Background: Carbon monoxide (CO) poisoning is a leading cause of toxicological emergencies worldwide. Although carboxyhemoglobin (COHb) levels are traditionally used to confirm exposure, they often fail to reflect clinical severity. This study aimed to evaluate the diagnostic performance of serum lactate and hematological parameters in predicting the need for hyperbaric oxygen therapy (HBOT) among patients with acute CO poisoning. Materials and methods: This retrospective cross-sectional study included 292 adult patients with confirmed CO poisoning admitted to a tertiary emergency department between 2020 and 2024. Patients were categorized according to HBOT administration. Laboratory values including lactate, COHb, and hematologic parameters and indices were analyzed. Multivariable logistic regression and ROC curve analysis were used to assess predictive performance. Results: Of the 292 patients, 94 (32.2%) received HBOT. Serum lactate and COHb were significantly higher in the HBOT group ( p < 0.001 for both) and were identified as independent predictors of HBOT requirement (lactate OR = 2.04; COHb OR = 1.32). AUC for lactate alone was 0.754; combining lactate with hematologic markers modestly improved AUC to 0.769. The most robust model, incorporating lactate and COHb, achieved an AUC of 0.936. Hematologic markers alone showed limited predictive value. Conclusion: Serum lactate, particularly when combined with COHb, provides strong diagnostic value in predicting HBOT need in CO poisoning. The integration of these readily available biomarkers may improve triage decisions in emergency care.

Background: Accurate burn size and depth assessment at first contact guides fluid resuscitation, referral, and operative planning, yet both tasks show meaningful inter-clinician variability. General-purpose multimodal large language models may offer scalable, image-based decision support in emergency care, but prospective benchmarking against clinicians and a robust reference standard remains limited. Methods: We conducted a prospective, single-centre diagnostic accuracy and agreement study in a tertiary emergency department (22 July–8 September 2025). Consecutive acute burn presentations (< 24 h) were screened; protocol-conformant cases contributed standardized three-view photographs per anatomically distinct burn region. A multimodal large language model generated region-level estimates of total body surface area (TBSA) contribution and burn depth class. Eighteen emergency physicians independently rated the same images and minimal metadata, blinded to model and reference outputs. A three-member expert panel served as the reference standard by consensus. The primary endpoint was non-inferiority of the model versus the physician median for region-level absolute TBSA error relative to the panel, with a pre-specified margin of 3 percentage points, using patient-level cluster bootstrap for inference. Secondary endpoints included TBSA agreement and depth agreement (quadratic-weighted kappa). Results: Of 413 screened presentations, 52 patients were enrolled, yielding 64 analyzable burn region-cases (35 pediatric, 29 adult). The model’s mean absolute TBSA error versus the panel was 1.40 percentage points (median 1.00); 87.5% of cases were within ± 3 percentage points and 98.4% within ± 5. The physician median had a mean absolute error of 0.89 percentage points (median 0.75). The paired non-inferiority analysis met the pre-specified criterion (Hodges–Lehmann median Δ = 0.25; one-sided 95% upper bound = 0.50), indicating the model was non-inferior to physicians for TBSA estimation. In contrast, depth agreement versus the panel was slight for the model (quadratic-weighted kappa 0.14), with systematic underestimation of deeper burns, while physician consensus showed substantially higher agreement (quadratic-weighted kappa 0.65). Conclusions: In this prospective emergency department evaluation, a general-purpose multimodal model achieved non-inferior performance to emergency physicians for region-level TBSA estimation but performed substantially worse for burn depth classification. These findings support a narrowly defined adjunct role for TBSA estimation, while depth-dependent decisions should remain clinician-led and require further method development and external validation.

