Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Allergen immunotherapy (AIT) has been used for over a century and has been demonstrated to be effective in treating patients with various allergic diseases. AIT allergens can be administered through various routes, including subcutaneous, sublingual, intralymphatic, oral, or epicutaneous routes. Sublingual immunotherapy (SLIT) has recently gained clinical interest, and it is considered an alternative treatment for allergic rhinitis (AR) and asthma. This review provides an overview of the current evidence-based studies that address the use of SLIT for treating AR, including (1) mechanisms of action, (2) appropriate patient selection for SLIT, (3) the current available SLIT products in Korea, and (4) updated information on its efficacy and safety. Finally, this guideline aims to provide the clinician with practical considerations for SLIT.

Allergen immunotherapy (AIT) is a causative treatment of allergic diseases in which allergen extracts are regularly administered in a gradually escalated doses, leading to immune tolerance and consequent alleviation of allergic diseases. The need for uniform practice guidelines in AIT is continuously growing as the number of potential candidates for AIT increases and new therapeutic approaches are tried. This updated version of the Korean Academy of Asthma Allergy and Clinical Immunology recommendations for AIT, published in 2010, proposes an expert opinion by specialists in allergy, pediatrics, and otorhinolaryngology. This guideline deals with the basic knowledge of AIT, including mechanisms, clinical efficacy, allergen standardization, important allergens in Korea, and special consideration in pediatrics. The article also covers the methodological aspects of AIT, including patient selection, allergen selection, schedule and doses, follow-up care, efficacy measurements, and management of adverse reactions. Although this guideline suggests the optimal dosing schedule, an individualized approach and modifications are recommended considering the situation for each patient and clinic.

Background: The treatment success rate for tuberculosis (TB) has stagnated at 80–81% in South Korea, indicating unsatisfactory outcomes. Enhancing treatment success rate necessitates the development of individualized treatment approaches for each patient. This study aimed to identify the risk factors associated with unfavorable treatment outcomes to facilitate tailored TB care. Methods: We retrospectively analyzed the data of patients with active TB between January 2019 and December 2020 at a single tertiary referral center. We classified unfavorable treatment outcomes according to the 2021 World Health Organization guidelines as follows:“lost to follow-up” (LTFU), “not evaluated” (NE), “death,” and “treatment failure” (TF).Moreover, we analyzed risk factors for each unfavorable outcome using Cox proportional hazard regression analysis. Results: A total of 659 patients (median age 62 years; male 54.3%) were included in the study.The total unfavorable outcomes were 28.1%: 4.6% LTFU, 9.6% NE, 9.1% deaths, and 4.9% TF. Multivariate analysis showed that a culture-confirmed diagnosis of TB was associated with a lower risk of LTFU (adjusted hazard ratio [aHR], 0.25; 95% confidence interval [CI], 0.10–0.63), whereas the occurrence of adverse drug reactions (ADRs) significantly increased the risk of LTFU (aHR, 6.63; 95% CI, 2.63–16.69). Patients living far from the hospital (aHR, 4.47; 95% CI, 2.50–7.97) and those with chronic kidney disease (aHR, 3.21; 95% CI, 1.33–7.75) were at higher risk of being transferred out to other health institutions (NE). Higher mortality was associated with older age (aHR, 1.06; 95% CI, 1.04–1.09) and comorbidities. The ADRs that occurred during TB treatment were a risk factor for TF (aHR, 6.88; 95% CI, 2.24–21.13). Conclusion Unfavorable outcomes of patients with TB were substantial at a tertiary referral center, and the risk factors for each unfavorable outcome varied. To improve treatment outcomes, close monitoring and the provision of tailored care for patients with TB are necessary.

Background: Over the last decade, extracorporeal membrane oxygenation (ECMO) use in critically ill children has increased and is associated with favorable outcomes. Our study aims to evaluate the current status of pediatric ECMO in Korea, with a specific focus on its volume and changes in survival rates based on diagnostic indications. Methods: This multicenter study retrospectively analyzed the indications and outcomes of pediatric ECMO over 10 years in patients at 14 hospitals in Korea from January 2012 to December 2021. Four diagnostic categories (neonatal respiratory, pediatric respiratory, postcardiotomy, and cardiac-medical) and trends were compared between periods 1 (2012–2016) and 2 (2017–2021). Results: Overall, 1065 ECMO runs were performed on 1032 patients, with the annual number of cases remaining unchanged over the 10 years. ECMO was most frequently used for post-cardiotomy (42.4%), cardiac-medical (31.8%), pediatric respiratory (17.5%), and neonatal respiratory (8.2%) cases. A 3.7% increase and 6.1% decrease in pediatric respiratory and post-cardiotomy cases, respectively, were noted between periods 1 and 2.Among the four groups, the cardiac-medical group had the highest survival rate (51.2%), followed by the pediatric respiratory (46.4%), post-cardiotomy (36.5%), and neonatal respiratory (29.4%) groups. A consistent improvement was noted in patient survival over the 10 years, with a significant increase between the two periods from 38.2% to 47.1% (P = 0.004). Improvement in survival was evident in post-cardiotomy cases (30–45%, P = 0.002).Significant associations with mortality were observed in neonates, patients requiring dialysis, and those treated with extracorporeal cardiopulmonary resuscitation (P < 0.001). In pediatric respiratory ECMO, immunocompromised patients also showed a significant correlation with mortality (P < 0.001). Conclusion Pediatric ECMO demonstrated a steady increase in overall survival in Korea;however, further efforts are needed since the outcomes remain suboptimal compared with global outcomes.

Background/Aims: The programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) pathway has not been fully evaluated in inflammatory bowel disease. We evaluated PD-1/PD-L1 levels in patients with ulcerative colitis (UC) and their significance in tonsil-derived mesenchymal stem cells (TMSCs) treatment. Methods: Using acute and chronic murine colitis model, we measured the PD-1 and PD-L1 levels in inflamed colonic tissues pre- and post-treatment with TMSCs. We also measured PD-1 and PD-L1 levels in colonic tissues from UC patients, compared to normal controls. Results: In the analysis using human colonic tissues, a significant increase in the levels of PD-1 and PD-L1 was observed in the colonic mucosa of patients with UC compared with normal controls (p < 0.001 and p = 0.005, respectively). When comparing the maximal disease extent, PD-L1 levels were highest in patients with proctitis (38.5 ± 46.7), followed by left-side colitis (17.5 ± 23.1) and extensive colitis (5.2 ± 8.2) (p < 0.001). In the chronic colitis model, the level of PD-L1 was decreased (p = 0.040) and the level of PD-1 increased more than in normal controls (p = 0.047). After treatment with TMSC, significant improvements were observed in body weight, disease activity index, and colon length recovery. Additionally, the levels of PD-1 and PD-L1 were recovered; PD-L1 significantly increased (p = 0.031), while the level of PD-1 decreased (p = 0.310). Conclusions The altered expression of PD-1 and PD-L1 in colonic mucosa may be a possible mechanism of UC, and T-MSC-derived PD-L1 could help suppress colitis.