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Global Disaster Alert and Coordination System (GDACS) alerts are widely used after earthquakes, yet their clinical relevance is uncertain. We performed a retrospective, global, event-level study spanning 2020–2024. To avoid double counting, alerts were clustered into country-bounded representatives using a 48-hour gap, retaining the alert with the highest GDACS score (n = 85; Red = 17, Orange = 68). Primary outcomes were reported deaths and field-hospital deployment. Associations used Spearman correlation; deployment was modeled with Firth logistic regression. Sensitivity analyses used alternative deployment definitions and composite windows. The GDACS score correlated with deaths (ρ = 0.522, p = 3.0 × 10 ⁻ ⁷). Field-hospital deployment occurred in 52.9% of Red events and 0% of Orange events. The GDACS score strongly predicted deployment (OR=42.7, 95% CI 4.7–385.7), with AUC = 0.98 and Brier = 0.034. An exploratory exposure-normalized subset where GDACS reported population “within 100 km” (n = 19) showed directionally consistent results (ρ = 0.50, p = 0.029).GDACS metrics provide early, scalable indicators for surge planning, but are hazard- and exposure-centric and cannot capture mediators such as collapse dynamics or health-system resilience. Treating GDACS as a first-layer signal, complemented by subnational exposure and rapid damage assessment, can support more timely, evidence-based medical response after major earthquakes.

Heart failure (HF) has become a public healthcare concern with significant costs to countries because of the aging world population. Acute heart failure (AHF) is a common condition faced frequently in emergency departments, and patients often present to hospitals with complaints of breathlessness. The patient must be evaluated with anamnesis, physical examination, blood, and imaging results to diagnose AHF. Brain natriuretic peptide (BNP) is a widely accepted biomarker for the diagnosis of HF. The files of the patients who applied to the emergency department with complaints of breathlessness were scanned, and BNP and urinary density (UD) levels were evaluated for the diagnosis of HF in patients. The results support that BNP is an effective biomarker in AHF, as is widely accepted. When the correlation between BNP and UD measurements was examined in the present study, a negative correlation was detected between the parameters. The results also suggested that low UD values may help diagnose AHF. If similar results are obtained in prospective multicenter studies with the participation of more patients, UD value can be used as a biomarker for the diagnosis of AHF.

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.

