Objectives: People-centered care and social protection are critical for improving tuberculosis (TB) treatment outcomes. This study aimed to evaluate whether a vulnerability assessment tool, developed for an enhanced community-based care and management (ECCM) program in 2 Korean cities, could predict and improve final TB treatment outcomes based on patients’ vulnerability levels. Methods: Treatment outcomes in the ECCM group were compared with those in a control group, stratified by vulnerability level. During stage 1, one city served as the intervention region and the other as the control, with a crossover in stage 2. The vulnerability assessment included all notified patients with TB, and those identified as highly vulnerable in the intervention group received social support following a consultation with a case manager. Results: The vulnerability assessment tool demonstrated moderate predictive ability for unfavorable outcomes, with an area under the curve of 0.70 (95% confidence interval, 0.63 to 0.77). The patients with high vulnerability who received ECCM treatment demonstrated a 19.8-percentage point (%p) higher treatment success rate than the high vulnerability subcategory of the control group. ECCM also appeared to reduce loss to follow-up and TB-related mortality by 8.4%p and 7.3%p, respectively, although these findings should be interpreted with caution. Conclusions The results suggest that providing social support tailored to patient vulnerability at the time of diagnosis could improve TB treatment outcomes.

Objectives: People-centered care and social protection are critical for improving tuberculosis (TB) treatment outcomes. This study aimed to evaluate whether a vulnerability assessment tool, developed for an enhanced community-based care and management (ECCM) program in 2 Korean cities, could predict and improve final TB treatment outcomes based on patients’ vulnerability levels. Methods: Treatment outcomes in the ECCM group were compared with those in a control group, stratified by vulnerability level. During stage 1, one city served as the intervention region and the other as the control, with a crossover in stage 2. The vulnerability assessment included all notified patients with TB, and those identified as highly vulnerable in the intervention group received social support following a consultation with a case manager. Results: The vulnerability assessment tool demonstrated moderate predictive ability for unfavorable outcomes, with an area under the curve of 0.70 (95% confidence interval, 0.63 to 0.77). The patients with high vulnerability who received ECCM treatment demonstrated a 19.8-percentage point (%p) higher treatment success rate than the high vulnerability subcategory of the control group. ECCM also appeared to reduce loss to follow-up and TB-related mortality by 8.4%p and 7.3%p, respectively, although these findings should be interpreted with caution. Conclusions The results suggest that providing social support tailored to patient vulnerability at the time of diagnosis could improve TB treatment outcomes.

Objectives: People-centered care and social protection are critical for improving tuberculosis (TB) treatment outcomes. This study aimed to evaluate whether a vulnerability assessment tool, developed for an enhanced community-based care and management (ECCM) program in 2 Korean cities, could predict and improve final TB treatment outcomes based on patients’ vulnerability levels. Methods: Treatment outcomes in the ECCM group were compared with those in a control group, stratified by vulnerability level. During stage 1, one city served as the intervention region and the other as the control, with a crossover in stage 2. The vulnerability assessment included all notified patients with TB, and those identified as highly vulnerable in the intervention group received social support following a consultation with a case manager. Results: The vulnerability assessment tool demonstrated moderate predictive ability for unfavorable outcomes, with an area under the curve of 0.70 (95% confidence interval, 0.63 to 0.77). The patients with high vulnerability who received ECCM treatment demonstrated a 19.8-percentage point (%p) higher treatment success rate than the high vulnerability subcategory of the control group. ECCM also appeared to reduce loss to follow-up and TB-related mortality by 8.4%p and 7.3%p, respectively, although these findings should be interpreted with caution. Conclusions The results suggest that providing social support tailored to patient vulnerability at the time of diagnosis could improve TB treatment outcomes.

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 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 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 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.

