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.

Scrub typhus is known as one of the most common seasonal infections in endemic rural areas, but life-threatening cardiac complications in cases of scrub typhus are very infrequent. In addition, scrub typhus infection has been rarely reported among workers assembling pallets using manufactured wood in metropolitan areas. Herein, we present two cases involving myocarditis and cardiac tamponade as complications of scrub typhus. One patient died and the other patient survived. These cases indicate that scrub typhus infection could be an environmental hazard in metropolitan areas, especially in locations with poor hygiene, and highlight the need for timely diagnosis and proper management of severe scrub typhus infections. Therefore, we present these two informative fatal cases of scrub typhus infection presenting with myocarditis and cardiac tamponade as an environmental hazard in metropolitan areas.

Purpose: To differentiate a non-cancer-related temporary increase in prostate-specific antigen (PSA) triggering unnecessary biopsy, we intentionally delayed biopsy with a serial follow-up, then investigated the efficacy of this strategy in identifying a significant prostate cancer (PCa). Materials and Methods: Retrospective data of patients who initially presented with a suspicious level of serum PSA (3–20 ng/mL), managed using the delayed strategy, and then eventually underwent biopsy were obtained from 4 tertiary centers between 2018–2020. Results: The collected 271 subjects had a median (interquartile range) PSA, age, and prostate volume of 5.03 ng/mL (4.46–7.79 ng/mL), 67 years (61–73 years), and 38 g (28–50 g), respectively. During the delay period of 8 weeks (4–19 weeks), most were managed with alpha-blockers (85.6%, n=232). Ninety-four (34.7%) experienced a PSA decrease of 20.53% (8.82–38.16). Eventual biopsy revealed 115 PCa cases (42.5%) including 82 significant ones and 46 high-risk diseases. Men with a PSA decrease had a lower probability of PCa (31.9% vs. 48%, p=0.014), a significant disease (21.3% vs. 35.0%, p=0.026), and high-risk PCa (7.4% vs. 22.0%, p=0.002) than the PSA-elevated counterparts. However, the degree of PSA decrease was not associated with the presence or the severity of PCa. In patients with PSA normalization (≤3 ng/mL), though 4 patients of them (66%) had PCa including a single significant disease, none had high-risk disease. Conclusions About one-third of individuals initially indicated for transrectal biopsy experienced a decrease in PSA, and their chance for significant PCa was diminished. This retrospective study suggests PSA normalization could be an acceptable notion, though requires further investigation.

Background: The main cause of death in pulmonary embolism (PE) is right-heart failure due to acute pressure overload. In this sense, extracorporeal membrane oxygenation (ECMO) might be useful in maintaining hemodynamic stability and improving organ perfusion. Some previous studies have reported ECMO as a bridge to reperfusion therapy of PE. However, little is known about the patients that benefit from ECMO. Methods: Patients who underwent ECMO due to pulmonary thromboembolism at a single university-affiliated hospital between January 2010 and December 2018 were retrospectively reviewed. Results: During the study period, nine patients received ECMO in high-risk PE. The median age of the patients was 60 years (range, 22–76 years), and six (66.7%) were male. All nine patients had cardiac arrests, of which three occurred outside the hospital. All the patients received mechanical support with veno-arterial ECMO, and the median ECMO duration was 1.1 days (range, 0.2–14.0 days). ECMO with anticoagulation alone was performed in six (66.7%), and ECMO with reperfusion therapy was done in three (33.3%). The 30-day mortality rate was 77.8%. The median time taken from the first cardiac arrest to initiation of ECMO was 31 minutes (range, 30–32 minutes) in survivors (n=2) and 65 minutes (range, 33–482 minutes) in non-survivors (n=7). Conclusion High-risk PE with cardiac arrest has a high mortality rate despite aggressive management with ECMO and reperfusion therapy. Early decision to start ECMO and its rapid initiation might help save those with cardiac arrest in high-risk PE.