BACKGROUND: Early detection of hemorrhagic shock and the need for blood product replacement in trauma patients is crucial. The present study aimed to evaluate the effectiveness of peripheral perfusion index (PPI) measurements in determining the severity of hemorrhagic shock and predicting the need for blood product replacement in trauma patients. METHODS: A total of 43 patients who presented to the emergency department due to trauma and were diagnosed with hemorrhagic shock according to the Advanced Trauma Life Support (ATLS) guidelines were included in this prospective cross-sectional study. Demographic characteristics, vital signs, laboratory parameters, PPI values, ATLS shock classification, and blood product replacement status were evaluated. RESULTS: The median age of the patients was 35 years (range: 18-94), and 12 (27.9%) were female. The median PPI value was 1.30 (range: 0.15-10.00), and 23 (53.5%) patients received blood product replacement. PPI values were found to be statistically significantly lower in patients who received blood product replacement compared to those who did not. The PPI values of ATLS Class I patients were statistically significantly higher than those of ATLS Class III and IV patients. Among patients in the Class II shock group, the PPI value was 0.75 (range: 0.30-4.70) in patients who received blood product replacement and 2.20 (range: 1.10-10.00) in those who did not, indicating a statistically significant difference between the groups. According to the receiver operating characteristic curve analysis performed to determine the effectiveness of PPI measurement in predicting the need for blood product replacement in Class II shock patients, the cut-off value was 1.2. CONCLUSION: The findings of this study demonstrated that PPI values were lower in patients who required blood product replacement due to traumatic shock compared to those who did not. These results suggest that PPI measurements may serve as an effective assessment method for predicting the need for blood product replacement, particularly in patients in the Class II shock group according to the ATLS shock classification. Keywords: Trauma; hemorrhagic shock; peripheral perfusion index. AMAC: Travma hastalarinda hemorajik sok varligi ve kan urunu replasmani ihtiyacinin erken tespiti hayati onem tasimaktadir. Calismamizda, periferik perfuzyon indeksi (PPI) olcumlerinin, travma hastalarinda hemorajik sok siddetinin belirlenmesinde ve kan urunu replasmani ihtiyacinin ongorulmesindeki basarisini degerlendirmeyi amacladik. GEREC VE YONTEM: Prospektif kesitsel nitelikte olan calismamizda acil servise travma nedeniyle basvurmus, travma sonrasinda Advanced Trauma Life Support (ATLS) kilavuzuna gore hemorajik sok tanisi konulan 43 hasta dahil edildi. Hastalarin demografik bilgileri, vital parametreleri, laboratuvar degerleri, PPI degerleri, ATLS sok evreleri, kan urunu replasmani yapilma durumlari degerlendirildi. BULGULAR: Hastanin ortanca yasi 35 (18-94) saptandi ve 12'si (%27.9) kadindi. PPI ortanca degeri 1.30 (0.15-10.00) saptandi, 23 (%53.5) hastaya kan urunu replasmani yapildigi tespit edildi. Kan urunu replasmani yapilan hastalarda, yapilmayan hastalara gore PPI degerleri istatistiksel olarak anlamli sekilde daha dusuk bulundu. Evre I soktaki hasta grubunun PPI degerlerinin evre III ve IV soktaki hastalarin PPI degerlerine gore istatistiksel olarak anlamli sekilde daha yuksek seyrettigi tespit edildi. Evre II sok grubundaki hastalarin, kan urunu replasmani yapilmasi durumlari ve PPI degerleri incelendiginde, kan urunu replasmani yapilan grupta PPI degeri 0.75 (0.30-4.70), yapilmayan grupta PPI degeri 2.20 (1.10-10.00) saptandi ve gruplar arasinda istatistiksel olarak anlamli fark bulundu. Evre II sok grubundaki hastalarda PPI olcumunun kan urunu replasmani ihtiyacini tahmin etmedeki gucunun belirlenmesi amaciyla yapilan ROC analizine gore kestirim degeri (cut-off) 1.2 saptandi. SONUC: Calismamiz sonucunda PPI degerlerinin travmatik sok nedeniyle kan urunu replasmani ihtiyaci bulunan hastalarda, kan urunu replasmani ihtiyaci bulunmayan hastalara gore daha dusuk seyrettigini saptadik. Ozellikle ATLS sok siniflamasina gore evre II sok grubunda bulunan hastalarda, PPI olcumlerinin kan urunu replasmani ihtiyacini ongormede guclu bir degerlendirme yontemi olabilecegini belirledik. Anahtar sozcukler: Travma; hemorajik sok; periferik perfuzyon indeksi.

Background The diagnostic utility of the D-dimer/pCO₂ ratio for pulmonary embolism (PE) risk stratification has not been fully established. This study evaluated its diagnostic performance among emergency department patients with positive age-adjusted D-dimer results undergoing computed tomography pulmonary angiography (CTPA). Methods This retrospective diagnostic accuracy study included 698 adult patients with positive age-adjusted D-dimer results, venous blood gas (VBG) pCO₂ measurements, and definitive CTPA interpretation. The D-dimer/pCO₂ ratio was calculated, and receiver operating characteristic (ROC) analysis was performed. Optimal and exploratory thresholds were assessed for overall PE detection and for excluding central PE. Robustness was tested using bootstrap validation and subgroup AUC comparisons. Decision curve analysis (DCA) was applied to evaluate clinical utility. Results PE was confirmed in 90 patients (12.9%). The ratio demonstrated good discrimination (AUC: 0.811, 95% CI: 0.775–0.847). At the optimal cut-off (44.91), sensitivity was 82.2% and specificity 71.1%, with a negative predictive value (NPV) of 96.4%. A lower cut-off (18.1) identified 91 patients with no observed PE (0/91; 95% CI upper bound for false negatives ≈ 4.0%). A higher threshold (61.25) identified 515 patients below this value, among whom no central PE was observed (0/515; 95% CI upper bound ≈ 0.7%). Discriminative ability was preserved across age groups (AUC range: 0.737–0.836). DCA showed modest, range-specific net benefit for incorporating the ratio within a low-to-intermediate threshold band. Conclusion In D-dimer–positive ED patients already being considered for CTPA, the D-dimer/pCO₂ ratio is an adjunctive imaging triage indicator rather than a stand-alone test and may help inform the imaging workflow in this defined context. These findings should not be extrapolated to D-dimer–negative patients or those with very high pretest probability.