Medicina clinica
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Multicenter Study
Classification tree obtained by artificial intelligence for the prediction of heart failure after acute coronary syndromes.
Coronary heart disease is the leading cause of heart failure (HF), and tools are needed to identify patients with a higher probability of developing HF after an acute coronary syndrome (ACS). Artificial intelligence (AI) has proven to be useful in identifying variables related to the development of cardiovascular complications. ⋯ The decision tree model, obtained by AI, identified 8 leading variables capable of predicting HF and generated 15 differentiated clinical patterns with respect to the probability of being hospitalized for HF. An electronic application was created and made available for free.
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Multicenter Study
Utility of applying a diagnostic algorithm in giant cell arteritis based on the level of clinical suspicion.
To reach the diagnosis of giant cell arteritis (GCA), signs, symptoms, laboratory tests, imaging findings, and occasionally anatomopathological results from temporal artery biopsy are evaluated. This study describes the results of an algorithm analysis based on clinical and ultrasound evaluation of patients with suspected GCA, highlighting its diagnostic utility by contrasting its use in different clinical suspicion scenarios. ⋯ In situations of high clinical suspicion, the algorithm provides sufficient information for the diagnosis of GCA if ultrasound is positive. A negative ultrasound is sufficient to rule out the diagnosis in the context of low clinical suspicion. 18-FDG-PET-CT may be useful in patients with high suspicion and negative or indeterminate ultrasound results.