Vertebral arteriovenous fistula (VAVF) is a rare lesion characterized by an abnormal connection between the extracranial vertebral artery and the surrounding venous plexus. It typically arises due to penetrating injury, although it can occasionally result from blunt trauma. Brachial plexus injury (BPI) is also infrequently associated with VAVF. We present a rare case of VAVF caused by blunt trauma, which resulted in BPI. The patient, who had previously sustained a C2 fracture and C2–3 myelopathy from a bicycle accident, presented with new-onset weakness in the right upper extremity. His previous clinical history led to an initial suspicion of either an exacerbation of a pre-existing lesion or a shoulder injury. However, electromyography indicated that the weakness was due to BPI. Further evaluations later revealed VAVF to be the primary cause of the BPI. VAVF must be recognized as a rare potential reason for BPI, as timely intervention is essential for improving patient recovery and prognosis.

Objectives: We aimed to derive observational research evidence on treatment patterns through a scoping review of common data model (CDM)-based publications. Methods: We searched the medical literature databases PubMed and EMBASE, as well as the Observational Health Data Sciences and Informatics (OHDSI) website, for papers published between January 1, 2010 and August 21, 2023 to identify research papers relevant to our topic. Results: Eighteen articles satisfied the inclusion criteria for this scoping review. We summarized study characteristics such as phenotypes, patient numbers, data periods, countries, Observational Medical Outcomes Partnership (OMOP) CDM databases, and definitions of index date and target cohort. Type 2 diabetes mellitus emerged as the most frequently studied disease, covered in five articles, followed by hypertension and depression, each addressed in four articles. Biguanides, with metformin as the primary drug, were the most commonly prescribed first-line treatments for type 2 diabetes mellitus. Most studies utilized sunburst plots to visualize treatment patterns, whereas two studies used Sankey plots. Various software tools were employed for treatment pattern analysis, including JavaScript, the open-source ATLAS by OHDSI, R code, and the R package “TreatmentPatterns.” Conclusions This study provides a comprehensive overview of research on treatment patterns using the CDM, highlighting the growing importance of OMOP CDM in enabling multinational observational network studies and advancing collaborative research in this field.

Objectives: We aimed to derive observational research evidence on treatment patterns through a scoping review of common data model (CDM)-based publications. Methods: We searched the medical literature databases PubMed and EMBASE, as well as the Observational Health Data Sciences and Informatics (OHDSI) website, for papers published between January 1, 2010 and August 21, 2023 to identify research papers relevant to our topic. Results: Eighteen articles satisfied the inclusion criteria for this scoping review. We summarized study characteristics such as phenotypes, patient numbers, data periods, countries, Observational Medical Outcomes Partnership (OMOP) CDM databases, and definitions of index date and target cohort. Type 2 diabetes mellitus emerged as the most frequently studied disease, covered in five articles, followed by hypertension and depression, each addressed in four articles. Biguanides, with metformin as the primary drug, were the most commonly prescribed first-line treatments for type 2 diabetes mellitus. Most studies utilized sunburst plots to visualize treatment patterns, whereas two studies used Sankey plots. Various software tools were employed for treatment pattern analysis, including JavaScript, the open-source ATLAS by OHDSI, R code, and the R package “TreatmentPatterns.” Conclusions This study provides a comprehensive overview of research on treatment patterns using the CDM, highlighting the growing importance of OMOP CDM in enabling multinational observational network studies and advancing collaborative research in this field.

Objectives: We aimed to derive observational research evidence on treatment patterns through a scoping review of common data model (CDM)-based publications. Methods: We searched the medical literature databases PubMed and EMBASE, as well as the Observational Health Data Sciences and Informatics (OHDSI) website, for papers published between January 1, 2010 and August 21, 2023 to identify research papers relevant to our topic. Results: Eighteen articles satisfied the inclusion criteria for this scoping review. We summarized study characteristics such as phenotypes, patient numbers, data periods, countries, Observational Medical Outcomes Partnership (OMOP) CDM databases, and definitions of index date and target cohort. Type 2 diabetes mellitus emerged as the most frequently studied disease, covered in five articles, followed by hypertension and depression, each addressed in four articles. Biguanides, with metformin as the primary drug, were the most commonly prescribed first-line treatments for type 2 diabetes mellitus. Most studies utilized sunburst plots to visualize treatment patterns, whereas two studies used Sankey plots. Various software tools were employed for treatment pattern analysis, including JavaScript, the open-source ATLAS by OHDSI, R code, and the R package “TreatmentPatterns.” Conclusions This study provides a comprehensive overview of research on treatment patterns using the CDM, highlighting the growing importance of OMOP CDM in enabling multinational observational network studies and advancing collaborative research in this field.

