Vasoactive and inotropic medications are essential for sepsis management; however, the association between the maximum Vasoactive-Inotropic score (VISmax) and clinical outcomes is unknown in adult patients with sepsis. We investigated the VISmax as a predictor for mortality among such patients in the emergency department (ED) and compared its prognostic value with that of the sequential organ failure assessment (SOFA) score. This single-center retrospective study included 910 patients diagnosed with sepsis between January 2016 and March 2020. We calculated the VISmax using the highest doses of vasopressors and inotropes administered during the first 6 h on ED admission and categorized it as 0–5, 6–15, 16–30, 31–45, and >45 points. The primary outcome was 30-day mortality. VISmax for 30-day mortality was significantly higher in non-survivors than in survivors. The mortality rates in the five VISmax groups were 17.2%, 20.8%, 33.3%, 54.6%, and 70.0%, respectively. The optimal cut-off value of VISmax to predict 30-day mortality was 31. VISmax had better prognostic value than the cardiovascular component of the SOFA score and initial lactate levels. VISmax was comparable to the APACHE II score in predicting 30-day mortality. Multivariable analysis showed that VISmax 16–30, 31–45, and >45 were independent risk factors for 30-day mortality. VISmax in ED could help clinicians to identify sepsis patients with poor prognosis.

Background: Timely and accurate triage is crucial for the emergency department (ED) care. Recently, there has been growing interest in applying large language models (LLMs) to support triage decision-making. However, most existing studies have evaluated these models using simulated scenarios rather than real-world clinical cases. Therefore, we evaluated the performance of multiple commercial LLMs for non-critical triage support in ED using real-world clinical conversations. Methods: We retrospectively analyzed real-world triage conversations prospectively collected from three tertiary hospitals in South Korea. Multiple commercial LLMs—including OpenAI GPT-4o, GPT-4.1, O3, Google Gemini 2.0 flash, Gemini 2.5 flash, Gemini 2.5 pro, DeepSeek V3, and DeepSeek R1—were evaluated for the accuracy in triaging patient urgency based solely on unsummarized dialogue. The Korean Triage and Acuity Scale (KTAS) assigned by triage nurses was used as the gold standard for evaluating the LLM classifications. Model performance was assessed under both a zero-shot prompting condition and a few-shot prompting condition that included representative examples. Results: A total of 1,057 triage cases were included in the analysis. Among the models, Gemini 2.5 flash achieved the highest accuracy (73.8%), specificity (88.9%), and PPV (94.0%). Gemini 2.5 pro demonstrated the highest sensitivity (90.9%) and F1-score (82.4%), though with lower specificity (23.3%). GPT-4.1 also showed balanced high accuracy (70.6%) and sensitivity (81.3%) with practical response times (1.79s). Performance varied widely between models and even between different versions from the same vendor. With few-shot prompting, most models showed further improvements in accuracy and F1-score. Conclusions: LLMs can accurately triage ED patient urgency using real-world clinical conversations. Several models demonstrated both high sensitivity and acceptable response times, supporting the feasibility of LLM in non-critical triage support tools in diverse clinical environments. These findings apply to non-critical patients (KTAS 3–5), and further research should address integration with objective clinical data and real-time workflow.

We developed a novel computer program, the Intelligent Sepsis Management System, based on Sepsis-3 definitions and 2016 Surviving Sepsis Campaign guidelines and performed a quasi-experimental pre-post study to assess its effect on compliance with the Surviving Sepsis Campaign guidelines and outcomes in patients with sepsis and septic shock. During the pre-period, patients were managed with usual care. During the post-period, patients were managed using the Intelligent Sepsis Management System upon arrival at the emergency department. A total of 631 patients were enrolled (pre-period, 316; post-period, 315). The overall compliance with the Surviving Sepsis Campaign guidelines’ bundle improved (pre-period 10.8% vs. post-period 54.6%; p < 0.001). The post-period showed significantly lower 30-day mortality than the pre-period (pre-period 37.3% vs. post-period 29.5%; p = 0.037), but was not a protective factor for 30-day mortality, with an adjusted hazard ratio (95% confidence interval) of 0.75 (0.55–1.04) (p = 0.151). The associated factors for 30-day mortality were age, sequential organ failure assessment score, overall compliance, and lactate levels. The 30-day mortality was significantly lower in the compliance group than in the non-compliance group (27.2% vs. 36.5%; p = 0.002). After implementation of the Intelligent Sepsis Management System, overall compliance with the Surviving Sepsis Campaign guidelines improved and was associated with reduced 30-day mortality. However, we could not verify the causal effect of this system on 30-day mortality.

