Clinical Outcomes for Heart Failure Patients Response
Description
Rastogi, A., Novak, E., Platts, A. E., & Mann, D. L. (2017). Epidemiology, pathophysiology, and
clinical outcomes for heart failure patients with a mid-range ejection fraction. European Journal of Heart Failure, 19, 1597-1605. https://doi.org/10.1002/ejhf.879
Typical presentation of chronic heart failure patients includes an ejection fraction of less than 35%. With medication therapies and interventional management, the prevalence of heart failure patients with preserved ejection fractions is increasing. There are no current guidelines for management of patients with a mid-range or preserved ejection fraction, so this articles purpose (or goal) is to determine the epidemiology, pathophysiology, and clinical outcomes of those patients with mid-range ejection fractions (Rastogi et al., 2017). Having the goal identified for the study gives a purpose on what will be studied throughout this research article. It gives defined outcomes or goals for the study to reach. This article used a case-control study of patients registered in the Washington University Heart Failure Registry (Rastogi et al., 2017). A case control study is set up to compare those who have the disease to individuals who do not and compare exposure to risk factors of said disease to understand the relationships that exist between risk factors and disease. Patients for this study were pulled from the Washington University Heart Failure Registry, and while the registry enrolled all heart failure patients, for this study, any patient with an unknown ejection fraction were excluded from this study (Rastogi et al., 2017). There were three tiers of heart failure patients identified: those who had an ejection fraction of 40-50% during the analysis, those who had an ejection fraction (EF) less than 40% which improved from the start of the analysis to present, and those who had an ejection fraction of 50% or greater and had a lower ejection fraction by the end of the analysis (Rastogi et al., 2017). So, while there were different tiers of heart failure EFs, those who did not have a recorded EF were excluded from the analysis completely. Bias was avoided by assessing outpatient echocardiogram results of the left ventricular (LV) function rather than the LV function in an acute hospitalization (where it may be lower than normal) (Rastogi et al., 2017). Data such as death, heart failure hospitalization, transplant, or cardiac hospitalization was collected by home, in-person, and telephone interviews of those enrolled and consented to the study (Rastogi et al., 2017). Data was also collected from patients records at six-, twelve-, eighteen-, and twenty-four-month intervals and those enrolled who were unable to be reached, verified death status by their social security number (Rastogi et al., 2017). There were over fifteen variables identified in this study. The variables were related to patient demographics, including age, gender, race, chronic diseases (COPD, diabetes, CAD, PVD), blood pressure measurements, as well as class and stage of heart failure the patient is in (Rastogi et al., 2017). Outcomes were in time-to-event analyses, and Kaplan- Meier curves were created and compared by a log-rank test (Rastogi et al., 2017). There was a hazard ratio created for each outcome variable (Rastogi et al., 2017). The focus was placed on a selected demographic of heart failure patients with mid-range ejection fractions. With a cohort study, the research is focused solely on one demographic so it can create a selection bias. It seems in this article, that selection bias tended to happen. This type of study may also create an observational bias in some instances. The factor of incidence within epidemiology is cannot typically be calculated with a cohort study. The conclusion of this study shows great strength to changing the protocol of heart failure management. The results of this study showed that a growing population of heart failure patients have a preserved or mid-range EF and still have the same rates of hospital admissions, cardiac complications, and death, which suggests that less emphasis needs to be placed on recovering the EF in heart failure (Rastogi et al., 2017).
Manemann, S. M., Chamberlain, A. M., Boyd, C. M., Gerber, Y., Dunlay, S. M., Weston, S. A.,
Jiang, R., & Roger, V. L. (2016). Multimorbidity in heart failure: Effect on outcomes. Journal of the American Geriatrics Society, 64(7), 1469-1474. https://doi.org/10.1111/jgs.14206
The aim of this study was to investigate the amount and type of comorbid disease on death and number of hospitalizations of heart failure patients (Manemann et al., 2016). Heart failure is a chronic condition and is typically presented with other disease comorbidities. Prior research has shown that comorbidities can heighten cost of care in heart failure patients and create longer and more complicated hospital admissions. This study is presented as a cohort research style. Cohort studies are those that research a group of people that can be prospective (forward-looking) or retrospective (looking back). Most cohort studies are prospective and plan the study in advance to be carried out in a future time. Participants with a possible heart failure diagnosis in Olmstead County, aged twenty-one or older, were recruited from an outpatient visit or hospital admission (Manemann et al., 2016). From a sample population, 50% of patients with a heart failure diagnosis from 2000-2006 and all potential heart failure cases were reviewed for this study using a Framingham criterion (Manemann et al., 2016). The United States Department of Health and Human Services have identified 20 chronic conditions and were divided in three categories: cardiovascular- related conditions, other physical conditions, and mental conditions (Manemann et al., 2017). Chronic illness of heart failure disease was not included in this study since all patients had a diagnosis of heart failure. Data was collected from the three identified comorbidity groups. Ejection fraction data was collected from echocardiograms, angiograms, or scans within 90 days before or after heart failure diagnosis, defined as either preserved or reduced ejection fractions (Manemann et al., 2017). Variables for this study were defined by hazard ratio (HR) with confidence intervals (association with death) (Manemann et al., 2016). The risk of death per increase of a cardiovascular condition was not associated with death, with a physical condition was 14% (HR=1.14), and risk with a mental condition was 31% (HR=1.31) (Manemann et al., 2016). Data was analyzed through a follow-up of 4.2 years, which tracked the number of hospitalizations (6,306) and death rate of 1,073 (Manemann et al., 2016). Some limitations to this study involved exclusion of geriatric conditions which can correlate with heart failure, and that recruitment and identification of chronic conditions through ICD-billing and coding could have created an error in incidence of chronic illnesses identified (Manemann et al., 2016). The downfall of cohort studies includes them being longitudinal and costly to conduct. Cohorts tend to be time consuming and can exclude rare disease prevalence. The greatest strength of this study was highlighting that physical and mental illness tend to have a higher impact on the death rate of heart failure patients than cardiovascular diseases commonly lumped in with a heart failure diagnosis. Most heart failure patients have hypertension, hyperlipidemia, or arrythmias which providers focus on when treating heart failure. Emphasis needs to be placed on a holistic approach with heart failure patients especially mental conditions such as depression and anxiety, which this study found is more impactful on death rates than hypertension and hyperlipidemia.
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