Browsing by Subject "Heart failure"

Effective patient care transitions require consideration of the patient’s social and clinical contexts, yet how these factors relate to the processes in care coordination remains poorly described. This dissertation aimed to describe provider networks and clinical care and social contexts involved during longitudinal care transitions across settings. The overall purpose of this dissertation is to uncover the longitudinal patterns of utilization and relational processes needed for effective care coordination in transitional care, so we can redesign interventions that focus on informational and relationship networks to improve interaction patterns and system performance for people living with heart failure (HF) as they undergo transitions across settings and over time. This dissertation was a retrospective exploratory study. Chapter 2 is an integrative review examining coordination processes in transitional care interventions for older adults with HF by integrating a social network analysis framework. We subsequently selected a cohort of patients aged 18 years or older (n = 1269) with an initial hospitalization for HF at Duke University Health System between January 1, 2016 and December 31, 2018 based on encounter, sociodemographic, and clinical data extracted from electronic health records (EHR). In Chapter 3, a latent growth trajectory analysis was used to identify distinct subgroups of patients based on the frequency of outpatient, as well as emergency department (ED) and inpatient encounters 1 year before and 1 year after the index hospitalization; multinomial logistic regression was then used to evaluate how outpatient utilization was related to acute care utilization. Based on findings (described in Chapter 3), we purposively sampled 11 patients from the Chapter 3 cohort for a second empirical study (described in Chapter 4) with a mixed-methods sequential explanatory design. These 11 patients had a full spectrum of experience in socioeconomic disadvantages based on three strata (race, insurance, and Area Deprivation Index), but they had similar levels of comorbidity and average severity of illness and displayed the same change in the severity of illness during the study period. We used quantitative and qualitative data available from clinical notes in the EHR, and integrated results from quantitative and qualitative analysis to better understand the social and clinical context and social structure essential for care coordination. High variability in transitional care is likely because care coordination processes are highly relational. The relational structure of transitional care interventions varied from triadic to complex network structures. Use of a network analysis framework helped to uncover relational structures and processes underlying transitional care to inform intervention development. Chapter 3 revealed that high heterogeneity exists in patients’ utilization patterns. A small subgroup of high users utilized a substantial amount of the resources. Patients with high outpatient utilization had more than 4 times the likelihood of also having high acute care utilization, and change in the severity of illness had the highest level of significance and strongest magnitude of effect on influencing high acute care utilization. Chapter 4 demonstrated the feasibility of using clinical notes and social network analysis (SNA) to assess the provider networks for patients with HF in care transitions. People who were experiencing more socioeconomic disadvantages and social instability were less likely to have densely connected provider teams and providers who were central and influential in the system network. Lacking consistent and reciprocal relationships with outpatient provider teams, especially primary care provider and cardiology teams, was precedent to poor care management and coordination. Turbulence in care transition can result from sources other than transitioning between settings. This dissertation demonstrated the (a) importance of understanding relational processes and structure during patients’ utilization of acute and outpatient care services and (b) potential to capture structural inequalities that may influence the efficiency of care coordination and health outcomes for patients with HF.

Heart Failure (HF) is a major cause of morbidity and mortality in the world. This disorder is characterized by compromised systolic and/or diastolic function of the myocardium that reduces the pumping and/or filling efficiency resulting in diminished cardiac output. Cardiovascular researchers have been attempting to develop tools for assessment of cardiac function for decades. Evaluating cardiac function helps clinicians to diagnose and to follow the progress of HF patients.

The gold standard technique for cardiac functional assessment, including systolic and diastolic function, is the pressure-volume (PV) loop measurement; however, this measurement is not typically used clinically due to the invasiveness of the technique. PV loop measurement requires the introduction of a pressure or pressure-volume catheter into the left ventricle. Cardiovascular researchers have been attempting to develop non-invasive tools for assessment of cardiac function primarily by measuring surrogates of ventricular contractility and compliance.

