FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2606256891> ?p ?o ?g. }

• W2606256891 endingPage "581" @default.
• W2606256891 startingPage "580" @default.
• W2606256891 abstract "Journal of Palliative MedicineVol. 20, No. 6 Letters to the EditorOpen AccessNot Illness Trajectory but Bayesian-Estimated Rate Model Should Be Appropriately Explained When Discussing Palliative Care in Heart DiseaseAtsushi Mizuno, Saran Yoshida, and Kuniyoshi HayashiAtsushi MizunoDepartment of Cardiology, St. Luke's International Hospital, Tokyo, Japan.Search for more papers by this author, Saran YoshidaGraduate School of Education, Tohoku University, Sendai, Miyagi, Japan.Search for more papers by this author, and Kuniyoshi HayashiCenter for Clinical Epidemiology, St. Luke's International University, Tokyo, Japan.Search for more papers by this authorPublished Online:1 Jun 2017https://doi.org/10.1089/jpm.2017.0074AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail Dear Editor:Despite the recent focus on palliative care for noncancer patients, many articles have highlighted the challenges in administering palliative care for patients with heart disease. Usually, the major challenge is the uncertain and unpredictable illness trajectory of heart disease compared with that of cancer. Predicting disease prognosis is essential to plan palliative care. Unfortunately, there is no suitable model to predict the probable disease trajectory in heart disease. Here, we advocate that the Bayesian-estimated rate model might be preferable over the life expectancy and trajectory model, in planning palliative care for patients of heart disease (Fig. 1).FIG. 1. The scheme of palliative care phase and each palliative care term by considering Bayesian-estimated rate model; “Palliative care period,” “End of life,” “Terminally ill,” “Terminal care period,” “Actively dying.” Acute and chronic phase/illness could be interchangeable at estimated rates.Bayesian-estimated rate model was found suitable to predict the outcome in heart disease. Although several trajectories using the life expectancy model advocated by Hupcey et al. could predict that the patients had a life-threatening disease, they could not predict the episodes such as sudden cardiac death and acute deteriorations.1 Hence, many cardiologists were of the opinion that the life expectancy model based on trajectory could not be used in clinical practice, especially when planning palliative care for patients of heart disease. Considering sudden death and acute deterioration as important outcomes due to the nature of heart disease, mortality rate such as in-hospital mortality and 30 days mortality might be more precise and useful for prognosis. Furthermore, many risk models, such as “American Heart Association Get With The Guidelines Score” for heart failure and “The Thrombolysis in Myocardial Infarction (TIMI) risk score” for myocardial infarction, have been developed and well validated to predict the mortality rate.2Using Bayesian-estimated rate model, we can apply the prospect theory to understand the differences between patients and medical teams, while making decisions such as whether life-sustaining therapy should be initiated in patients with the potential risk of brain death during acute deterioration settings. Patients often tend to overreact to low probability events but underreact to high probabilities, which is sometimes difficult for the physician to understand. Furthermore, as Verma et al. mentioned, the prospect theory might help us offer appropriate reference settings and frame them appropriately, which could be useful to draw up care plans in advance.3 Numerical mortality rate calculation can help in predicting future risk appropriately by estimating the patients’ value. Contrarily, the trajectory and life-expectancy model does not help the patient to estimate the risk and make an appropriate choice.Of course, Bayesian-estimated rate model can be difficult to understand not only for the patients but also for physicians, and to imagine the outcome objectively, much like a Monty Hall problem. However, the recent progress of artificial intelligence and data analysis will enable calculation of the precise numerical value to predict prognosis. We need to make a paradigm shift in outcome prediction and explaining the prognosis to patients with heart disease.We recommend using the mortality rate models, especially when considering and planning palliative care for patients with heart disease.AcknowledgmentThis research is supported by the “Practical Research Project for Life-Style related Diseases including Cardiovascular Diseases and Diabetes Mellitus” from Japan Agency for Medical Research and Development, AMED.References1 Hupcey JE, Penrod J, Fenstermacher K: A model of palliative care for heart failure. Am J Hosp Palliat Med 2009;26:399–404. Crossref, Medline, Google Scholar2 Yancy CW, Jessup M, Bozkurt B, et al.: ACCF/AHA guideline for the management of heart failure: A report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013;62:e147–e239. Crossref, Medline, Google Scholar3 Verma AA, Razak F, Detsky AS: Understanding choice: Why physicians should learn prospect theory. JAMA 2014;311:571–572. Crossref, Medline, Google ScholarFiguresReferencesRelatedDetails Volume 20Issue 6Jun 2017 InformationCopyright 2017, Mary Ann Liebert, Inc.To cite this article:Atsushi Mizuno, Saran Yoshida, and Kuniyoshi Hayashi.Not Illness Trajectory but Bayesian-Estimated Rate Model Should Be Appropriately Explained When Discussing Palliative Care in Heart Disease.Journal of Palliative Medicine.Jun 2017.580-581.http://doi.org/10.1089/jpm.2017.0074creative commons licensePublished in Volume: 20 Issue 6: June 1, 2017Online Ahead of Print:March 15, 2017PDF download" @default.
