Internal and emergency medicine
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This prospective cohort enrolled all patients above 16 years of age presenting to the in the emergency department (ED) for a reported syncope was designed to test the accuracy of a point-of-care ultrasound (POCUS) integrated approach in risk stratification. The emergency physician responsible for the patient care was asked to classify the syncope risk after the initial clinical assessment and after performing POCUS evaluation. All risk group definitions were based on the 2018 European Society of Cardiology guidelines. ⋯ Positive and negative likelihood ratios were 1.73 (95% CI 0.87-3.44) and 0.84 (95% CI 0.62-1.12) for the clinical evaluation, and 5.93 (95% CI 2.83-12.5) and 0.63 (95% CI 0.45-0.9) for the POCUS-integrated evaluation. The POCUS-integrated approach would reduce the diagnostic error of the clinical evaluation by 4.5 cases/100 patients. This cohort study suggested that the integration of the clinical assessment with POCUS results in patients presenting to the ED for non-high-risk syncope may increase the accuracy of predicting the risk of SFSR outcomes and the usefulness of the clinical assessment alone.
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Multicenter Study
Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.
Artificial Intelligence and machine learning (ML) methods are promising for risk-stratification, but the added benefit over traditional statistical methods remains unclear. We compared predictive models developed using machine learning (ML) methods to the Canadian Syncope Risk Score (CSRS), a risk-tool developed with logistic regression for predicting serious adverse events (SAE) after emergency department (ED) disposition for syncope. We used the prospective multicenter cohort data collected for CSRS development at 11 Canadian EDs over an 8-year period to develop four ML models to predict 30-day SAE (death, arrhythmias, MI, structural heart disease, pulmonary embolism, hemorrhage) after ED disposition. ⋯ The AUCs and calibration slopes for the ML models and CSRS were similar. Two ML models used fewer predictors than the CSRS but matched its performance. Overall, the ML models matched the CSRS in performance, with some models using fewer predictors.
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Cognitive biases are systematic cognitive distortions, which can affect clinical reasoning. The aim of this study was to unravel the most common cognitive biases encountered in in the peculiar context of the COVID-19 pandemic. Case study research design. ⋯ The pandemic context is a breeding ground for the emergence of cognitive biases, which can influence clinical reasoning and lead to errors. Awareness of these cognitive mechanisms could potentially reduce biases and improve clinical reasoning. Moreover, the analysis of cognitive biases can offer an insight on the functioning of the clinical reasoning process in the midst of the pandemic crisis.
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Robust evidence of whether vitamin D deficiency is associated with COVID-19 infection and its severity is still lacking. The aim of the study was to evaluate the association between vitamin D levels and the risks of SARS-CoV-2 infection and severe disease in those infected. A retrospective study was carried out among members of Clalit Health Services (CHS), the largest healthcare organization in Israel, between March 1 and October 31, 2020. ⋯ An inverse correlation was demonstrated between the level of vitamin D and the risks of SARS-CoV-2 infection and of severe disease in those infected. Patients with very low vitamin D levels (< 30 nmol/L) had the highest risks for SARS-CoV-2 infection and also for severe COVID-19 when infected-OR 1.246 [95% CI 1.210-1.304] and 1.513 [95% CI 1.230-1.861], respectively. In this large observational population study, we show a significant association between vitamin D deficiency and the risks of SARS-CoV-2 infection and of severe disease in those infected.