Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Background: and Purpose The Treat Stroke to Target (TST) was a randomized clinical trial involving French and Korean patients demonstrating that a lower low-density lipoprotein cholesterol (LDL-C, <70 mg/dL) target group (LT) experienced fewer cerebro-cardiovascular events than a higher target (90–110 mg/dL) group (HT). However, whether these results can be applied to Asian patients with different ischemic stroke subtypes remains unclear. Methods: Patients from 14 South Korean centers were analyzed separately. Patients with ischemic stroke or transient ischemic attack with evidence of atherosclerosis were randomized into LT and HT groups. The primary endpoint was a composite of ischemic stroke, myocardial infarction, coronary or cerebral revascularization, and cardiovascular death. Results: Among 712 enrolled patients, the mean LDL-C level was 71.0 mg/dL in 357 LT patients and 86.1 mg/dL in 355 HT patients. The primary endpoint occurred in 24 (6.7%) of LT and in 31 (8.7%) of HT group patients (adjusted hazard ratio [HR]=0.78; 95% confidence interval [CI]=0.45–1.33, P=0.353). Cardiovascular events alone occurred significantly less frequently in the LT than in the HT group (HR 0.26, 95% CI 0.09–0.80, P=0.019), whereas there were no significant differences in ischemic stroke events (HR 1.12, 95% CI 0.60–2.10, P=0.712). The benefit of LT was less apparent in patients with small vessel disease and intracranial atherosclerosis than in those with extracranial atherosclerosis. Conclusion In contrast to the French TST, the outcomes in Korean patients were neutral. Although LT was more effective in preventing cardiovascular diseases, it was not so in stroke prevention, probably attributed to the differences in stroke subtypes. Further studies are needed to elucidate the efficacy of statins and appropriate LDL-C targets in Asian patients with stroke.

Objective: To improve our understanding of EPEC, this study focused on analyzing and comparing the genomic characteristics of EPEC isolates from humans and companion animals in Korea. Methods: The whole genome of 26 EPEC isolates from patients with diarrhea and 20 EPEC isolates from companion animals in Korea were sequenced using the Illumina HiSeq X (Illumina, USA) and Oxford Nanopore MinION (Oxford Nanopore Technologies, UK) platforms. Results: Most isolates were atypical EPEC, and did not harbor the bfpA gene. The most prevalent virulence genes were found to be ompT (humans: 61.5%; companion animals:60.0%) followed by lpfA (humans: 46.2%; companion animals: 60.0%). Although pangenome analyses showed no apparent correlation among the origin of the strains, virulence profiles, and antimicrobial resistance profiles, isolates included in clade A obtained from both humans and companion animals exhibited high similarity. Additionally, all the isolates included in clade A encoded the ompT gene and did not encode the hlyE gene. The two isolates from companion animals harbored an incomplete bundle-forming pilus region encoding bfpA and bfpB. Moreover, the type IV secretion system-associated genes tra and trb were found in the bfpA-encoding isolates from humans. Conclusions and Relevance: Whole-genome sequencing enabled a more accurate analysis of the phylogenetic structure of EPEC and provided better insights into the understanding of EPEC epidemiology and pathogenicity.

Background: Complete revascularization has demonstrated better outcomes in patients with acute myocardial infarction (AMI) and multivessel disease. However, in the case of left main (LM) culprit lesion AMI with multivessel disease, there is limited evidence to suggest that complete revascularization is better. Methods: We reviewed 16,831 patients in the Korea Acute Myocardial Infarction Registry who were treated from July 2016 to June 2020, and 399 patients were enrolled with LM culprit lesion AMI treated with percutaneous coronary intervention. We categorized the patients as those treated with complete revascularization (n=295) or incomplete revascularization (n=104). The study endpoint was major adverse cardiac and cerebrovascular events (MACCE), a composite of all-cause death, myocardial infarction, ischemia-driven revascularization, stent thrombosis, and stroke. We performed propensity score matching (PSM) and analyzed the incidence of MACCE at 1 year. Results: After PSM, the two groups were well balanced. There was no significant difference between the two groups in MACCE at 1 year (12.1% vs. 15.2%; hazard ratio, 1.28; 95% confidence interval, 0.60–2.74; p=0.524) after PSM. The components of MACCE and major bleeding were also not significantly different. Conclusion There was no significant difference in clinical outcomes between the groups treated with complete or incomplete revascularization for LM culprit lesion AMI with multivessel disease.