These measures are based on imaging and include Ejection Fraction and Doppler and ultrasound strain imaging. These measurements are indirect measures that rely on cardiac motion or volume changes. The measurements are load dependent and could be affected by the heart rhythm and valvular disorders. Despite research toward this goal, there is no clinically accepted noninvasive technique to provide a direct myocardial measurement of cardiac function.

Acoustic radiation force (ARF) based ultrasound elastography techniques were developed in early 2000s and have been used to measure the static stiffness of tissue. These techniques are being used in the clinic for diagnosis of disorders and malignancies in tissues such as liver. When applied to the heart, it was shown that dynamic changes in the stiffness of the myocardium during the cardiac cycle could be recorded using modified versions of these static techniques.

This had the potential to be a direct measure of the time-varying elastance measured during the cardiac cycle using pressure-volume measurements. The question arose as to whether these ARF based measurements of dynamic stiffness could be used for cardiac functional assessments during systole and diastole; and if so, what the relationship is between these measurements and the gold standard method. The goal of this research was to assess the ability of ARF based measurements of cardiac dynamic stiffness to provide meaningful indices of cardiac function.

In this dissertation both acoustic radiation force impulse (ARFI) and shear wave elasticity imaging (SWEI) ultrasound elastography techniques were studied. These are qualitative and quantitative measures of stiffness, respectively. While the focus of the studies was more on SWEI due to its quantitative nature, ARFI measurements of cardiac function were also investigated and compared to SWEI.

The studies were performed in isolated rabbit hearts in Langendorff or working modes because the preparation has several significant advantages. 1) Parameters including preload, afterload, and coronary perfusion can be accurately controlled. 2) The heart’s left ventricular free wall can be easily imaged from multiple angles. 3) Confounding neurohormonal reflexes of the body can be eliminated.

SWEI measurements of stiffness were used to characterize changes in contractility induced using the Gregg effect. The Gregg effect is the active effect of coronary perfusion on cardiac contractility. It was shown that SWEI measurements of stiffness could detect the changes in contractility induced by this known effect and that the effect was blocked using a Ca channel blocker.

The relationship between ARFI and SWEI measurements was characterized and the possibility of deriving functional indices such as systolic/diastolic ratio and isovolumic relaxation time constant (τ) using either of these techniques was evaluated. It was shown that in the same imaging configuration, the measurements of ARFI and SWEI are linearly related to one another. This could be important, as the ARFI technique will likely be the first cardiac elastography measurement technique to be implemented using transthoracic ultrasound throughout the cardiac cycle.

The Garden Hose effect was used to investigate SWEI’s ability to measure cardiac compliance. SWEI was used to detect the passive effect of coronary perfusion on cardiac compliance and the relationship between perfusion pressure and stiffness was characterized. Finally, SWEI derived measurements of diastolic function were compared to the gold standard PV measurements of cardiac diastolic function including end diastolic stiffness and the relaxation time constant. It was shown that SWEI could detect the changes in cardiac stiffness after induction of global ischemia. These changes were similar to the changes in the PV measure of diastolic stiffness. Furthermore, the results indicated that SWEI could be used to derive the relaxation time constant similar to the relaxation constant derived from intra-ventricular pressure recordings.

In summary, the results of the studies presented in this thesis illustrate the assessments of systolic and diastolic function using ARFI and SWEI ultrasound based elastography. It is concluded that these measurements can be used to derive cardiac functional indices that would have the advantages of an ultrasound based technique; they would be noninvasive, less expensive and could be widely applied outside of the cath lab.

The main function of the human heart is to act as a pump, facilitating the delivery of oxygenated blood to the many cells within the body. Heart failure (HF) is the medical condition in which a heart cannot adequately pump blood to the body, often resulting from other conditions such as coronary artery disease, previous heart attacks, high blood pressure, diabetes, or abnormal heart valves. HF afflicts approximately 6.5 million adults in the US alone [1] and manifests itself often in the form of fatigue, shortness of breath, increased heart rate, confusion, and more, resulting in a lower quality of life for those afflicted. At the earliest stage of HF, an adequate treatment plan could be relatively manageable, including healthy lifestyle changes such as eating better and exercising more. However, the symptoms (and the heart) worsen overtime if left untreated, requiring more extreme treatment such as surgical intervention and/or a heart transplant [2]. Given the magnitude of this condition, there is potential for large impact both in (1) automating (and thus expediting) the diagnosis of HF and (2) in improving HF treatment options and care. These topics are explored in this work.

