Articles: emergency-medical-services.
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Ulus Travma Acil Cerrahi Derg · Jul 2023
Observational StudyHow did COVID-19 affect acute urolithiasis? An inner Anatolian experience.
The COVID-19 pandemic has changed the number of patients seeking medical help from the emergency service (ES) with non-COVID complaints, consequencing in postponed presentations of different surgical and medical situations. Acute urinary stone disease is one of these situations and needs to be investigated in terms of the effect of COVID-19 on its presentation to the ES. ⋯ The COVID-19 pandemic resulted in neither sicker nor fewer patients suffering from acute ureteric colic in the ES.
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Multicenter Study
THE ASSOCIATION BETWEEN SYSTOLIC BLOOD PRESSURE AND HEART RATE IN EMERGENCY DEPARTMENT PATIENTS: A MULTICENTER COHORT STUDY.
Guidelines and textbooks assert that tachycardia is an early and reliable sign of hypotension, and an increased heart rate (HR) is believed to be an early warning sign for the development of shock, although this response may change by aging, pain, and stress. ⋯ No association between SBP and HR existed in ED patients of any age category, nor in ED patients who were hospitalized with a suspected infection, even during and after ED treatment. Emergency physicians may be misled by traditional concepts about HR disturbances because tachycardia may be absent in hypotension.
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Journal of neurotrauma · Jul 2023
Multicenter StudyPrediction of Mortality Among Patients with Isolated Traumatic Brain Injury Using Machine Learning Models in Asian Countries: An International Multicenter Cohort Study.
Abstract Traumatic brain injury (TBI) is a significant healthcare concern in several countries, accounting for a major burden of morbidity, mortality, disability, and socioeconomic losses. Although conventional prognostic models for patients with TBI have been validated, their performance has been limited. Therefore, we aimed to construct machine learning (ML) models to predict the clinical outcomes in adult patients with isolated TBI in Asian countries. ⋯ Among the tested models, the gradient-boosted decision tree showed the best performance (AUPRC, 0.746 [0.700-0.789]; AUROC, 0.940 [0.929-0.952]). The most powerful contributors to model prediction were the Glasgow Coma Scale, O2 saturation, transfusion, systolic and diastolic blood pressure, body temperature, and age. Our study suggests that ML techniques might perform better than conventional multi-variate models in predicting the outcomes among adult patients with isolated moderate and severe TBI.