scholarly journals IMPACT OF CMR ON MANAGEMENT AND CLINICAL DECISION-MAKING IN HEART FAILURE PATIENTS

2013 ◽  
Vol 61 (10) ◽  
pp. E826 ◽  
Author(s):  
Siddique Abbasi ◽  
Andrew Ertel ◽  
Ravi Shah ◽  
Tomas Neilan ◽  
Bobby Heydari ◽  
...  
Keyword(s):  
Heart Failure ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Heart Failure Patients

scholarly journals Effects of age on aerobic capacity in heart failure patients under beta-blocker therapy: Possible impact in clinical decision-making?

2013 ◽  
Vol 20 (6) ◽  
pp. 655-661 ◽  
Author(s):  
Emmanuel Gomes Ciolac ◽  
Edimar Alcides Bocchi ◽  
Miguel Morita Fernandes da Silva ◽  
Aline Cristina Tavares ◽  
Iram Soares Teixeira-Neto ◽  
...  
Keyword(s):  
Heart Failure ◽  
Decision Making ◽  
Aerobic Capacity ◽  
Beta Blocker ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Blocker Therapy ◽  
Heart Failure Patients ◽  
Beta Blocker Therapy

Contemporary Heart Failure Treatment Based on Improved Knowledge and Personalized Care of Comorbidities

2019 ◽  
pp. 1565-1579
Author(s):  
Kostas Giokas ◽  
Charalampos Tsirmpas ◽  
Athanasios Anastasiou ◽  
Dimitra Iliopoulou ◽  
Vassilia Costarides ◽  
...  
Keyword(s):  
Heart Failure ◽  
Decision Making ◽  
Chronic Diseases ◽  
Clinical Decision Making ◽  
Medical Condition ◽  
Collective Intelligence ◽  
Clinical Decision ◽  
Mortality And Morbidity ◽  
Personalized Care ◽  
Care Approach

Chronic diseases are the leading cause of mortality and morbidity. A significant contribution to the burden of chronic diseases is the concurrence of co-morbidities. Heart failure (HF) is a complex, chronic medical condition frequently associated with co-morbidities. The current care approach for HF patients with co-morbidities is neither capable to deliver personalised care nor to halt the on-going increase of its socio-economic burden. Our approach aims to improve the complete care process for HF patients and related co-morbidities to improve outcome and quality of life. This will be achieved by the proposed standardised yet personalised patient-oriented ICT system that supports evidence-based clinical decision making as well as interaction and communication between all stakeholders with focus on the patients and their relatives to improve self-management. We propose that such a system should be build upon a novel European-wide data standard for clinical input and outcome and that it should facilitate decision making and outcome tracking by new collective intelligence algorithms.

scholarly journals Clinical decision-making at the end of life: a mixed-methods study

2018 ◽  
Vol 10 (3) ◽  
pp. e26-e26 ◽  
Author(s):  
Paul Taylor ◽  
Miriam J Johnson ◽  
Dawn Wendy Dowding
Keyword(s):  
Heart Failure ◽  
Decision Making ◽  
Mixed Methods ◽  
End Of Life ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Study Groups ◽  
Mixed Methods Study ◽  
Semistructured Interview ◽  
The North

ObjectivesTo improve the ability of clinical staff to recognise end of life in hospital inpatients dying as a result of cancer and heart failure, and to generate new hypotheses for further research.MethodsThis mixed-methods study used decision theory as a theoretical basis. It involved a parallel databases-convergent design, incorporating findings from previously published research, with equal priority to study groups and synthesis by triangulation. The individual arms were (1) a retrospective cohort study of 102 patients with cancer and 81 patients with heart failure in an acute trust in the North of England, and(2) a semistructured interview study of 19 healthcare professionals caring for the same patient groups.ResultsThe synthesis of findings demonstrated areas of agreement, partial agreement, silence and dissonance when comparing the cohort findings with the interview findings. Trajectories of change are identified as associated with poor prognosis in both approaches, but based on different parameters. Management of patients has a significant impact on decision-making. The decision process requires repeated, iterative assessments and may benefit from a multidisciplinary approach. Uncertainty is a defining characteristic of the overall process, and objective parameters only have a limited role in predicting end of life.ConclusionsThe role of uncertainty is important as a trigger for discussions and a defined stage in a patient’s illness journey. This is consistent with current approaches to recognising irreversible deterioration in those with serious illness. This study contributes ongoing evidence that these concepts are vital for decision-making.

Abstract 112: Targeted Metabolomics Profiling of Heart Failure Patients Undergoing Mechanical Circulatory Support

2014 ◽  
Vol 115 (suppl_1) ◽  
Author(s):  
X'avia Chan ◽  
J.H. Howard Choi ◽  
Chelsea J.-T. Ju ◽  
Wei Wang ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Heart Failure ◽  
Functional Groups ◽  
Mechanical Circulatory Support ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Circulatory Support ◽  
Surgical Interventions ◽  
Heart Failure Patients ◽  
Metabolic Marker ◽  
Metabolic Fingerprint

Metabolomics investigations hold promise for the characterization of small molecules, metabolites, which govern the ultimate manifestation of cardiac phenotypes. In this study, we employed a mass spectrometry-based metabolomics approach to identify metabolic marker(s), which dynamically reflect the cardiac performance of heart failure patients amid the implantation of mechanical circulatory support. Using the MRM-based and triple quadrupole technology platform, we have quantified 266 metabolites native to human plasma and collected from thirteen heart failure patients. The temporal profile of these metabolites was sampled from 1 day prior to the implantation of mechanical circulatory support, as well as 1-, 3-, 5-, and 7-day following their surgical interventions. We identified subgroups of these metabolites with coordinated behaviors that are interesting to their diseased phenotypes. In a pair-wise correlation analysis, 36.8% (98 out of 266) of metabolites were significantly correlated. Intriguingly, majority of which (65 out of 98) are representing the functional groups of phosphatidylcholines; several of them are known to have close associations with the pathogenesis of cardiovascular diseases. In addition, there are 33 metabolites contributing to multiple functional groups, including twelve of them belong to sphingomyelines, ten of them in the family of lysophosphatidylcholines, eight amino acids (Gln, Ser, Ala, His, Lys, Gly, Thr, and Arg), as well as three fatty acids (eicosapentaenoic acid, pentadecenoic acid, and heptadecenoic acid). The behaviors of these 266 metabolites have constituted individualized metabolic fingerprints. Delineation of the intrinsic relationships among alterations in distinct metabolite groups and their reflected cardiac function will enable us to identify new metabolic markers aiding stratification and/or prediction on the clinical outcome of each individual patient undergoing the treatment of mechanical circulatory support. This personalized metabolic fingerprint will offer unique prognostic utilities, supporting clinical decision-making process to deliver intervention that is most effective and beneficial to an individual.

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