An early diagnosis of HF is beneficial because HF left untreated will result in an increasingly severe condition, requiring more extreme treatment and care. Typically, HF is first diagnosed by a physician during auscultation, which is the act of listening to sounds from the heart through a stethoscope [3]. Therefore, physicians are trained to listen to heart sounds and identify them as normal or abnormal. Heart sounds are the acoustic result of the internal pumping mechanism of the heart. Therefore, when the heart is functioning normally, there is a resulting acoustic spectrum representing normal heart sounds, that a physician listens to and identifies as normal. However, when the heart is functioning abnormally, there is a resulting acoustic spectrum that differs from normal heart sounds, that a physician listens to and identifies as abnormal [3]–[5].

One goal of this work is to automate the auscultation process by developing a machine learning algorithm to identify heart sounds as normal or abnormal. An algorithm is developed for this work that extracts features from a digital stethoscope recording and classifies the recording as normal or abnormal. An extensive feature extraction and selection analysis is performed, ultimately resulting in a classification algorithm with an accuracy score of 0.85. This accuracy score is comparable to current high-performing heart sound classification algorithms [6].

The purpose of the first portion of this work is to automate the HF diagnosis process, allowing for more frequent diagnoses and at an earlier stage of HF. For an individual already diagnosed with HF, there is potential to improve current treatment and care. Specifically, if the HF is extreme, an individual may require a surgically implanted medical device called a Left Ventricular Assist Device (LVAD). The purpose of an LVAD is to assist the heart in pumping blood when the heart cannot adequately do so on its own. Although life-saving, LVADs have a high complication rate. These complications are difficult to identify prior to a catastrophic outcome. Therefore, there is a need to monitor LVAD patients to identify these complications. Current methods of monitoring individuals and their LVADs are invasive or require an in-person hospital visit. Acoustical monitoring has the potential to non-invasively remotely monitor LVAD patients to identify abnormalities at an earlier stage. However, this is made difficult because the LVAD pump noise obscures the acoustic spectrum of the native heart sounds.

The second portion of this work focuses on this specific case of HF, in which an individual’s treatment plan includes an LVAD. A signal processing pipeline is proposed to extract the heart sounds in the presence of the LVAD pump noise. The pipeline includes down sampling, filtering, and a heart sound segmentation algorithm to identify states of the cardiac cycle: S1, S2, systole, and diastole. These states are validated using two individuals’ digital stethoscope recordings by comparing the labeled states to the characteristics expected of heart sounds. Both subjects’ labeled states closely paralleled the expectations of heart sounds, validating the signal processing pipeline developed for this work.

This exploratory analysis can be furthered with the ongoing data collection process. With enough data, the goal is to extract clinically relevant information from the underlying heart sounds to assess cardiac function and identify LVAD disfunction prior to a catastrophic outcome. Ultimately, this non-invasive, remote model will allow for earlier diagnosis of LVAD complications.

In total, this work serves two main purposes: the first is developing a machine learning algorithm that automates the HF diagnosis process; the second is extracting heart sounds in the presence of LVAD noise. Both of these topics further the goal of earlier diagnosis and therefore better outcomes for those afflicted with HF.

Heart failure is a worldwide public health problem with substantial clinical burden and economic costs. In the progression into failure, the heart undergoes dramatic alterations in mitochondrial fuel metabolism and bioenergetics. As such, there is considerable interest in the delineation of regulatory events involved in the metabolic dysfunction of heart failure. Previous collaborative work identified three metabolic signatures associated with early stage heart failure: 1) accumulation of acylcarnitine metabolites; 2) mitochondrial hyperacetylation; and 3) elevated ketone catabolism. The goal of this dissertation was to explore the role of these metabolic signatures in the pathogenesis of heart failure.

