9 research outputs found
Quality Improvement Project to Reduce Prescription Errors in Patients Hospitalized due to Cardiovascular Diseases
No full textIntroducción: Los errores de prescripción son un problema frecuente que amenaza la seguridad de los pacientes internados, especialmente en áreas de cuidados críticos.Objetivo: Evaluar la efectividad de un proyecto de mejora de la calidad para reducir errores de prescripción en pacientes internados por patologías de origen cardiovascular.Material y métodos: Se implementó un proyecto de mejora de la calidad destinado a reducir errores de prescripción intrahospitalaria.Los tres componentes principales del proyecto fueron: supervisión obligatoria de las indicaciones, utilización de un software que ordena las indicaciones por sistemas biológicos e implementación de una norma de formato universal deprescripción de medicamentos, que incluyó un diccionario de abreviaturas y de diluciones normalizadas. Con anterioridad a la implementación de estos cambios se midió la cantidad de errores de prescripción semanales, estratificados por área de internación. Se analizó el impacto del proyecto dividiendo las muestras en cuatro períodos consecutivos de 9 semanas cada uno (un período preintervención y tres posintervención) y se comparó luego la cantidad de errores detectados en cada uno de ellos. En cada período se evaluaron de manera aleatoria las indicaciones de 180 pacientes.Resultados: Se analizaron en total 720 prescripciones. La implementación del proyecto de mejora logró reducir la cantidad de errores de manera rápida y sostenida en el tiempo (mediana preintervención de 85, RIC 70-95 y mediana final de 26, RIC 21-37; p = 0,0004).Conclusión: El proyecto de mejora de la calidad implementado permitió reducir significativamente la cantidad de errores de prescripción en pacientes internados por patologías cardiovasculares.Background: Prescription errors are a common problem which threatens hospitalized patients’ safety, particularly in critical care areas. Objective: The aim of the study was to evaluate the effectiveness of a quality improvement project to reduce prescription errors in patients hospitalized due to cardiovascular diseases. Methods: A quality improvement project was implemented to reduce in-hospital prescription errors. The three main components of the project were: mandatory supervision of indications, use of a software program that organizes physicians’ indications by biological systems, and implementation of a rule with universal format for the prescription of medications, including a dictionary of abbreviations and normalized dilutions. Before the implementation of these changes, the number of weakly prescription errors was assessed, stratified by hospitalization area. The impact of the project was analyzed by dividing the samples into four consecutive 9-week periods (one period before the intervention and three periods after the intervention), comparing the number of errors detected in each period. The indications of 180 patients were randomly evaluated in each period. Results: A total of 720 prescriptions were analyzed. The implementation of an improvement project reduced the number of errors rapidly and consistently over time (median of 85 before the intervention, IQR 70-95, and 26 after the intervention, IQR 21-37; p=0.0004). Conclusion: The quality improvement project produced a significant reduction in the number of prescription errors in patients hospitalized due to cardiovascular diseases.Fil: Seoane, Leonardo. Instituto Cardiovascular de Buenos Aires; ArgentinaFil: Benzadón, Mariano. Instituto Cardiovascular de Buenos Aires; ArgentinaFil: Daquarti, Gustavo. Instituto Cardiovascular de Buenos Aires; ArgentinaFil: Mitrione, Soledad. Instituto Cardiovascular de Buenos Aires; ArgentinaFil: Vecchio, Nicolas. Instituto Cardiovascular de Buenos Aires; ArgentinaFil: Ametrano, Clara. Instituto Cardiovascular de Buenos Aires; ArgentinaFil: Furmento, Juan F.. Instituto Cardiovascular de Buenos Aires; ArgentinaFil: Conde, Diego. Instituto Cardiovascular de Buenos Aires; ArgentinaFil: Alves de Lima, Alberto Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Cardiovascular de Buenos Aires; Argentin
Impact of a hybrid decision support system to improve the adherence to implantable cardioverter defibrillator therapy guidelines for primary prevention of sudden cardiac death
No full textCOMPARISON OF CORONARY FLOW RESERVE IN NORMAL PERFUSION PATIENTS WITH AND WITHOUT CORONARY RISK FACTORS USING CADMIUM ZINC TELLURIDE BASED GAMMA CAMERA
No full textAnemia e insuficiência cardíaca com fração de ejeção preservada em pacientes adultos com síndrome cardiorrenal: estudo transversal
No full textIntroduction: Patients hospitalized for heart failure with preserved ejection fraction (HFpEF) have a higher prevalence of anemia than those hospitalized for heart failure with reduced ejection fraction (HFpEF). However, it is unknown if this relationship is maintained in patients with chronic kidney disease, forming cardiorenal syndrome type IV.
Methods: Cross-sectional, multicenter study of patients with chronic kidney disease hospitalized for heart failure. The primary outcome was the difference in hemoglobin concentration. The secondary outcome was the difference in the prevalence of anemia.
Results: 229 patients were enrolled. Hemoglobin concentration was lower in patients with HFpEF compared to patients with HFrEF (11.1±1.8 g/dl vs 12.3±2.0 g/dl; p < 0.0001). . Presenting HFpEF was associated with a lower hemoglobin concentration (β1 = -0.90 g/dl; p = 0.001), after including the use of iron supplements, moderate to severe deterioration of glomerular filtration rate, sex and age. The prevalence of anemia was higher in patients with HFpEF compared to those with HFrEF (72.3% vs 59.8%; p = 0.0462). Belonging to the HFpEF group was not associated with anemia (OR = 1.77; p = 0.078), after including the same covariates. Exploratory, belonging to the HFpEF group was associated with moderate to severe anemia, after including the same covariates.
