scholarly journals An evidence-based data science perspective on the prediction of heart failure readmissions

2021 ◽  
Author(s):  
Kenneth John Locey ◽  
Thomas A. Webb ◽  
Bala Hota
Keyword(s):  
Heart Failure ◽  
Data Science ◽  
Common Factors ◽  
Roc Curves ◽  
Sampling Theory ◽  
Evidence Based ◽  
Predictive Success ◽  
Heart Failure Readmissions ◽  
The Many ◽  
Alternative Metrics

The prevention of unplanned 30-day readmissions of patients discharged with a diagnosis of heart failure (HF) remains a profound challenge among hospital enterprises. Despite the many models and indices developed to predict which HF patients will readmit for any unplanned cause within 30 days, predictive success has been meager. Using simulations of HF readmission models and the diagnostics most often used to evaluate them (C-statistics, ROC curves), we demonstrate common factors that have contributed to the lack of predictive success among studies. We reveal a greater need for precision and alternative metrics such as partial C-statistics and precision-recall curves and demonstrate via simulations how those tools can be used to better gauge predictive success. We suggest how studies can improve their applicability to hospitals and call for a greater understanding of the uncertainty underlying 30-day all-cause HF readmission. Finally, using insights from sampling theory, we suggest a novel uncertainty-based perspective for predicting readmissions and non-readmissions.

Abstract 5961: Defining An Evidence-Based Cutpoint for Medication Adherence in Heart Failure

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Jia-Rong Wu ◽  
Terry A Lennie ◽  
Marla J De Jong ◽  
Mary Kay Rayens ◽  
Misook L Chung ◽  
...  
Keyword(s):  
Heart Failure ◽  
Medication Adherence ◽  
Cox Regression ◽  
Enzyme Inhibitor ◽  
Roc Curves ◽  
Evidence Based ◽  
List Type ◽  
Medication Regimen ◽  
Event Free Survival ◽  
Free Survival

Patients with heart failure (HF) are required to take multiple medications. Long-term adherence to the HF medication regimen is necessary in order to achieve better patient outcomes. Despite the importance of adherence, clinically relevant cutpoints for distinguishing the level of adherence associated with outcomes are unknown. The purpose of this study was to use outcomes (i.e., event-free) as a criterion to determine the level of medication adherence required to achieve the best clinical outcomes. The specific aim was to determine the cutpoint above which there is a positive relationship between level of medication adherence and outcomes. This was a longitudinal study of 135 patients with HF. Medication adherence was measured using a valid and objective measure, the Medication Event Monitoring System (MEMS). Two indicators of adherence were assessed by the MEMS: dose-count, percentage of prescribed doses taken and dose-days, percentage of days correct number of doses taken. Patients were followed up to 3.5 years to collect data on outcomes. A series of Kaplan-Meier plots with log-rank tests, Cox-survival analyses, and receiver operating characteristic (ROC) curves were assessed comparing event-free survival in patients divided at one point incremental cutpoints. Event-free survival was significantly better when the prescribed number of doses taken [dose-count] or the correct dose [dose-day]) was ≥ 88%. This level was confirmed in a Cox regression model controlling for age, gender, ejection fraction, NYHA, comorbidity, angiotensin-converting enzyme inhibitor use, and beta-blocker use. ROC curves showed that adherence rates above 88 – 89% produced the optimal combination of sensitivity and specificity with respect to predicting better event-free survival. With this as the adherence cutpoint, patients in the nonadherent group were 2.2 times (by dose-count) to 3.2 times (by dose-day) more likely to experience a first event ( p = .021 and .002, respectively), compared to patients in the adherent group. The results of this study provide clinicians and researchers with an evidence-based recommendation about the level of adherence needed to achieve the best outcomes.