Tissue accumulation of acylcarnitine metabolites is characteristic of mitochondrial dysfunction and indicative of incomplete β-oxidation. This occurs when a large portion of the fatty acids (i.e., acyl groups) within the mitochondria are not fully catabolized and the resulting intermediates are transferred to carnitine esters, enabling the traversal of biological membranes and departure from the mitochondrial matrix.

Nϵ-acetylation in the mitochondrial matrix is a non-enzymatic, post-translational modification (PTM) that spontaneously arises from the relatively basic pH and abundance of acetyl-CoA. Accumulation of this PTM has been observed in other tissues and disease states with evidence suggesting it impairs mitochondrial metabolism and causes dysfunction. However, convincing studies are lacking to establish a direct causal connection between dysfunction and acetylation. To address this shortcoming, a novel assay platform for the comprehensive assessment of mitochondrial bioenergetic transduction was developed and validated. Next, we generated and validated a novel mouse model of cardiac mitochondrial hyperacetylation and utilized the bioenergetic assay platform to test the hypothesis that it causes metabolic perturbations. Surprisingly, these hyperacetylated mitochondria exhibited almost no deficits in mitochondrial oxidative metabolism. To determine if hyperacetylation causes mitochondrial dysfunction in vivo under pathologic stimuli, the mouse model and littermate controls were subjected to transaortic constriction, a surgical method to induce pressure-overload heart failure. The hyperacetylated animals did not exhibit enhanced sensitivity toward cardiac dysfunction relative controls. With these results, we concluded that mitochondrial hyperacetylation does not contribute to the pathogenesis of heart failure.

Elevated ketone catabolism was observed in early stage failing hearts. Through a series of murine and canine heart failure models, ketone catabolism was shown to be adaptive in response to pathological stress. Additionally, the mitochondrial bioenergetic assay platform was applied to cardiac mitochondria under substrate limited-conditions. These results indicate that ketone catabolism improves bioenergetic efficiency under constraints which mimic the failing heart. With these results, we conclude ketone catabolism is an important metabolic defense in response to the dysfunction of the failing heart.

Background: Cardiovascular diseases form a large part of a growing pandemic of non-communicable diseases afflicting Sub-Saharan Africa(1–3) Heart failure is one of the most debilitating of these diseases. The global 5-year life expectancy of patients afflicted by heart failure is less than 50%(4–6). Cardiac rehabilitation (CR) has been demonstrated to improve functional status, quality of life, and reduce depression in patients with heart failure(7,8). Even though CR is a simple and comparatively low-cost intervention, adherence rates of CR remains poor and are estimated at 20% in the US(9–11). In Western Kenya, CR is non-existent. We sought to establish the feasibility of two different models of cardiac rehabilitation for heart failure in Western Kenya and to identify potential barriers to participation.

Methods: This was a feasibility study using mixed methods to describe characteristics and changes in a cohort of patients with heart failure. Study participants were prospectively recruited and allocated by convenience into an institution based cardiac rehabilitation (IBCR) arm, a home based cardiac rehabilitation (HBCR) arm and an observational arm (OA). At completion of 3 month follow up period, participants were invited to take part in focus group discussions exploring perspectives on heart failure and cardiac rehabilitation. The primary measure of feasibility was the ability of study participants to attain a mean adherence rate of at least 25%, of prescribed rehabilitation sessions.

Results: This study found that cardiac rehabilitation is a feasible intervention for patients with heart failure in Western Kenya with an adherence rate of 46% for institutional based cardiac rehabilitation and an adherence rate of 28% for home based cardiac rehabilitation. All study arms demonstrated significant change in depression screening and quality of life scores. Participants in focus group discussions identified competing interests, distance to the facility and forgetfulness as barriers to cardiac rehabilitation.

Conclusions: Cardiac rehabilitation is a feasible treatment intervention for heart failure in Western Kenya. However, the barriers to delivery of care are similar to barriers in other health systems around the world(12). There is need for further research to evaluate the efficacy of cardiac rehabilitation and development of innovative ways to improve treatment adherence.

