The prevalence of dementia is 2- to 7-fold higher among patients with end-stage kidney disease (ESKD) than among the general population; however, its clinical implications in this population remain unclear. Therefore, this study aimed to determine whether comorbid dementia increases mortality among older patients with ESKD undergoing newly initiated hemodialysis. Methods: We analyzed data from the Korean Society of Geriatric Nephrology retrospective cohort, which included 2,736 older ESKD patients (≥70 years old) who started hemodialysis between 2010 and 2017. Kaplan-Meier survival and Cox regression analyses were used to examine all-cause mortality between the patients with and without dementia in this cohort. Results: Of the 2,406 included patients, 8.3% had dementia at the initiation of dialysis; these patients were older (79.6 ± 6.0 years) than patients without dementia (77.7 ± 5.5 years) and included more women (male:female, 89:111). Pre-ESKD diagnosis of dementia was associated with an increased risk of overall mortality (hazard ratio, 1.503; p < 0.001), and this association remained consistent after multivariate adjustment (hazard ratio, 1.268; p = 0.009). In subgroup analysis, prevalent dementia was associated with mortality following dialysis initiation in female patients, those aged <85 years, those with no history of cerebrovascular accidents or severe behavioral disorders, those not residing in nursing facilities, and those with no or short-term hospitalization. Conclusion: A pre-ESKD diagnosis of dementia is associated with mortality following dialysis initiation in older Korean population. In older patients with ESKD, cognitive assessment at dialysis initiation is necessary.

Digital therapeutics are emerging as treatments for diseases and disabilities. In chronic kidney disease (CKD), gait is a potential biomarker for health status and intervention effectiveness. This study aims to analyze gait characteristics in CKD patients, providing baseline data for digital therapeutics development. Methods: At baseline and after an 8-week intervention, we performed bioimpedance analysis measurements, the Timed Up and Go, Tinetti, and grip strength tests, and gait analysis in 217 healthy individuals and 276 patients with CKD. Demographic and clinical information was collected, including underlying diseases and medications, laboratory tests, and quality of life satisfaction surveys. Gait analysis was performed using skeleton data, which involved acquiring three-dimensional skeleton data of a walker using a single Kinect sensor. The performance of an artificial intelligence-based classification model in distinguishing between healthy individuals and those with CKD was then investigated. Simultaneously, inertia measurement unit analysis was conducted using measurements taken from the wrist and waist. Results: Most subjects received a health intervention via an app, and their gait was assessed for improvements after an 8-week period. Incidents such as falls, fractures, hospitalizations, and deaths will be investigated in years 1 and 3. Conclusion: This study confirmed that the gaits of healthy individuals and CKD patients were different, and the effect of the 8-week app-based health intervention will be analyzed. The study will yield important baseline data for creating digital therapeutics for CKD patients’ diet/exercise in the future.

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.

Remimazolam, an ultra-short-acting anesthetic with flumazenil as a reversal agent, typically facilitates patient awakening postoperatively. However, our case reveals an unusual occurrence: despite flumazenil initially restoring consciousness, re-sedation due to remimazolam ensued six hours later. Case: A 65-year-old woman underwent total intravenous general anesthesia with remimazolam and remifentanil during the 140-min surgery. Despite an initially smooth recovery, she progressively became drowsy upon transfer to the general ward, eventually reaching a stuporous state. Multiple interventions, including opioid reversal (intravenous patient-controlled analgesia discontinuation, and naloxone administration) were attempted. Neurological consultation revealed no issues; however, immediate improvement after flumazenil administration suggested remimazolam’s involvement. The patient was discharged without complications. Conclusions: This case challenges our understanding of remimazolam’s dynamics, emphasizing the necessity for vigilant post-anesthesia monitoring, even in seemingly low-risk cases. It advocates for standardized response protocols to promptly manage unforeseen events and ensure patient safety.

