Heart failure (HF) is a major cause of mortality and morbidity in South Korea, imposing substantial physical, emotional, and financial burdens on patients and society. Despite the high burden of symptom and complex care needs of HF patients, palliative care and hospice services remain underutilized in South Korea due to cultural, institutional, and knowledge-related barriers. This position statement from the Korean Society of Heart Failure emphasizes the need for integrating palliative and hospice care into HF management to improve quality of life and support holistic care for patients and their families. By clarifying the role of palliative care in HF and proposing practical referral criteria, this position statement aims to bridge the gap between HF and palliative care services in South Korea, ultimately improving patient-centered outcomes and aligning treatment with the goals and values of HF patients.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Objective: This study aimed to empirically evaluate the effectiveness of chronic disease management services utilizing ICT for patients with chronic illnesses. Methods: From May to December, 2023, 452 people who were diagnosed with hypertension and diabetes at 9 participating public health centers were provided with customized health care services for 24 weeks, and 15 performance indicators were analyzed to evaluate their effectiveness. Results: Health behavior indicators and health risk factors decreased before and after participation in the project, blood pressure control rate, hypertension and diabetes management rate, medication compliance, weight, BMI, BP, WC, FBG, and HDL-cholesterol improved(p<0.001).Service factors that influence the improvement of health behaviors included the number of activity monitor transmissions(p=0.049), confirmed concentrated consultations on physical activity(p=0.003) and nutrition(p=0.005), and the adherence to medication missions for hypertension(p=0.020).As for service factors influencing chronic disease management, the improvement in blood pressure regulation rate was due to the number of times the blood pressure monitor was linked(p=0.004), and the number of confirmed intensive consultations on physical activity(p=0.026), and nutrition(p=0.049); the improvement in hypertension control rate was due to the number of times the activity monitor and blood pressure monitor were linked(p<0.001), and the number of hypertension medication missions carried out (p=0.004); and the improvement in diabetes control rate was due to the number of times the blood pressure monitor(p=0.022) and blood sugar system were linked(p=0.017). Conclusion Although this study has limitations as a comparative study before and after the service, it has proved that chronic disease management using ICT has a positive effect on improvement of health behavior indicator, reduction of health risk factors, hypertension, diabetes management index, weight, BMI, TG, BP, FBG improvement.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Objective: This study aimed to empirically evaluate the effectiveness of chronic disease management services utilizing ICT for patients with chronic illnesses. Methods: From May to December, 2023, 452 people who were diagnosed with hypertension and diabetes at 9 participating public health centers were provided with customized health care services for 24 weeks, and 15 performance indicators were analyzed to evaluate their effectiveness. Results: Health behavior indicators and health risk factors decreased before and after participation in the project, blood pressure control rate, hypertension and diabetes management rate, medication compliance, weight, BMI, BP, WC, FBG, and HDL-cholesterol improved(p<0.001).Service factors that influence the improvement of health behaviors included the number of activity monitor transmissions(p=0.049), confirmed concentrated consultations on physical activity(p=0.003) and nutrition(p=0.005), and the adherence to medication missions for hypertension(p=0.020).As for service factors influencing chronic disease management, the improvement in blood pressure regulation rate was due to the number of times the blood pressure monitor was linked(p=0.004), and the number of confirmed intensive consultations on physical activity(p=0.026), and nutrition(p=0.049); the improvement in hypertension control rate was due to the number of times the activity monitor and blood pressure monitor were linked(p<0.001), and the number of hypertension medication missions carried out (p=0.004); and the improvement in diabetes control rate was due to the number of times the blood pressure monitor(p=0.022) and blood sugar system were linked(p=0.017). Conclusion Although this study has limitations as a comparative study before and after the service, it has proved that chronic disease management using ICT has a positive effect on improvement of health behavior indicator, reduction of health risk factors, hypertension, diabetes management index, weight, BMI, TG, BP, FBG improvement.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Objective: This study aimed to empirically evaluate the effectiveness of chronic disease management services utilizing ICT for patients with chronic illnesses. Methods: From May to December, 2023, 452 people who were diagnosed with hypertension and diabetes at 9 participating public health centers were provided with customized health care services for 24 weeks, and 15 performance indicators were analyzed to evaluate their effectiveness. Results: Health behavior indicators and health risk factors decreased before and after participation in the project, blood pressure control rate, hypertension and diabetes management rate, medication compliance, weight, BMI, BP, WC, FBG, and HDL-cholesterol improved(p<0.001).Service factors that influence the improvement of health behaviors included the number of activity monitor transmissions(p=0.049), confirmed concentrated consultations on physical activity(p=0.003) and nutrition(p=0.005), and the adherence to medication missions for hypertension(p=0.020).As for service factors influencing chronic disease management, the improvement in blood pressure regulation rate was due to the number of times the blood pressure monitor was linked(p=0.004), and the number of confirmed intensive consultations on physical activity(p=0.026), and nutrition(p=0.049); the improvement in hypertension control rate was due to the number of times the activity monitor and blood pressure monitor were linked(p<0.001), and the number of hypertension medication missions carried out (p=0.004); and the improvement in diabetes control rate was due to the number of times the blood pressure monitor(p=0.022) and blood sugar system were linked(p=0.017). Conclusion Although this study has limitations as a comparative study before and after the service, it has proved that chronic disease management using ICT has a positive effect on improvement of health behavior indicator, reduction of health risk factors, hypertension, diabetes management index, weight, BMI, TG, BP, FBG improvement.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background: The association between renal dysfunction and cardiovascular outcomes has yet to be determined in patients with hypertrophic cardiomyopathy (HCM). We aimed to investigate whether mildly reduced renal function is associated with the prognosis in patients with HCM. Methods: Patients with HCM were enrolled at two tertiary HCM centers. Patients who were on dialysis, or had a previous history of heart failure (HF) or stroke were excluded. Patients were categorized into 3 groups by estimated glomerular filtration rate (eGFR): stage I (eGFR ≥ 90 mL/min/1.73 m2 , n = 538), stage II (eGFR 60–89 mL/min/1.73 m2 , n = 953), and stage III–V (eGFR < 60 mL/min/1.73 m2 , n = 265). Major adverse cardiovascular events (MACEs) were defined as a composite of cardiovascular death, hospitalization for HF (HHF), or stroke during median 4.0-year follow-up. Multivariable Cox regression model was used to adjust for covariates. Results: Among 1,756 HCM patients (mean 61.0 ± 13.4 years; 68.1% men), patients with stage III–V renal function had a significantly higher risk of MACEs (adjusted hazard ratio [aHR], 2.71; 95% confidence interval [CI], 1.39–5.27; P = 0.003), which was largely driven by increased incidence of cardiovascular death and HHF compared to those with stage I renal function. Even in patients with stage II renal function, the risk of MACE (vs. stage I: aHR, 2.21’ 95% CI, 1.23–3.96; P = 0.008) and HHF (vs. stage I: aHR, 2.62; 95% CI, 1.23–5.58; P = 0.012) was significantly increased. Conclusion This real-world observation showed that even mildly reduced renal function (i.e., eGFR 60–89 mL/min/1.73 m2 ) in patients with HCM was associated with an increased risk of MACEs, especially for HHF.