Background: Latency in transferring patients from intensive care units (ICUs) to general wards impedes the optimal allocation of ICU resources, underscoring the urgency of initiatives to reduce it. This study evaluates the extent of ICU transfer latency and assesses the potential benefits of minimizing it. Methods: Transfer latency was measured as the time between the first transfer request and the actual ICU discharge at a single-center tertiary hospital in 2021. Computer-based simulations and cost analyses were performed to examine how reducing transfer latency could affect average hourly ICU bed occupancy, the proportion of time ICU occupancy exceeds 80%, and hospital costs. The first analysis evaluated all ICU admissions, and the second analysis targeted a subset of ICU admissions with longer transfer latency, those requiring infectious precautions. Results: A total of 7,623 ICU admissions were analyzed, and the median transfer latency was 5.7 hours. Eliminating transfer latency for all ICU admissions would have resulted in a 32.8% point decrease in the proportion of time ICU occupancy exceeded 80%, and a potential annual savings of $6.18 million. Eliminating transfer latency for patients under infectious precautions would have decreased the time ICU occupancy exceeded 80% by 13.5% points, and reduced annual costs by a potential $1.26 million. Conclusions Transfer latency from ICUs to general wards might contribute to high ICU occupancy. Efforts to minimize latency for all admissions, or even for a subset of admissions with particularly long transfer latency, could enable more efficient use of ICU resources.

Background: The design of intensive care units (ICUs) is increasingly acknowledged as a crucial factor affecting patient outcomes. Transitioning from multi-bed patient rooms (MPRs) to single-bed patient rooms (SPRs) aims to improve infection control, patient privacy, and quality of care. However, concerns remain regarding potential patient isolation and reduced staff situational awareness. This study aims to evaluate clinical outcomes in SPR-structured ICUs compared to mixed SPR and MPR ICUs. Methods: This multicenter retrospective cohort study was conducted across three university-affiliated tertiary hospitals between April 2022 and August 2023. The study population included ICU patients aged ≥18 years, excluding those admitted to cardiac and neonatal ICUs. Outcomes assessed included ICU mortality and severity scores based on Simplified Acute Physiology Score 3 and Acute Physiology and Chronic Health Evaluation II scores. Results: This study included 3,179 ICU patients across three sites: site A consisted exclusively of SPRs, while sites B and C had mixed SPR and MPR arrangements. ICU mortality rates were 8.3%, 15.2%, and 9.7% for sites A, B, and C, respectively (P<0.001). Propensity score matching and logistic regression analysis demonstrated that SPRs were associated with significantly reduced ICU mortality (adjusted odds ratio, 0.54; 95% CI, 0.40–0.73). Conclusions SPRs were associated with a protective effect, reducing ICU mortality. Clinical outcomes in ICUs appear to be influenced by structural design improvements alongside other clinical factors.

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.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Cardio-oncology is a critical field due to the escalating significance of cardiovascular toxicity as a side effect of anti‑ cancer treatments. Cancer therapy-related cardiac dysfunction (CTRCD) is a prevalent condition associated with car‑ diovascular toxicity, necessitating effective strategies for prediction, monitoring, management, and tracking. This comprehensive review examines the definition and risk stratification of CTRCD, explores monitoring approaches during anticancer therapy, and highlights specific cardiovascular toxicities linked to various cancer treatments. These include anthracyclines, HER2-targeted agents, vascular endothelial growth factor inhibitors, immune checkpoint inhibitors, chimeric antigen receptor T-cell therapies, and tumor-infiltrating lymphocytes therapies. Incorporating the Korean data, this review offers insights into the regional nuances in managing CTRCD. Using systematic follow-up incorporating cardiovascular imaging and biomarkers, a better understanding and management of CTRCD can be achieved, optimizing the cardiovascular health of both cancer patients and survivors.