Detecting sepsis patients who are at a high-risk of mechanical ventilation is important in emergency departments (ED). The respiratory rate oxygenation (ROX) index is the ratio of tissue oxygen saturation/fraction of inspired oxygen to the respiratory rate. This study aimed to investigate whether the ROX index could predict mechanical ventilator use in sepsis patients in an ED. This retrospective observational study included quick sequential organ failure assessment (qSOFA) ≥ 2 sepsis patients that presented to the ED between September 2019 and April 2020. The ROX and ROX-heart rate (HR) indices were significantly lower in patients with mechanical ventilator use within 24 h than in those without the use of a mechanical ventilator (4.0 [3.2–5.4] vs. 10.0 [5.9–15.2], p < 0.001 and 3.9 [2.7–5.8] vs. 10.1 [5.4–16.3], p < 0.001, respectively). The area under the receiver operating characteristic (ROC) curve of the ROX and ROX-HR indices were 0.854 and 0.816 (both p < 0.001). The ROX and ROX-HR indices were independently associated with mechanical ventilator use within 24 h (adjusted hazard ratio = 0.78, 95% CI: 0.68–0.90, p < 0.001 and adjusted hazard ratio = 0.87, 95% CI 0.79–0.96, p = 0.004, respectively). The 28-day mortality was higher in the low ROX and low ROX-HR groups. The ROX and ROX-HR indices were associated with mechanical ventilator use within 24 h in qSOFA ≥ 2 patients in the ED.

In addition to glycemic control, the anti-inflammatory effects and protective effect of metformin on sepsis have been reported in animal studies, which may be beneficial for patients with septic shock. Few observational studies have evaluated metformin administration after sepsis or bacteremia; however, these studies did not specifically analyze septic shock or long-term outcomes. Therefore, this study aimed to evaluate the associations between metformin administration after septic shock and the short- and long-term survival in septic shock patients with type 2 diabetes mellitus. This retrospective observational study used data from a prospectively collected sepsis registry. From October 2016 to June 2022, adult septic shock patients with type 2 diabetes mellitus were included in this study. The variable of interest was metformin administration within 48 h after diagnosis of septic shock. The 90-day mortality and 365-day mortality were evaluated as outcomes. A multivariable Cox proportional hazards model was conducted. A total of 320 patients were included in the study. Metformin administration within 48 h after diagnosis of septic shock was associated with lower 90-day mortality (13.0% vs. 39.8%, P < 0.001), 365-day mortality (23.3% vs. 48.3%, P = 0.001), and in-hospital mortality (9.3% vs. 28.6%, P = 0.002) than those who did not administer metformin within 48 h. Metformin administration within 48 h was independently associated with decreased 90-day mortality (adjusted hazard ratio [aHR]: 0.371, 95% confidence interval [CI]: 0.153-0.900, P = 0.028) and 365-day mortality (aHR 0.453, 95% CI 0.219-0.937, P = 0.033) after adjusting for potential confounders. Similar results were found for metformin administration within 72 h after septic shock (aHR 0.433, 95% CI 0.235-0.797, P = 0.007 for 90-day mortality and aHR 0.450, 95% CI 0.264-0.767, P = 0.003 for 365-day mortality). In septic shock patients with type 2 diabetes mellitus, metformin administration within 48 h was associated with lower 90-day and 365-day mortality. While these findings suggest potential benefits of metformin administration after septic shock, further large, multicenter studies are warranted.

Bacteremia requires early detection and appropriate treatment. High rates of false-positives in blood cultures in emergency departments (EDs) have led to the need for accurate predictive tools. This study developed and validated a bacteremia prediction score to improve patient selection for blood cultures. A multicenter retrospective cohort study used data from a tertiary academic hospital in Republic of Korea (2014–2019) to develop and validate the model, with external validation at two additional hospitals (Hospital A and Hospital B) (2022). The AutoScore method, integrating machine learning and logistic regression, generated the score using initial vital signs and laboratory data. Model performance was assessed via the area under the receiver operating characteristic (AUROC) curve. Of 24,856 patients, 17,373 formed the development cohort, 2491 the validation cohort, and 4992 the test cohort. Bacteremia incidence was 11.39%. The score, incorporating eight variables (procalcitonin, lactate, white blood cell count, c-reactive protein, blood urea nitrogen, body temperature, platelet count, and total bilirubin), achieved an AUROC of 0.825 (95% confidence interval [CI], 0.808–0.842), outperforming procalcitonin (0.808; 95% CI, 0.790–0.826), SOFA score (0.718; 95% CI, 0.696–0.741), MEWS score (0.630; 95% CI, 0.606–0.654) and SIRS score (0.583; 95% CI, 0.558–0.608). External validation yielded AUROCs of 0.796 (95% CI, 0.773–0.818) at Hospital A and 0.813 (95% CI, 0.778–0.848) at Hospital B. Risk stratification categorized patients into low-, intermediate-, and high-risk groups with bacteremia rates of 2.6–3.2%, 5.2–9.3%, and 23.7–30.2%. A bacteremia prediction score was developed and validated using AutoScore, integrating machine learning and logistic regression. The model, based on eight clinical variables, stratified patients by risk and showed favorable predictive performance compared with conventional markers.