Conclusion: In patients with type IV cardiorenal syndrome, hemoglobin concentration is lower in patients hospitalized with HFpEF, possibly playing a role in the destabilization of these patients.Introducción: Los pacientes internados por insuficiencia cardíaca con fracción de eyección conservada (IC-FEc) tienen mayor prevalencia de anemia que aquellos internados por insuficiencia cardíaca con fracción de eyección reducida (IC-FEr). Sin embargo, se desconoce si esta relación se mantiene en pacientes con enfermedad renal crónica, conformando el síndrome cardiorrenal tipo IV.
Métodos: Estudio de corte trasversal, multicéntrico, de pacientes con enfermedad renal crónica internados por insuficiencia cardíaca. El desenlace primario fue la diferencia en la concentración de hemoglobina. El desenlace secundario fue la diferencia en la prevalencia de anemia.
Resultados: Se enrolaron 229 pacientes. La concentración de hemoglobina fue menor en pacientes con IC-FEc en relación a los pacientes con IC-FEr (11,1±1,8 g/dl vs 12,3±2,0 g/dl; p < 0,0001). Presentar IC-FEc estaba asociado a una menor concentración de hemoglobina (β1 = -0,90 g/dl; p = 0,001), luego de incluir el uso de suplementos de hierro, el deterioro moderado a grave del filtrado glomerular, el sexo y la edad. La prevalencia de anemia fue mayor en pacientes con IC-FEc en relación a aquellos con IC-FEr (72,3% vs 59,8%; p = 0,0462). Pertenecer al grupo de IC-FEc no se asoció con anemia (OR = 1,77; p = 0,078), luego de incluir las mismas covariables. Exploratoriamente, pertenecer al grupo de IC-FEc se asoció con anemia moderada a grave, luego de incluir las mismas covariables.
Conclusión: En pacientes con síndrome cardiorrenal tipo IV la concentración de hemoglobina es menor en pacientes internados con IC-FEc, posiblemente jugando un rol en la desestabilización de estos pacientes.Introdução: Pacientes hospitalizados por insuficiência cardíaca com fração de ejeção preservada (ICFEp) apresentam maior prevalência de anemia do que aqueles hospitalizados por insuficiência cardíaca com fração de ejeção reduzida (ICFEp). Entretanto, não se sabe se essa relação se mantém em pacientes com doença renal crônica, formando a síndrome cardiorrenal tipo IV.
Métodos: Estudo transversal e multicêntrico de pacientes com doença renal crônica internados por insuficiência cardíaca. O desfecho primário foi a diferença na concentração de hemoglobina. O desfecho secundário foi a diferença na prevalência de anemia.
Resultados: 229 pacientes foram incluídos. A concentração de hemoglobina foi menor em pacientes com ICFEp em comparação com pacientes com ICFEr (11,1±1,8 g/dl vs 12,3±2,0 g/dl; p < 0,0001). Presentar IC-FEc estaba asociado a una menor concentración de hemoglobina (β1 = -0,90 g/dl; p = 0,001), luego de incluir el uso de suplementos de hierro, el deterioro moderado a grave del filtrado glomerular, el sexo y idade. A prevalência de anemia foi maior em pacientes com ICFEp em comparação com aqueles com ICFEr (72,3% vs 59,8%; p = 0,0462). Pertencer ao grupo ICFEP não se associou à anemia (OR = 1,77; p = 0,078), após inclusão das mesmas covariáveis. Exploratório, pertencer ao grupo ICFEP foi associado à anemia moderada a grave, após inclusão das mesmas covariáveis.
Conclusão: Em pacientes com síndrome cardiorrenal tipo IV, a concentração de hemoglobina é menor em pacientes hospitalizados com ICFEP, possivelmente desempenhando um papel na desestabilização desses pacientes
P27 Prevalencia de disfunción microvascular en pacientes con diabetes tipo 1 y 2 con GATED-SPECTnormal
No full textArtificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies
No full textPurpose of ReviewAs machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease.Recent Findings and SummaryThere is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations. These publications still report objective divergence in methods either utilizing statistical machine learning approaches or deep learning with varying architectures, dataset sizes, and performance. Recent efforts have been placed into bringing standardization and quality to the experimentation and application of machine learning-based AI in cardiovascular imaging to generate standards in data harmonization and analysis through AI. Machine learning-based AI offers the possibility to improve risk evaluation in cardiovascular disease through its implementation on cardiac nuclear studies.</p
Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies
No full textPURPOSE OF REVIEW: As machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease. There is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations. These publications still report objective divergence in methods either utilizing statistical machine learning approaches or deep learning with varying architectures, dataset sizes, and performance. Recent efforts have been placed into bringing standardization and quality to the experimentation and application of machine learning-based AI in cardiovascular imaging to generate standards in data harmonization and analysis through AI. Machine learning-based AI offers the possibility to improve risk evaluation in cardiovascular disease through its implementation on cardiac nuclear studies. AI in improving risk evaluation in nuclear cardiology. * Based on the 2019 ESC guidelines.</p