Background: Large Language Models (LLMs), such as GPT-4o, are increasingly investigated for clinical decision support in emergency medicine. However, their real-world performance in disposition prediction remains insufficiently studied. This study evaluated the diagnostic accuracy of GPT-4o in predicting ED disposition—discharge, ward admission, or ICU admission—in complex emergency respiratory cases requiring pulmonology consultation and chest CT, representing a selective high-acuity subgroup of ED patients. Methods: We conducted a retrospective observational study in a tertiary ED between November 2024 and February 2025. We retrospectively included ED patients with complex respiratory presentations who underwent pulmonology consultation and chest CT, representing a selective high-acuity subgroup rather than the general ED respiratory population. GPT-4o was prompted to predict the most appropriate ED disposition using three progressively enriched input models: Model 1 (age, sex, oxygen saturation, home oxygen therapy, and venous blood gas parameters); Model 2 (Model 1 plus laboratory data); and Model 3 (Model 2 plus chest CT findings). Model performance was assessed using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results: Among the 221 patients included, 69.2% were admitted to the ward, 9.0% to the intensive care unit (ICU), and 21.7% were discharged. For hospital admission prediction, Model 3 demonstrated the highest sensitivity (91.9%) and overall accuracy (76.5%), but the lowest specificity (20.8%). In contrast, for discharge prediction, Model 3 achieved the highest specificity (91.9%) but the lowest sensitivity (20.8%). Numerical improvements were observed across models, but none reached statistical significance (all p > 0.22). Model 1 therefore performed comparably to Models 2–3 while being less complex. Among patients who were discharged despite GPT-4o predicting admission, the 14-day ED re-presentation rates were 23.8% (5/21) for Model 1, 30.0% (9/30) for Model 2, and 28.9% (11/38) for Model 3. Conclusion: GPT-4o demonstrated high sensitivity in identifying ED patients requiring hospital admission, particularly those needing intensive care, when provided with progressively enriched clinical input. However, its low sensitivity for discharge prediction resulted in frequent overtriage, limiting its utility for autonomous decision-making. This proof-of-concept study demonstrates GPT-4o’s capacity to stratify disposition decisions in complex respiratory cases under varying levels of limited input data. However, these findings should be interpreted in light of key limitations, including the selective high-acuity cohort and the absence of vital signs, and require prospective validation before clinical implementation.

Early risk stratification may support emergency department (ED) decision-making for patients hospitalized with heart failure (HF), yet commonly used tools may perform differently across clinically relevant outcomes. We conducted a retrospective observational cohort study using MIMIC-IV (v3.1) linked to MIMIC-IV-ED (2011–2019). Adult ED encounters with HF International Classification of Diseases-9/10 codes from the ED diagnosis table that were linkable to an inpatient admission were included. The primary analytic cohort was restricted to complete-case encounters with sufficient data to compute both Acute Decompensated Heart Failure National Registry (ADHERE) CART and Get With The Guidelines–Heart Failure (GWTG-HF). The primary outcome was 30-day all-cause mortality; secondary outcomes were intensive care unit (ICU) admission within 24 hours after ED disposition, any ICU admission, acute kidney injury (AKI; Kidney Disease Improving Global Outcomes creatinine criteria), and hospital/ICU length of stay. Associations were evaluated using univariable logistic regression. Discrimination was assessed by area under the receiver operating characteristic curve (bootstrap 95% confidence intervals [CI]s) and compared using DeLong test. Calibration was assessed with patient-level 10-fold cross-validated calibration intercept/slope and calibration curves. Among 5508 eligible encounters, 4812 had complete data for both scores and constituted the analytic cohort. Thirty-day mortality occurred in 317/4812 (6.6%). ICU admission within 24 hours after ED disposition occurred in 822/4812 (17.1%), any ICU admission in 1098/4812 (22.8%), and AKI in 1373/4810 (28.5%). GWTG-HF was associated with higher 30-day mortality (odds ratio 2.3 per 10 points, 95% CI 2.1–2.6); ADHERE CART also showed higher odds across strata (group 4 vs 1 odds ratio 5.2, 95% CI 3.1–8.8). Mortality discrimination was higher for GWTG-HF than ADHERE (area under the receiver operating characteristic curve 0.75 vs 0.64; difference 0.10; P < .001), with near-ideal calibration for both. In ED patients hospitalized with HF, GWTG-HF more reliably stratified 30-day mortality risk than ADHERE CART, while both scores showed limited utility for ICU utilization and AKI, supporting outcome-specific and dynamic risk assessment.