A collaborative clinical simulation model for the development of competencies by medical students

Herein, we present a new collaborative clinical simulation (CCS) model for the development of medical competencies by medical students. The model is a comprehensive compendium of published considerations and recommendations on clinical simulation (CS) and computer-supported collaborative learning (CSCL). Currently, there are no educational models combining CS and CSCL. The CCS model was designed for the acquisition and assessment of clinical competencies; working collaboratively and supported by technology, small groups of medical students independently design and perform simulated cases. The model includes four phases in which the learning objectives, short case scenarios, materials, indices, and the clinical simulation are designed, monitored, rated and debriefed.Sin financiación2.450 JCR (2017) Q1, 8/41 Education, Scientific Disciplines; Q2, 31/94 Health Care Sciences & ServicesUE

Novel deep learning method for coronary artery tortuosity detection through coronary angiography

Abstract Coronary artery tortuosity is usually an undetected condition in patients undergoing coronary angiography. This condition requires a longer examination by the specialist to be detected. Yet, detailed knowledge of the morphology of coronary arteries is essential for planning any interventional treatment, such as stenting. We aimed to analyze coronary artery tortuosity in coronary angiography with artificial intelligence techniques to develop an algorithm capable of automatically detecting this condition in patients. This work uses deep learning techniques, in particular, convolutional neural networks, to classify patients into tortuous or non-tortuous based on their coronary angiography. The developed model was trained both on left (Spider) and right (45°/0°) coronary angiographies following a fivefold cross-validation procedure. A total of 658 coronary angiographies were included. Experimental results demonstrated satisfactory performance of our image-based tortuosity detection system, with a test accuracy of (87 ± 6)%. The deep learning model had a mean area under the curve of 0.96 ± 0.03 over the test sets. The sensitivity, specificity, positive predictive values, and negative predictive values of the model for detecting coronary artery tortuosity were (87 ± 10)%, (88 ± 10)%, (89 ± 8)%, and (88 ± 9)%, respectively. Deep learning convolutional neural networks were found to have comparable sensitivity and specificity with independent experts’ radiological visual examination for detecting coronary artery tortuosity for a conservative threshold of 0.5. These findings have promising applications in the field of cardiology and medical imaging

Association of kidney disease, potassium, and cardiovascular risk factor prevalence with coronary arteriosclerotic burden, by sex

The present study aimed to determine the relationship between the prevalence of cardiovascular risk factors and the number and severity of coronary artery atherosclerotic lesions obtained by coronary angiography. We reviewed and analyzed 1642 records from consecutive patients at the Catheter Laboratory of Talca Regional Hospital in Chile between March 2018 and May 2019. Patients were stratified according to the presence and severity of atherosclerotic lesions: 632 (38.5%) had no lesions or <30% stenosis and 1010 (61.5%) had at least one coronary atherosclerotic lesion with ≥30% stenosis (CALS-30). CALS-30 was more frequent in males, smokers, and patients with diabetes and/or hypertension (all p-values < 0.02). Serum potassium, glycaemia, creatinine and glomerular filtration rates were also associated with CALS-30 (all p-values < 0.01) in males. The age and the proportion of males with CALS-30 increased with the number of risk factors (p-values for trends < 0.001). Our results showed a stronger association between the accumulation of risk factors and CALS-30 in women than in men. Serum potassium levels were inversely associated with CALS-30 in men but not in women