Remimazolam, an ultra-short-acting anesthetic with flumazenil as a reversal agent, typically facilitates patient awakening postoperatively. However, our case reveals an unusual occurrence: despite flumazenil initially restoring consciousness, re-sedation due to remimazolam ensued six hours later. Case: A 65-year-old woman underwent total intravenous general anesthesia with remimazolam and remifentanil during the 140-min surgery. Despite an initially smooth recovery, she progressively became drowsy upon transfer to the general ward, eventually reaching a stuporous state. Multiple interventions, including opioid reversal (intravenous patient-controlled analgesia discontinuation, and naloxone administration) were attempted. Neurological consultation revealed no issues; however, immediate improvement after flumazenil administration suggested remimazolam’s involvement. The patient was discharged without complications. Conclusions: This case challenges our understanding of remimazolam’s dynamics, emphasizing the necessity for vigilant post-anesthesia monitoring, even in seemingly low-risk cases. It advocates for standardized response protocols to promptly manage unforeseen events and ensure patient safety.

Remimazolam, an ultra-short-acting anesthetic with flumazenil as a reversal agent, typically facilitates patient awakening postoperatively. However, our case reveals an unusual occurrence: despite flumazenil initially restoring consciousness, re-sedation due to remimazolam ensued six hours later. Case: A 65-year-old woman underwent total intravenous general anesthesia with remimazolam and remifentanil during the 140-min surgery. Despite an initially smooth recovery, she progressively became drowsy upon transfer to the general ward, eventually reaching a stuporous state. Multiple interventions, including opioid reversal (intravenous patient-controlled analgesia discontinuation, and naloxone administration) were attempted. Neurological consultation revealed no issues; however, immediate improvement after flumazenil administration suggested remimazolam’s involvement. The patient was discharged without complications. Conclusions: This case challenges our understanding of remimazolam’s dynamics, emphasizing the necessity for vigilant post-anesthesia monitoring, even in seemingly low-risk cases. It advocates for standardized response protocols to promptly manage unforeseen events and ensure patient safety.

Remimazolam, an ultra-short-acting anesthetic with flumazenil as a reversal agent, typically facilitates patient awakening postoperatively. However, our case reveals an unusual occurrence: despite flumazenil initially restoring consciousness, re-sedation due to remimazolam ensued six hours later. Case: A 65-year-old woman underwent total intravenous general anesthesia with remimazolam and remifentanil during the 140-min surgery. Despite an initially smooth recovery, she progressively became drowsy upon transfer to the general ward, eventually reaching a stuporous state. Multiple interventions, including opioid reversal (intravenous patient-controlled analgesia discontinuation, and naloxone administration) were attempted. Neurological consultation revealed no issues; however, immediate improvement after flumazenil administration suggested remimazolam’s involvement. The patient was discharged without complications. Conclusions: This case challenges our understanding of remimazolam’s dynamics, emphasizing the necessity for vigilant post-anesthesia monitoring, even in seemingly low-risk cases. It advocates for standardized response protocols to promptly manage unforeseen events and ensure patient safety.

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.

Remimazolam, an ultra-short-acting anesthetic with flumazenil as a reversal agent, typically facilitates patient awakening postoperatively. However, our case reveals an unusual occurrence: despite flumazenil initially restoring consciousness, re-sedation due to remimazolam ensued six hours later. Case: A 65-year-old woman underwent total intravenous general anesthesia with remimazolam and remifentanil during the 140-min surgery. Despite an initially smooth recovery, she progressively became drowsy upon transfer to the general ward, eventually reaching a stuporous state. Multiple interventions, including opioid reversal (intravenous patient-controlled analgesia discontinuation, and naloxone administration) were attempted. Neurological consultation revealed no issues; however, immediate improvement after flumazenil administration suggested remimazolam’s involvement. The patient was discharged without complications. Conclusions: This case challenges our understanding of remimazolam’s dynamics, emphasizing the necessity for vigilant post-anesthesia monitoring, even in seemingly low-risk cases. It advocates for standardized response protocols to promptly manage unforeseen events and ensure patient safety.

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.