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)

Background: Clinical ethics support is a form of preventive ethics aimed at mediating ethicsrelated conflicts and managing ethical issues arising in the healthcare setting. However, limited evidence exists regarding the specific ethical issues in clinical practice. This study aimed to explore the diverse ethical issues of cases referred to clinical ethics support after the new legislation on hospice palliative care and end-of-life decision-making was implemented in Korea in 2018. Methods: A retrospective study of cases referred to clinical ethics support at a university hospital in Korea from February 2018 to February 2021 was conducted. The ethical issues at the time of referral were analyzed via qualitative content analysis of the ethics consultationrelated documents. Results: A total of 60 cases of 57 patients were included in the study, of whom 52.6% were men and 56.1% were older than 60 years of age. The majority of cases (80%) comprised patients from the intensive care unit. One-third of the patients were judged as being at the end-of-life stage. The most frequent ethical categories were identified as goals of care/ treatment (78.3%), decision-making (75%), relationship (41.7%), and end-of-life issues (31.7%). More specifically, best interests (71.7%), benefits and burdens/harms (61.7%), refusal (53.3%), and surrogate decision-making (33.3%), followed by withholding or withdrawal (28.3%) were the most frequent ethical issues reported, which became diversified by year. In addition, the ethical issues appeared to differ by age group and judgment of the end-of-life stage. Conclusion The findings of this study expand the current understanding of the diverse ethical issues including decision-making and goals of care/treatment that have been referred to clinical ethics support since the enforcement of the new legislation in Korea. This study suggests a need for further research on the longitudinal exploration of ethical issues and implementation of clinical ethics support in multiple healthcare centers.

Background: Recent studies have successfully implemented next-generation sequencing (NGS) in HLA typing. We performed HLA NGS in a Korean population to estimate HLA-A, -B, -C, and -DRB1 allele and haplotype frequencies up to an 8-digit resolution, which might be useful for an extended application of HLA results. Methods: A total of 128 samples collected from healthy unrelated Korean adults, previously subjected to Sanger sequencing for 6-digit HLA analysis, were used. NGS was performed for HLA-A, -B, -C, and -DRB1 using the AllType NGS kit (One Lambda, West Hills, CA, USA), Ion Torrent S5 platform (Thermo Fisher Scientific, Waltham, MA, USA), and Type Steam Visual NGS analysis software (One Lambda). Results: Eight HLA alleles showed frequencies of ≥ 10% in the Korean population, namely, A*24:02:01:01 (19.5%), A*33:03:01 (15.6%), A*02:01:01:01 (14.5%), A*11:01:01:01 (13.3%), B*15:01:01:01 (10.2%), C*01:02:01 (19.9%), C*03:04:01:02 (11.3%), and DRB1*09:01:02 (10.2%). Nine previous 6-digit HLA alleles were further identified as two or more 8-digit HLA alleles. Of these, eight alleles (A*24:02:01, B*35:01:01, B*40:01:02, B*55:02:01, B*58:01:01, C*03:02:02, C*07:02:01, and DRB1*07:01:01) were identified as two 8-digit HLA alleles, and one allele (B*51:01:01) was identified as three 8-digit HLA alleles. The most frequent four-loci haplotype was HLA-A*33:03:01-B*44:03:01:01-C*14:03-DRB1*13:02:01. Conclusions We identified 8-digit HLA-A, -B, -C, and -DRB1 allele and haplotype frequencies in a healthy Korean population using NGS. These new data can be used as a representative Korean data for further disease-related HLA type analysis.

Purpose: This study aimed to investigate difficulties doctors experience during life-sustaining treatment (LST) discussion with seriously ill patients and their families after enactment of the LST Decisions Act in February 2018. Materials and Methods: A cross-sectional survey was conducted in a tertiary hospital in the Republic of Korea in August 2019. Six hundred eighty-six doctors who care for seriously ill patients were given a structured questionnaire, and difficulties during the discussion were examined. Results: One hundred thirty-two doctors completed the questionnaire. Eighty-five percent answered they treat cancer patients. Most (86.4%) experienced considerable difficulties during LST discussions (mean score, 7.4±1.6/10). The two most common difficulties were communication with patients and family and determining when to discuss LST. Two-thirds of doctors found direct discussions with the patient difficult and said they would initiate LST discussions only with family. LST discussions were actually initiated later than considered appropriate. When medically assessing whether the patient is imminently dying, 56% of doctors experienced disagreements with other doctors, which could affect their decisions. Conclusion This study found that most doctors experienced serious difficulties regarding communication with patients and family and medical assessment of dying process during LST discussions. To alleviate these difficulties, further institutional support is needed to improve the LST discussion between doctors, patients, and family.

Purpose: This study aimed to investigate difficulties doctors experience during life-sustaining treatment (LST) discussion with seriously ill patients and their families after enactment of the LST Decisions Act in February 2018. Materials and Methods: A cross-sectional survey was conducted in a tertiary hospital in the Republic of Korea in August 2019. Six hundred eighty-six doctors who care for seriously ill patients were given a structured questionnaire, and difficulties during the discussion were examined. Results: One hundred thirty-two doctors completed the questionnaire. Eighty-five percent answered they treat cancer patients. Most (86.4%) experienced considerable difficulties during LST discussions (mean score, 7.4±1.6/10). The two most common difficulties were communication with patients and family and determining when to discuss LST. Two-thirds of doctors found direct discussions with the patient difficult and said they would initiate LST discussions only with family. LST discussions were actually initiated later than considered appropriate. When medically assessing whether the patient is imminently dying, 56% of doctors experienced disagreements with other doctors, which could affect their decisions. Conclusion This study found that most doctors experienced serious difficulties regarding communication with patients and family and medical assessment of dying process during LST discussions. To alleviate these difficulties, further institutional support is needed to improve the LST discussion between doctors, patients, and family.

In the era of coronavirus disease 2019 (COVID-19), social distancing and strict visitation policies at hospitals have made it difficult for medical staff to provide high-quality endof-life (EOL) care to dying patients and their families. There are various issues related to EOL care, including psychological problems of patients and their families, difficulties in EOL decision-making, the complicated grief of the bereaved family, moral distress, and exhaustion of medical staff. In relation to these issues, we aimed to discuss practical considerations in providing high-quality EOL care in the COVID-19 pandemic. First, medical staff should discuss advance care planning as early as possible and use the parallel planning strategy. Second, medical staff should play a role in facilitating patient-family communication. Third, medical staff should actively and proactively evaluate and alleviate dying patients’ symptoms using non-verbal communication. Lastly, medical staff should provide care for family members of the dying patient, who may be particularly vulnerable to postbereavement problems in the COVID-19 era. Establishing a system of screening highrisk individuals for complicated grief and connecting them to bereavement support services might be considered. Despite the challenging and limited environment, providing EOL care is essential for patients to die with dignity in peace and for the remaining family to return to life after the loved one’s death. Efforts considering the practical issues faced by all medical staff and healthcare institutions caring for dying patients should be made.