Background: and Purpose Although gastrointestinal (GI) surgery can affect drug efficacy, its impact on oral anticoagulants (OACs) remains unclear. This study investigated the impact of GI surgery on the comparative effectiveness and safety of direct oral anticoagulants (DOACs) versus warfarin in atrial fibrillation (AF). Methods: Using a nationwide South Korean claims database, AF patients prescribed OACs between 2015 and 2021 were analyzed. The primary outcome was ischemic stroke, and secondary outcomes were major bleeding, all-cause death, and composite outcome (ischemic stroke, major bleeding, all-cause death). Outcomes were compared by OAC type (DOAC vs. warfarin) and GI surgery status using inverse probability of treatment weighting and a Fine–Gray subdistribution hazard model with time-varying covariates. Results: Among 388,214 AF patients (mean age, 71.9 yr; 42.7% male), 6,907 (1.8%) underwent GI surgery. The effect of GI surgery differed between warfarin and DOACs for ischemic stroke (Pint= 0.018), with increased risk for warfarin (adjusted HR [aHR]: 2.32; 95% confidence interval [CI] 1.17–4.62), but not for DOACs (aHR: 0.97 [95% CI 0.77–1.22]) after surgery. Effects of surgery on major bleeding, all-cause death, and composite outcome did not differ. In post–GI surgery setting, DOACs demonstrated lower ischemic stroke risk (aHR: 0.35 [95% CI 0.17–0.72]) and comparable risks for other outcomes. Exploratory analyses by surgery location suggested a more favorable profile of DOACs after upper GI surgery. Conclusions In AF patients, GI surgery significantly increased ischemic stroke risk among warfarin users, but not among DOAC users. DOACs showed generally favorable profiles after GI surgery and may remain a reasonable anticoagulant, with potentially more favorable profile after upper GI surgery.

Purpose: : This retrospective study clarified the peripheral limb ischemiaincidence and examined the differences in demographic, clinical, and nursing care-related characteristics according to the presence of peripheral limb ischemia in intensive care unit (ICU) patients undergoing extracorporeal membrane oxygenation (ECMO) therapy. Methods: : Participants were 170 patients who received ECMO therapy at a university hospital over a 10-year period (January 2013 to December 2022). Data, including general, clinical, and nursing care characteristics, were collected from electronic medical records. Data analysis was conducted using descriptive statistics, independent t-tests, χ2-tests, and Fisher’s exact tests. Results: : Participants’ mean age was 57.49 years, 125 (73.5%) were male, and the average duration of ECMO therapy was 7.22 days. Among them, 75 (44.1%) patients developed peripheral limb ischemia. Significant differences were found between the groups with and without peripheral limb ischemia in mean arterial pressure (t=6.19, p ‹.001), vasopressor use (χ2=8.54, p =.003), anticoagulant use (χ2=15.01, p ‹.001), use of clotting factors (χ2=5.84, p=.016), presence of abdominal distension (χ2=8.97, p=.003), insertion site bleeding (χ2=8.14, p =.004), and laboratory values including white blood cell count (t=2.65, p =.009), hemoglobin (t=2.71, p =.008), platelet count (t=2.88, p =.004), creatine kinase (t=-4.47, p ‹.001), prothrombin time (t=-4.22, p ‹.001), international normalized ratio (t=-4.29, p ‹.001), and frequency of lower limb monitoring (t=5.57, p ‹.001). Conclusion : This study explored the clinical and nursing care-related characteristics associated with peripheral limb ischemia in ICU patients undergoing ECMO therapy. The findings provide evidence to support future research focusing on the early detection and proactive nursing interventions to prevent peripheral limb ischemia in this high-risk population.

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.