Evidence-based management of arrhythmogenic right ventricular cardiomyopathy in pregnancy

2020 ◽  
Author(s):  
Jagjit Khosla ◽  
Reshma Golamari ◽  
Alice Cai ◽  
Jamal Benson ◽  
Wilbert S Aronow ◽  
...  
Keyword(s):  
Heart Failure ◽  
Right Ventricular ◽  
Retrospective Studies ◽  
Arrhythmogenic Right Ventricular Cardiomyopathy ◽  
Genetic Disorder ◽  
Ventricular Myocardium ◽  
Evidence Based ◽  
Ventricular Cardiomyopathy ◽  
Genes Encoding ◽  
In Pregnancy

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic disorder resulting in fibrofatty replacement of the myocardium. Genetic mutations in genes encoding for desmosome proteins result in a ventricular myocardium prone to arrhythmias and heart failure. Although ARVC is known for a few decades, most of the outcomes in pregnancy are reported recently. Pregnancy leads to significant physiological changes with excess mechanical stress on the myocardium. All the retrospective studies suggest that pregnancy is well tolerated in these patients despite the high risk of arrhythmias and heart failure. Our review focuses on the most up-to-date evidence on the management of ARVC patients during the antepartum and postpartum period.

Evidence-based Assessment

2010 ◽  
pp. 76-98
Author(s):  
John Hunsley ◽  
Eric J. Mash
Keyword(s):  
Decision Making ◽  
Clinical Assessment ◽  
Assessment Process ◽  
Evidence Based ◽  
The Past ◽  
Specific Assessment ◽  
The Many ◽  
Selection Of ◽  
Made In

Evidence-based assessment relies on research and theory to inform the selection of constructs to be assessed for a specific assessment purpose, the methods and measures to be used in the assessment, and the manner in which the assessment process unfolds. An evidence-based approach to clinical assessment necessitates the recognition that, even when evidence-based instruments are used, the assessment process is a decision-making task in which hypotheses must be iteratively formulated and tested. In this chapter, we review (a) the progress that has been made in developing an evidence-based approach to clinical assessment in the past decade and (b) the many challenges that lie ahead if clinical assessment is to be truly evidence-based.

scholarly journals Utilization of evidence-based therapies for heart failure in the institutionalized elderly

2005 ◽  
Vol 7 (7) ◽  
pp. 1122-1125 ◽  
Author(s):  
Marcelo C. Shibata ◽  
Cynthia M. Soneff ◽  
Ross T. Tsuyuki
Keyword(s):  
Heart Failure ◽  
Evidence Based ◽  
Institutionalized Elderly

Cardiology: Treatment of Systolic Heart Failure in the Elderly: An Evidence-Based Review

2010 ◽  
Vol 44 (10) ◽  
pp. 1604-1614 ◽  
Author(s):  
Sally A Arif ◽  
Karl A Mergenhagen ◽  
Roberto O Diaz Del Carpio ◽  
Christopher Ho
Keyword(s):  
Heart Failure ◽  
Systolic Heart Failure ◽  
The Elderly ◽  
Evidence Based

Heart failure in an elderly population

2001 ◽  
Vol 11 (4) ◽  
pp. 311-321
Author(s):  
DN Carmichael ◽  
Michael Lye
Keyword(s):  
Heart Failure ◽  
Treatment Options ◽  
Clinical Syndrome ◽  
Point Of View ◽  
Diagnostic Challenge ◽  
The Common ◽  
Characteristic Features ◽  
The Many ◽  
Neuroendocrine Activation ◽  
Symptoms And Signs

Heart failure has been defined in many ways and definitions change over time. The multiplicity of definitions reflect the paucity of our understanding of the primary underlying physiology of heart failure and the many diseases for which heart failure is the common end-point. Fundamentally, heart failure represents a failure of the heart to meet the body’s requirement for blood supply for whatever reason. It is thus a clinical syndrome with characteristic features – not a single disease in its own right. The syndrome includes symptoms and signs of organ underperfusion, fluid retention and neuroendocrine activation. The syndrome arises from a range of possible causes of which ischaemic heart disease is the commonest. From the point of view of a clinician, the underlying pathology will determine treatment options and prognosis. The extensive range of possible aetiologies present a diagnostic challenge both to correctly identify the syndrome amongst all other causes of dyspnoea and to identify the aetiology, allowing optimization of treatment.

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