Background: This study aimed to identify key priorities for the development of guidelines for information and communication technology (ICT)-based patient education tailored to the needs of patients with rheumatic diseases (RDs) in the Republic of Korea, based on expert consensus. Methods: A two-round modified Delphi study was conducted with 20 rheumatology, patient education, and digital health literacy experts. A total of 35 items covering 7 domains and 18 subdomains were evaluated. Each item was evaluated for its level of importance, and the responses were rated on a 4-point Likert scale. Consensus levels were defined as “high” (interquartile range [IQR] ≤ 1, agreement ≥ 80%, content validity ratio [CVR] ≥ 0.7), "Moderate" (IQR ≥ 1, agreement 50–79%, CVR 0.5–0.7), and "Low" (IQR > 1, agreement < 50%, CVR < 0.5). Results: Strong consensus was reached for key priorities for developing guidelines in areas such as health literacy, digital health literacy, medical terminology, user interface, and user experience design for mobile apps. Chatbot use and video (e.g., YouTube) also achieved high consensus, whereas AI-powered platforms such as ChatGPT showed moderate-to-high agreement. Telemedicine was excluded because of insufficient consensus. Conclusion The key priorities identified in this study provide a foundation for the development of ICT-based patient education guidelines for RDs in the Republic of Korea.Future efforts should focus on integrating digital tools into clinical practice to enhance patient engagement and improve clinical outcomes.

Background: Shortage of organ donors in the Republic of Korea has become a major problem. To address this, it has been questioned whether heart transplant (HTx) allocation should be modified to reduce priority of older patients. We aimed to evaluate post-HTx outcomes according to recipient age and specific pre-HTx conditions using a nationwide prospective cohort. Methods: We analyzed clinical characteristics of 628 patients from the Korean Organ Transplant Registry who received HTx from January 2015 to December 2020. Enrolled recipients were divided into three groups according to age. We also included comorbidities including ambulatory status. Non-ambulatory status was defined as pre-HTx support with either extracorporeal membrane oxygenation, continuous renal replacement therapy, or mechanical ventilation. Results: Of the 628 patients, 195 were < 50 years, 322 were 50–64 years and 111 were ≥ 65years at transplant. Four hundred nine (65.1%) were ambulatory and 219 (34.9%) were nonambulatory. Older recipients tended to have more comorbidities, ischemic cardiomyopathy, and received older donors. Post-HTx survival was significantly lower in older recipients (P = 0.025) and recipients with non-ambulatory status (P < 0.001). However, in contrast to non-ambulatory recipients who showed significant survival differences according to the recipient’s age (P = 0.004), ambulatory recipients showed comparable outcomes (P = 0.465). Conclusion Our results do not support use of age alone as an allocation criterion. Transplant candidate age in combination with some comorbidities such as non-ambulatory status may identify patients at a sufficiently elevated risk at which suitability of HTx should be reconsidered.

Background: This study aimed to identify key priorities for the development of guidelines for information and communication technology (ICT)-based patient education tailored to the needs of patients with rheumatic diseases (RDs) in the Republic of Korea, based on expert consensus. Methods: A two-round modified Delphi study was conducted with 20 rheumatology, patient education, and digital health literacy experts. A total of 35 items covering 7 domains and 18 subdomains were evaluated. Each item was evaluated for its level of importance, and the responses were rated on a 4-point Likert scale. Consensus levels were defined as “high” (interquartile range [IQR] ≤ 1, agreement ≥ 80%, content validity ratio [CVR] ≥ 0.7), "Moderate" (IQR ≥ 1, agreement 50–79%, CVR 0.5–0.7), and "Low" (IQR > 1, agreement < 50%, CVR < 0.5). Results: Strong consensus was reached for key priorities for developing guidelines in areas such as health literacy, digital health literacy, medical terminology, user interface, and user experience design for mobile apps. Chatbot use and video (e.g., YouTube) also achieved high consensus, whereas AI-powered platforms such as ChatGPT showed moderate-to-high agreement. Telemedicine was excluded because of insufficient consensus. Conclusion The key priorities identified in this study provide a foundation for the development of ICT-based patient education guidelines for RDs in the Republic of Korea.Future efforts should focus on integrating digital tools into clinical practice to enhance patient engagement and improve clinical outcomes.

