Hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) are correlated with high morbidity and mortality rates. Guidelines that consider local epidemiologic data are fundamental for identifying optimal treatment strategies. However, Korea has no HAP/VAP guidelines. This study was conducted by a committee of nine experts from the Korean Academy of Tuberculosis and Respiratory Diseases Respiratory Infection Study Group using the results of Korean HAP/VAP epidemiologic studies. Eleven key questions for HAP/VAP diagnosis and treatment were addressed. The Convergence of Opinion on Suggestions and Evidence (CORE) process was used to derive suggestions, and evidence levels and recommendation grades were in accordance with the Grading of Recommendations Assessment Development and Evaluation (GRADE) methodology. Suggestions were made for the 11 key questions pertinent to diagnosis, biomarkers, antibiotics, and treatment strategies for adult patients with HAP/VAP. Using the CORE process and GRADE methodology, the committee generated a series of recommendations for HAP/VAP diagnosis and treatment in the Korean context.

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)

Hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) are correlated with high morbidity and mortality rates. Guidelines that consider local epidemiologic data are fundamental for identifying optimal treatment strategies. However, Korea has no HAP/VAP guidelines. This study was conducted by a committee of nine experts from the Korean Academy of Tuberculosis and Respiratory Diseases Respiratory Infection Study Group using the results of Korean HAP/VAP epidemiologic studies. Eleven key questions for HAP/VAP diagnosis and treatment were addressed. The Convergence of Opinion on Suggestions and Evidence (CORE) process was used to derive suggestions, and evidence levels and recommendation grades were in accordance with the Grading of Recommendations Assessment Development and Evaluation (GRADE) methodology. Suggestions were made for the 11 key questions pertinent to diagnosis, biomarkers, antibiotics, and treatment strategies for adult patients with HAP/VAP. Using the CORE process and GRADE methodology, the committee generated a series of recommendations for HAP/VAP diagnosis and treatment in the Korean context.

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)

Hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) are correlated with high morbidity and mortality rates. Guidelines that consider local epidemiologic data are fundamental for identifying optimal treatment strategies. However, Korea has no HAP/VAP guidelines. This study was conducted by a committee of nine experts from the Korean Academy of Tuberculosis and Respiratory Diseases Respiratory Infection Study Group using the results of Korean HAP/VAP epidemiologic studies. Eleven key questions for HAP/VAP diagnosis and treatment were addressed. The Convergence of Opinion on Suggestions and Evidence (CORE) process was used to derive suggestions, and evidence levels and recommendation grades were in accordance with the Grading of Recommendations Assessment Development and Evaluation (GRADE) methodology. Suggestions were made for the 11 key questions pertinent to diagnosis, biomarkers, antibiotics, and treatment strategies for adult patients with HAP/VAP. Using the CORE process and GRADE methodology, the committee generated a series of recommendations for HAP/VAP diagnosis and treatment in the Korean context.

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)

Hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) are correlated with high morbidity and mortality rates. Guidelines that consider local epidemiologic data are fundamental for identifying optimal treatment strategies. However, Korea has no HAP/VAP guidelines. This study was conducted by a committee of nine experts from the Korean Academy of Tuberculosis and Respiratory Diseases Respiratory Infection Study Group using the results of Korean HAP/VAP epidemiologic studies. Eleven key questions for HAP/VAP diagnosis and treatment were addressed. The Convergence of Opinion on Suggestions and Evidence (CORE) process was used to derive suggestions, and evidence levels and recommendation grades were in accordance with the Grading of Recommendations Assessment Development and Evaluation (GRADE) methodology. Suggestions were made for the 11 key questions pertinent to diagnosis, biomarkers, antibiotics, and treatment strategies for adult patients with HAP/VAP. Using the CORE process and GRADE methodology, the committee generated a series of recommendations for HAP/VAP diagnosis and treatment in the Korean context.

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)

Hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) are correlated with high morbidity and mortality rates. Guidelines that consider local epidemiologic data are fundamental for identifying optimal treatment strategies. However, Korea has no HAP/VAP guidelines. This study was conducted by a committee of nine experts from the Korean Academy of Tuberculosis and Respiratory Diseases Respiratory Infection Study Group using the results of Korean HAP/VAP epidemiologic studies. Eleven key questions for HAP/VAP diagnosis and treatment were addressed. The Convergence of Opinion on Suggestions and Evidence (CORE) process was used to derive suggestions, and evidence levels and recommendation grades were in accordance with the Grading of Recommendations Assessment Development and Evaluation (GRADE) methodology. Suggestions were made for the 11 key questions pertinent to diagnosis, biomarkers, antibiotics, and treatment strategies for adult patients with HAP/VAP. Using the CORE process and GRADE methodology, the committee generated a series of recommendations for HAP/VAP diagnosis and treatment in the Korean context.

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)