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 evaluate the performance of a clinical ladder system in a tertiary hospital by examining how nurses' clinical competence and perceptions of the system affect organizational commitment. Methods: The study involved 394 nurses working at a tertiary hospital. Data were collected from May 3 to July 10, 2023, using a self-reported questionnaire. Statistical analyses, including descriptive statistics, independent t-tests, one-way ANOVA, Kruskal-Wallis test, Scheffé post-hoc test, Pearson correlation, and hierarchical regression analysis, were performed using SPSS 27.0. Results: Nurses who applied for promotion to the CN III level and current CN III nurses reported higher clinical competence, perceptions of the clinical ladder system, and organizational commitment than those who did not and those at lower levels (p<.001). A positive correlation existed among all independent variables.Controlling for general characteristics, the effects of clinical competence and perceptions of the clinical ladder system explained 49% of organizational commitment variance (Adjusted R 2 =.49, F=33.43, p<.001). Conclusion Greater clinical competence and positive perceptions of the clinical ladder system are likely to enhance organizational commitment, emphasizing its effectiveness in fostering better organizational 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.

Purpose: This study aimed to evaluate the performance of a clinical ladder system in a tertiary hospital by examining how nurses' clinical competence and perceptions of the system affect organizational commitment. Methods: The study involved 394 nurses working at a tertiary hospital. Data were collected from May 3 to July 10, 2023, using a self-reported questionnaire. Statistical analyses, including descriptive statistics, independent t-tests, one-way ANOVA, Kruskal-Wallis test, Scheffé post-hoc test, Pearson correlation, and hierarchical regression analysis, were performed using SPSS 27.0. Results: Nurses who applied for promotion to the CN III level and current CN III nurses reported higher clinical competence, perceptions of the clinical ladder system, and organizational commitment than those who did not and those at lower levels (p<.001). A positive correlation existed among all independent variables.Controlling for general characteristics, the effects of clinical competence and perceptions of the clinical ladder system explained 49% of organizational commitment variance (Adjusted R 2 =.49, F=33.43, p<.001). Conclusion Greater clinical competence and positive perceptions of the clinical ladder system are likely to enhance organizational commitment, emphasizing its effectiveness in fostering better organizational 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.

Purpose: This study aimed to evaluate the performance of a clinical ladder system in a tertiary hospital by examining how nurses' clinical competence and perceptions of the system affect organizational commitment. Methods: The study involved 394 nurses working at a tertiary hospital. Data were collected from May 3 to July 10, 2023, using a self-reported questionnaire. Statistical analyses, including descriptive statistics, independent t-tests, one-way ANOVA, Kruskal-Wallis test, Scheffé post-hoc test, Pearson correlation, and hierarchical regression analysis, were performed using SPSS 27.0. Results: Nurses who applied for promotion to the CN III level and current CN III nurses reported higher clinical competence, perceptions of the clinical ladder system, and organizational commitment than those who did not and those at lower levels (p<.001). A positive correlation existed among all independent variables.Controlling for general characteristics, the effects of clinical competence and perceptions of the clinical ladder system explained 49% of organizational commitment variance (Adjusted R 2 =.49, F=33.43, p<.001). Conclusion Greater clinical competence and positive perceptions of the clinical ladder system are likely to enhance organizational commitment, emphasizing its effectiveness in fostering better organizational 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.

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)