Background: Shortage of organ donors in the Republic of Korea has become a major problem. To address this, it has been questioned whether heart transplant (HTx) allocation should be modified to reduce priority of older patients. We aimed to evaluate post-HTx outcomes according to recipient age and specific pre-HTx conditions using a nationwide prospective cohort. Methods: We analyzed clinical characteristics of 628 patients from the Korean Organ Transplant Registry who received HTx from January 2015 to December 2020. Enrolled recipients were divided into three groups according to age. We also included comorbidities including ambulatory status. Non-ambulatory status was defined as pre-HTx support with either extracorporeal membrane oxygenation, continuous renal replacement therapy, or mechanical ventilation. Results: Of the 628 patients, 195 were < 50 years, 322 were 50–64 years and 111 were ≥ 65years at transplant. Four hundred nine (65.1%) were ambulatory and 219 (34.9%) were nonambulatory. Older recipients tended to have more comorbidities, ischemic cardiomyopathy, and received older donors. Post-HTx survival was significantly lower in older recipients (P = 0.025) and recipients with non-ambulatory status (P < 0.001). However, in contrast to non-ambulatory recipients who showed significant survival differences according to the recipient’s age (P = 0.004), ambulatory recipients showed comparable outcomes (P = 0.465). Conclusion Our results do not support use of age alone as an allocation criterion. Transplant candidate age in combination with some comorbidities such as non-ambulatory status may identify patients at a sufficiently elevated risk at which suitability of HTx should be reconsidered.

Objective: Patients with severe symptomatic aortic stenosis are assessed for coronary artery disease (CAD) prior to transcatheter aortic valve implantation (TAVI) with treatment implications. Invasive coronary angiography (ICA) is the recommended modality but is associated with peri-procedural complications. Integrating machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) into existing TAVI-planning CT protocol may aid exclusion of significant CAD and thus avoiding ICA in selected patients. Materials and Methods: A single-center, retrospective study was conducted, 41 TAVI candidates with both TAVI-planning CT and ICA performed were analyzed. CT datasets were evaluated by a ML-based CT-FFR software. Beta-blocker and nitroglycerin were not administered in these patients. The primary outcome was to identify significant CAD. The diagnostic performance of CT-FFR was compared against ICA. Results: On per-patient level, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy were 89%, 94%, 80%, 97% and 93%, respectively. On per-vessel level, the sensitivity, specificity, PPV, NPV and diagnostic accuracy were 75%, 94%, 67%, 96% and 92%, respectively. The area under the receiver operative characteristics curve per individual coronary vessels yielded overall 0.90 (95% confidence interval 85%–95%). ICA may be avoided in up to 80% of patients if CT-FFR results were negative. Conclusion ML-based CT-FFR can provide accurate screening capabilities for significant CAD thus avoiding ICA. Its integration to existing TAVI-planning CT is feasible with the potential of improving the safety and efficiency of pre-TAVI CAD assessment.

Purpose: Adjuvant chemotherapy (AC) is recommended for muscle-invasive or lymph node-positive upper urinary tract urothelial carcinoma (UTUC) after radical nephroureterectomy (RNU). However, disease recurrences are frequently observed in pT1 disease, and AC may increase the risk of overtreatment in pT2 UTUC patients. This study aimed to validate a risk-adapted scoring model for selecting UTUC patients with ≤pT2 disease who would benefit from AC. Materials and Methods: We retrospectively analyzed 443 ≤pT2 UTUC patients who underwent RNU. A risk-adapted scoring model was applied, categorizing patients into low- or high-risk groups. Recurrence-free survival (RFS) and cancer-specific survival (CSS) were analyzed according to risk group. Results: Overall, 355 patients (80.1%) and 88 patients (19.9%) were categorized into the low- and high-risk groups, respectively, with the latter having higher pathological stages, concurrent carcinoma in situ, and synchronous bladder tumors. Disease recurrence occurred in 45 patients (10.2%), among whom 19 (5.4%) and 26 (29.5%) belonged to the low- and high-risk groups, respectively (p<0.001). High-risk patients had significantly shorter RFS (64.3% vs. 93.6% at 60 months; hazard ratio [HR] 13.66; p<0.001) and worse CSS (80.7% vs. 91.5% at 60 months; HR 4.25; p=0.002). Multivariate analysis confirmed that pT2 stage and the high-risk group were independent predictors of recurrence and cancer-specific death (p<0.001). Decision curve analysis for RFS showed larger net benefits with our model than with the T stage model. Conclusions The risk-adapted scoring model effectively predicts recurrence and identifies optimal candidates for AC post RNU in non-metastatic UTUC.