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: Emergency department (ED) overcrowding poses a global challenge, particularly for critically ill patients requiring intensive care unit (ICU) admission. Although delays in ICU transfer increase mortality in critically ill populations, the optimal timing for septic shock remains uncertain. Methods: We conducted a target trial emulation using a prospective cohort of 815 septic shock patients from 19 Korean hospitals. Delayed ICU transfer was defined using restricted cubic splines. The primary outcome was in-hospital mortality. Multivariable logistic regression and inverse probability treatment weighting were used to adjust for confounders of age, sex, comorbidities, severity of illness, and mechanical ventilation use. Subgroup analyses were performed to assess the effect across patient characteristics. Results: The median time of ED-to-ICU transfer was 6.7 hours (interquartile range, 4.7–11.4), and only 7% of patients were transferred within 3 hours. ICU transfer within 3 hours was associated with significantly lower in-hospital mortality (odds ratio, 0.48; 95% CI, 0.24–0.94) compared to later transfers. Mortality risk increased with elapsing time up to 6 hours and then plateaued. The benefit of early ICU transfer was consistent across subgroups but was particularly pronounced in patients requiring extracorporeal membrane oxygenation or continuous renal replacement therapy (P for interaction=0.02). Conclusions Early ICU transfer within 3 hours significantly reduces mortality in patients with septic shock, with the greatest benefit observed in those requiring advanced organ support. These findings highlight the need for system-wide strategies to reduce ED boarding time and prioritize timely ICU admission for septic shock management.

In a case of contrast media-induced anaphylactic shock managed with epinephrine, a 57-year-old male patient developed lactic acidosis without cardiogenic shock or global hypoperfusion, highlighting epinephrine’s potential to trigger lactic acidosis. Despite the previous management of similar reactions with antihistamines and corticosteroids, this case required intensive care unit admission and emergency intervention for alarmingly high lactate level. The rapid resolution of acidosis following epinephrine discontinuation underscores the need for careful monitoring and consideration of alternative vasopressor strategies in severe anaphylaxis, illustrating the complex relationship between epinephrine’s metabolic effects and anaphylaxis-induced tissue hypoperfusion.

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.

This retrospective cohort study aimed to compare coronavirus disease 2019 (COVID-19)-related clinical outcomes between patients with and without gout. Electronic health recordbased data from two centers (Seoul National University Hospital [SNUH] and Boramae Medical Center [BMC]), from January 2021 to April 2022, were mapped to a common data model. Patients with and without gout were matched using a large-scale propensityscore algorithm based on population-level estimation methods. At the SNUH, the risk for COVID-19 diagnosis was not significantly different between patients with and without gout (hazard ratio [HR], 1.07; 95% confidence interval [CI], 0.59–1.84). Within 30 days after COVID-19 diagnosis, no significant difference was observed in terms of hospitalization (HR, 0.57; 95% CI, 0.03–3.90), severe outcomes (HR, 2.90; 95% CI, 0.54–13.71), or mortality (HR, 1.35; 95% CI, 0.06–16.24). Similar results were obtained from the BMC database, suggesting that gout does not increase the risk for COVID-19 diagnosis or severe outcomes.

Background: Current international guidelines recommend against deep sedation as it is associated with worse outcomes in the intensive care unit (ICU). However, in Korea the prevalence of deep sedation and its impact on patients in the ICU are not well known. Methods: From April 2020 to July 2021, a multicenter, prospective, longitudinal, noninterventional cohort study was performed in 20 Korean ICUs. Sedation depth extent was divided into light and deep using a mean Richmond Agitation–Sedation Scale value within the first 48 hours. Propensity score matching was used to balance covariables; the outcomes were compared between the two groups. Results: Overall, 631 patients (418 [66.2%] and 213 [33.8%] in the deep and light sedation groups, respectively) were included. Mortality rates were 14.1% and 8.4% in the deep and light sedation groups (P = 0.039), respectively. Kaplan-Meier estimates showed that time to extubation (P < 0.001), ICU length of stay (P = 0.005), and death P = 0.041) differed between the groups. After adjusting for confounders, early deep sedation was only associated with delayed time to extubation (hazard ratio [HR], 0.66; 95% confidence inter val [CI], 0.55– 0.80; P < 0.001). In the matched cohort, deep sedation remained significantly associated with delayed time to extubation (HR, 0.68; 95% 0.56–0.83; P < 0.001) but was not associated with ICU length of stay (HR, 0.94; 95% CI, 0.79–1.13; P = 0.500) and in-hospital mortality (HR, 1.19; 95% CI, 0.65–2.17; P = 0.582). Conclusion In many Korean ICUs, early deep sedation was highly prevalent in mechanically ventilated patients and was associated with delayed extubation, but not prolonged ICU stay or in-hospital death.