• W2606256891 created "2017-04-28" @default.
• W2606256891 creator A5002170800 @default.
• W2606256891 creator A5025839598 @default.
• W2606256891 creator A5090764961 @default.
• W2606256891 date "2017-06-01" @default.
• W2606256891 modified "2023-10-16" @default.
• W2606256891 title "Not Illness Trajectory but Bayesian-Estimated Rate Model Should Be Appropriately Explained When Discussing Palliative Care in Heart Disease" @default.
• W2606256891 cites W2036825844 @default.
• W2606256891 cites W2042473813 @default.
• W2606256891 cites W2119340816 @default.
• W2606256891 doi "https://doi.org/10.1089/jpm.2017.0074" @default.
• W2606256891 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5455609" @default.
• W2606256891 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28296558" @default.
• W2606256891 hasPublicationYear "2017" @default.
• W2606256891 type Work @default.
• W2606256891 sameAs 2606256891 @default.
• W2606256891 citedByCount "0" @default.
• W2606256891 crossrefType "journal-article" @default.
• W2606256891 hasAuthorship W2606256891A5002170800 @default.
• W2606256891 hasAuthorship W2606256891A5025839598 @default.
• W2606256891 hasAuthorship W2606256891A5090764961 @default.
• W2606256891 hasBestOaLocation W26062568911 @default.
• W2606256891 hasConcept C126322002 @default.
• W2606256891 hasConcept C133925201 @default.
• W2606256891 hasConcept C159110408 @default.
• W2606256891 hasConcept C177713679 @default.
• W2606256891 hasConcept C2779134260 @default.
• W2606256891 hasConcept C2780074459 @default.
• W2606256891 hasConcept C2908647359 @default.
• W2606256891 hasConcept C2994186709 @default.
• W2606256891 hasConcept C512399662 @default.
• W2606256891 hasConcept C71924100 @default.
• W2606256891 hasConcept C99454951 @default.
• W2606256891 hasConceptScore W2606256891C126322002 @default.
• W2606256891 hasConceptScore W2606256891C133925201 @default.
• W2606256891 hasConceptScore W2606256891C159110408 @default.
• W2606256891 hasConceptScore W2606256891C177713679 @default.
• W2606256891 hasConceptScore W2606256891C2779134260 @default.
• W2606256891 hasConceptScore W2606256891C2780074459 @default.
• W2606256891 hasConceptScore W2606256891C2908647359 @default.
• W2606256891 hasConceptScore W2606256891C2994186709 @default.
• W2606256891 hasConceptScore W2606256891C512399662 @default.
• W2606256891 hasConceptScore W2606256891C71924100 @default.
• W2606256891 hasConceptScore W2606256891C99454951 @default.
• W2606256891 hasIssue "6" @default.
• W2606256891 hasLocation W26062568911 @default.
• W2606256891 hasLocation W26062568912 @default.
• W2606256891 hasLocation W26062568913 @default.
• W2606256891 hasLocation W26062568914 @default.
• W2606256891 hasOpenAccess W2606256891 @default.
• W2606256891 hasPrimaryLocation W26062568911 @default.
• W2606256891 hasRelatedWork W144772298 @default.
• W2606256891 hasRelatedWork W173034263 @default.
• W2606256891 hasRelatedWork W2051348912 @default.
• W2606256891 hasRelatedWork W2056495982 @default.
• W2606256891 hasRelatedWork W2064722072 @default.
• W2606256891 hasRelatedWork W2082482010 @default.
• W2606256891 hasRelatedWork W2765379856 @default.
• W2606256891 hasRelatedWork W2948262314 @default.
• W2606256891 hasRelatedWork W3048139975 @default.
• W2606256891 hasRelatedWork W4214641333 @default.
• W2606256891 hasVolume "20" @default.
• W2606256891 isParatext "false" @default.
• W2606256891 isRetracted "false" @default.
• W2606256891 magId "2606256891" @default.
• W2606256891 workType "article" @default.