Fibroblast growth factor homologous factors (FHFs) are non-canonical members of the fibroblast growth factor family (FGF11-14) that were initially discovered to bind and regulate neuronal and cardiac voltage-gated Na+ channels. Loss-of-function mutations that disrupt interaction between FHFs and Na+ channels cause spinocerebellar ataxias and cardiac arrhythmias such as Brugada syndrome. Although recent studies in brain of FHF knockout mice suggested novel functions for FHFs beyond ion channel modulation, it is unclear whether FHFs in the heart serve additional roles beyond regulating cardiac excitability. In this study, we performed a proteomic screen to identify novel interacting proteins for FGF13 in mouse heart. Mass spectrometry analysis revealed an interaction between FGF13 and a complex of cavin proteins that regulate caveolae, membrane invaginations that organize protective signaling pathways and provide a reservoir to buffer membrane stress. FGF13 controls the relative distribution of cavin 1 between the plasma membrane and cytosol and thereby acts as a negative regulator of caveolae. In inducible, cardiac-specific Fgf13 knockout mice, cavin 1 redistributed to the plasma membrane and stabilized the caveolar structural protein caveolin 3, leading to an increased density of caveolae. In a transverse aortic constriction model of pressure overload, this increased caveolar abundance enhanced cardioprotective signaling through the caveolar-organized PI3 kinase pathway, preserving cardiac function and reducing fibrosis. Additionally, the increased caveolar reserve provided mechanoprotection, as indicated by reduced membrane rupture in response to hypo-osmotic stress. Thus, our results establish FGF13 as a novel regulator of caveolae-mediated mechanoprotection and adaptive hypertrophic signaling, and suggest that inhibition of FHFs in the adult heart may have cardioprotective benefits in the setting of maladaptive hypertrophy.

Background: HIV-associated cardiac dysfunction has severe consequences, and traditional measures of echocardiography underestimate disease. Novel echocardiographic measures may detect early disease in time for intervention. The aims of this study are to define the prevalence of early cardiac dysfunction in children living with HIV, and the relationships between cardiac function and same-day plasma HIV RNA levels.

Methods: Using a cross-sectional study design, we performed echocardiograms and obtained plasma HIV RNA levels on perinatally HIV-infected children engaged in care at Moi Teaching and Referral Hospital in Eldoret, Kenya. Early cardiac dysfunction was defined as normal ejection fraction but left ventricular global longitudinal strain (LV GLS) z-score < -2 or myocardial performance index (MPI) ≥ 0.5. The relationship between measures of cardiac function and HIV RNA levels and the assessment of other clinical covariates (age, sex, duration on antiretrovirals, and AZT exposure) with measures of cardiac function were modeled using multivariable regression.

Results: 302 perinatally HIV-infected children (mean age 9.8±3.2 years, range 2-16 years) were enrolled. The mean BMI-for-age z-s core was -1.0±1.1. The median duration on antiretrovirals was 5.4 years (IQR 3.2, 7.6). One hundred and sixteen children (38.4%) had been exposed to AZT (median duration of exposure 2.6 years, IQR 0.2, 5.4). One hundred and one of 298 (33.9%) had HIV RNA measurements ≥ 40 copies/ml. Only 1 of 302 children had LV GLS-for-BSA z-score ≤ -2 and normal ejection fraction meeting the criteria for early cardiac dysfunction, and 65 of 292 (22.3%) children had an MPI ≥ 0.5 and normal ejection fraction. In multivariate analysis, neither LVGLS z-score nor MPI were associated with HIV RNA levels ≥ 40 copies/ml or any clinical variables in the model [β -0.08 (95%CI -0.39, 0.23) and β 0.01 (95%CI -0.01, 0.03), respectively]. MPI was very weakly correlated with LVGLS z-score (r -0.15; 95%CI -0.26, -0.04).

Conclusions: Nearly one quarter of these perinatally HIV-infected children demonstrated echocardiographic evidence of early cardiac dysfunction, based primarily on abnormal MPI measurements. This finding was not correlated with same-day HIV RNA levels or other clinically relevant variables. Further investigation into the clinical significance of this finding is urgently needed as abnormal MPI measurements have been shown to be predictive of heart failure in at risk populations.