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Objectives: This study presents the cardiology referral model adopted at the University of São Paulo-Hospital das Clínicas complex during the initial period of the coronavirus disease (COVID-19) pandemic, main reasons for requesting a cardiologic evaluation, and clinical profile of and prognostic predictors in patients with COVID-19.
Methods: In this observational study, data of all cardiology referral requests between March 30, 2020 and July 6, 2020 were collected prospectively. A descriptive analysis of the reasons for cardiologic evaluation requests and the most common cardiologic diagnoses was performed. A multivariable model was used to identify independent predictors of in-hospital mortality among patients with COVID-19.
Results: Cardiologic evaluation was requested for 206 patients admitted to the ICHC-COVID. A diagnosis of COVID-19 was confirmed for 180 patients. Cardiologic complications occurred in 77.7% of the patients. Among these, decompensated heart failure was the most common complication (38.8%), followed by myocardial injury (35%), and arrhythmias, especially high ventricular response atrial fibrillation (17.7%). Advanced age, greater need of ventilatory support on admission, and pre-existing heart failure were independently associated with in-hospital mortality.
Conclusions: A hybrid model combining in-person referral with remote discussion and teaching is a viable alternative to overcome COVID-19 limitations. Cardiologic evaluation remains important during the pandemic, as patients with COVID-19 frequently develop cardiovascular complications or decompensation of the underlying heart disease.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8610220 | PMC |
http://dx.doi.org/10.6061/clinics/2021/e3538 | DOI Listing |
Epilepsia
December 2024
Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy.
Objective: This study was undertaken to characterize the clinical and genetic features of patients with 22q11.2 deletion syndrome (22q11.2DS) and generalized epilepsy compared with 22q11.
View Article and Find Full Text PDFBMC Health Serv Res
December 2024
Chair for Artificial Intelligence and Knowledge Systems, University of Würzburg, Würzburg, Germany.
Background: Feeding patients' self-reported medical history into the diagnostic care process may accelerate workflows in clinical routine.
Methods: We prospectively piloted a novel medical history documentation system in a German cardiological outpatient practice and evaluated its feasibility and perceived usefulness. Based on a generic software that allows to record structured information, a customized solution for the cooperating practice was developed and implemented.
Arch Ital Urol Androl
December 2024
Centro Hospitalar e Universitário de Coimbra; Faculty of Medicine, University of Coimbra.
Background And Objectives: Erectile dysfunction (ED) is an independent and strong marker of cardiovascular disease (CVD) risk. The Princeton Consensus aimed to evaluate and manage cardiovascular risk in men with ED and no known cardiovascular disease, focusing on identifying those requiring additional cardiologic work-up. It has recently been updated to the American population demographics, but European recommendations are needed.
View Article and Find Full Text PDFJ Clin Med
December 2024
Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, 02-525 Warsaw, Poland.
This study aimed to evaluate the accuracy of machine learning (ML) techniques in classifying pediatric individuals-cardiological patients, healthy participants, and athletes-based on cardiorespiratory features from short-term static measurements. It also examined the impact of cardiorespiratory coupling (CRC)-related features (from causal and information domains) on the modeling accuracy to identify a preferred cardiorespiratory feature set that could be further explored for specialized tasks, such as monitoring training progress or diagnosing health conditions. We utilized six self-prepared datasets that comprised various subsets of cardiorespiratory parameters and applied several ML algorithms to classify subjects into three distinct groups.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Cardiology Residency, Department of Cardiology, Sincan Training and Research Hospital, Ankara 06930, Turkey.
Artificial intelligence (AI) tools, like ChatGPT, are gaining attention for their potential in supporting clinical decisions. This study evaluates the performance of ChatGPT-4o in acute cardiological cases compared to cardiologists and emergency physicians. Twenty acute cardiological scenarios were used to compare the responses of ChatGPT-4o, cardiologists, and emergency physicians in terms of accuracy, completeness, and response time.
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