Background: Limited data are available on the mortality rates of patients receiving extracorporeal membrane oxygenation (ECMO) support for coronavirus disease 2019 (COVID-19). We aimed to analyze the relationship between COVID-19 and clinical outcomes for patients receiving ECMO. Methods: We retrospectively investigated patients with COVID-19 pneumonia requiring ECMO in 19 hospitals across Korea from January 1, 2020 to August 31, 2021. The primary outcome was the 90-day mortality after ECMO initiation. We performed multivariate analysis using a logistic regression model to estimate the odds ratio (OR) of 90-day mortality. Survival differences were analyzed using the Kaplan–Meier (KM) method. Results: Of 127 patients with COVID-19 pneumonia who received ECMO, 70 patients (55.1%) died within 90 days of ECMO initiation. The median age was 64 years, and 63% of patients were male. The incidence of ECMO was increased with age but was decreased after 70 years of age. However, the survival rate was decreased linearly with age. In multivariate analysis, age (OR, 1.048; 95% confidence interval [CI], 1.010–1.089; P = 0.014) and receipt of continuous renal replacement therapy (CRRT) (OR, 3.069; 95% CI, 1.312–7.180; P = 0.010) were significantly associated with an increased risk of 90-day mortality. KM curves showed significant differences in survival between groups according to age (65 years) (log-rank P = 0.021) and receipt of CRRT (log-rank P = 0.004). Conclusion Older age and receipt of CRRT were associated with higher mortality rates among patients with COVID-19 who received ECMO.

Background: and Purpose Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted image (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype. Methods: Model development was done in 2,988 patients with ischemic stroke from three centers by using U-net for infarct segmentation and EfficientNetV2 for subtype classification. Experienced neurologists (n=5) determined subtypes for external test datasets, while establishing a consensus for clinical trial datasets. Automatically segmented infarcts were fed into the model (DWI-only algorithm). Subsequently, another model was trained, with AF included as a categorical variable (DWI+AF algorithm). These models were tested: (1) internally against the opinion of the labeling experts, (2) against fresh external DWI data, and (3) against clinical trial dataset. Results: In the training-and-validation datasets, the mean (±standard deviation) age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only and the DWI+AF algorithms respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3%–60.7% and 73.7%–74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen’s kappa were respectively 58.1% and 0.34 for the DWI-only vs. 72.9% and 0.57 for the DWI+AF algorithms. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm. Conclusion Our model trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes comparable to a consensus of stroke experts.

Objective: Geriatric patients requiring gynecological surgery is increasing worldwide. However, older patients are at higher risk of postoperative morbidity and mortality, particularly cardiopulmonary complications. Laparoscopic surgery is widely used as a minimally invasive method for reducing postoperative morbidities. We compared the outcomes of open and laparoscopic gynecologic surgeries in patients older than 55 years. Methods: We included patients aged >55 years who underwent gynecological surgery at a single tertiary center between 2010 and 2020, excluding vaginal or ovarian cancer surgeries were excluded. Surgical outcomes were compared between the open surgery and laparoscopic groups, with age cutoff was set at 65 years for optimal discriminative power. We performed linear or logistic regression analyses to compare the surgical outcomes according to age and operation type. Results: Among 2,983 patients, 28.6% underwent open surgery and 71.4% underwent laparoscopic surgery. Perioperative outcomes of laparoscopic surgery were better than those of open surgery in all groups. In both the open and laparoscopic surgery groups, the older patients showed worse overall surgical outcomes. However, age-related differences in perioperative outcomes were less severe in the laparoscopic group. In the linear regression analysis, the differences in estimated blood loss, transfusion, and hospital stay between the age groups were smaller in the laparoscopy group. Similar results were observed in cancer-only and benign-only cohorts. Conclusion Although the surgical outcomes were worse in the older patients, the difference between age groups was smaller for laparoscopic surgery. Laparoscopic surgery offers more advantages and safety in patients aged >65 years.

The bond strength to the silicate-based CAD/CAM blocks may be affected by the composition and microstructure of the silicatebased blocks, surface treatment, and the properties of the applied adhesive. In this study, the shear bond strength to the silicate blocks with various universal adhesives after sandblasting or HF-treatment was measured, and the effects of the additional silane application after surface treatments are evaluated. Six silica-containing blocks and five universal adhesives currently used in dental clinics were selected. After polishing the silicate block surface, the specimens were divided into two groups. The first groups were HF-treated and the second groups were sandblasted with alumina, and the surface roughness, contact angle, and microstructure were analyzed by CLSM (Confocal Laser Scanning Microscope), contact angle analyzer, and FE-SEM (Field Emission Scanning Electron Microscope).Composite resin (2 mm diameter) was bonded with universal adhesive to silicate blocks and stored in a 37 ℃ water bath for 24 hours, and the shear bond strength was measured using a universal testing machine. The measured values were statistically analyzed using the Tukey-multiple comparison test (α=0.05). For hybrid composite ceramics, there was no significant difference in bond strength between sandblasting or HF-treatment, and additional silane application may not be necessary when bonding with a universal adhesive, whereas for leucite-reinforced and lithium disilicate glass-ceramics, HF-treatment may be more favorable for adhesion than sandblasting, and additional silane application appears to be necessary even when applying a universal adhesive.