance (p=0.14). Six patients treated with GLE/PIB experienced treatment failure. They were all male, genotype 2, and showed a negative hepatitis C virus RNA level at the end of treatment. Two patients treated with GLE/PIB stopped medication because of fever and abdominal discomfort. Conclusions Both regimens had similar treatment completion rates, effectiveness, and safety profiles. Therefore, the SOF/LDV regimen can also be considered a viable DAA for the treatment of patients with genotype 1 or 2 chronic hepatitis C.