A 30-year-old Korean man with myelodysplastic syndrome admitted hospital due to undifferentiated fever and recurrent skin lesions. He received combination therapy with high doses of meropenem, tigecycline and amikacin, yielding carbapenem resistant Klebsiella pneumoniae (CRKP) harboring K. pneumoniae carbapenemase (KPC)-2 from blood cultures on hospital day (HD) 23. Ceftazidime/avibactam was started at HD 37 and CRKP was eradicated from blood cultures after 5 days. However, ceftazidime/avibactam-resistant CRKP carrying KPC-44 emerged after 26 days of ceftazidime/avibactam treatment and then ceftazidime/ avibactam-resistant, carbapenem-susceptible K. pneumoniae carrying KPC-135 was isolated on HD 65. The 3-D homology of KPC protein showed that hot spot changes in the omega loop could be attributed to ceftazidime/avibactam resistance and loss of carbapenem resistance.Whole genome sequencing of serial isolates supported that phenotypic variation was due to clonal evolution than clonal replacement. The treatment regimen was changed from CAZ/AVI to meropenem-based therapy (meropenem 1 g iv q 8 hours and amikacin 600 mg iv per day) starting with HD 72. CAZ/AVI-susceptible CRKP was presented again from blood cultures on HD 84, and the patient expired on HD 85. This is the first Korean report on the acquisition of ceftazidime/avibactam resistance through the emergence of blaKPC variants.

Herein, we report a case of uncomplicated falciparum malaria with late parasitological failure in a 45-year-old businessman returning from Ghana. The patient visited the emergency department with high fever, headache, and dizziness. He traveled without antimalarial chemoprophylaxis. Laboratory tests led to the diagnosis of uncomplicated falciparum malaria with an initial density of 37,669 parasites per μL of blood (p/μL). The patient was treated with intravenous artesunate followed by atovaquone/proguanil. He was discharged with improved condition and decreased parasite density of 887 p/μL. However, at follow-up, parasite density increased to 7,630 p/μL despite the absence of any symptoms. Suspecting treatment failure, the patient was administered intravenous artesunate and doxycycline for seven days and then artemether/lumefantrine for three days. Blood smear was negative for asexual parasitemia after re-treatment but positive for gametocytemia until day 101 from the initial diagnosis. Overall, this case highlights the risk of late parasitological failure in patients with imported uncomplicated falciparum malaria.

Background: Chronic cough is a common symptom encountered by healthcare practitioners.The global prevalence of chronic cough is 9.6%, with a female predominance. The aim of our study is to reveal the sex differences in prevalence and severity of chronic cough in South Korea, stratified by age and etiology. Methods: This study included adult patients with chronic cough who were recruited from 19 respiratory centers in South Korea. Patients completed the cough numeric rating scale (NRS) and COugh Assessment Test (COAT) questionnaire to assess the severity and multidimensional impact of cough. Results: Among the 625 patients, 419 (67.0%) were females, with a male-to-female ratio of 1:2.03. The mean age was 49.4 years, and the median duration of cough was 12 weeks. The mean NRS and COAT scores were 5.5 ± 1.8 and 9.5 ± 3.6, respectively. Female patients were older (45.3 ± 15.4 vs. 51.6 ± 15.2, P < 0.001) and more likely to have asthma/cough variant asthma (CVA) (26.7% vs. 40.8%, P = 0.001) than male patients. There was no difference in the duration or severity of cough between sexes, regardless of the cause. The male-tofemale ratio was lower for upper airway cough syndrome (UACS), asthma/CVA, and gastroesophageal reflux disease (GERD), but not for eosinophilic bronchitis (EB) or unexplained cough. The mean age of female patients was higher in UACS and asthma/CVA, but not in EB, GERD, or unexplained cough. The majority (24.2%) fell within the age category of 50s. The proportion of females with cough increased with age, with a significant rise in the 50s, 60s, and 70–89 age groups. The severity of cough decreased in the 50s, 60s, and 70–89 age groups, with no significant sex differences within the same age group. Conclusion The sex disparities in prevalence and severity of cough varied significantly depending on the age category and etiology. Understanding the specific sex-based difference could enhance comprehension of cough-related pathophysiology and treatment strategies.