Clinical and non-clinical markers of prognosis in heart failure

Heart failure (HF) is a major cause of morbidity and mortality, and the prevalence of HF is only increasing globally. The rise in prevalence is primarily attributed to a combination of increasing survival especially in patients in industrialized countries and increasing incidence in low- and middle-income countries (mostly in a younger population). The clinical course of HF varies from patient to patient. For some, an initial diagnosis of HF is soon followed by multiple hospitalisations deeply impacting their quality of life, others have a fairly indolent course and some die soon after a diagnosis of HF is made. The treatment for many also depends on various factors including the phenotype of HF, the aetiology of HF and other co-existent chronic conditions to name a few. There are patients with HF who may not be candidates for intensive invasive procedures but would on the other hand benefit from supportive care and palliative care advice with treatment being directed towards preservation of quality of life. Physicians are therefore often faced with the question of the prognosis their patients with HF face. Accurate assessment of prognosis is therefore important in shared decision making for patients with HF. However, assessment of prognosis is not straightforward. Reliance on a clinician’s acumen or single prognostic markers such as left ventricular ejection fraction (LVEF) and New York heart association (NYHA) class can be inaccurate and is not advised. Therefore, multivariable models were turned to in order to paint a more accurate picture of a patient’s prognosis by incorporating different individual markers known to be associated with clinical outcomes in HF. Multiple prognostic models have consequently been developed for assessment of prognosis in HF. However, uptake of these in clinical practice remain low. Many factors contribute including issues with reproducibility of prognostic ability in different populations, unavailability of variables and complexity of statistical methodologies. The evolving risk of different outcomes due to pharmacological and non-pharmacological advances in HF is another influencing factor. I consequently conducted a systemic analysis of the literature of prognostic models in HF – focusing primarily on a single phenotype of HF – HF with reduced ejection fraction (HFrEF). I identified several variables common to most models, with LVEF, sex, age, NYHA class being some of the most frequently featured. Inclusion of more contemporary prognostic markers such as NT-proBNP and non-clinical markers such as region, race/ethnicity and socioeconomic status was however very less frequent or absent altogether. Given this background, the aim of this thesis was to explore a select set of clinical and non-clinical markers, some of which have featured in previous models to review their prognostic importance along with a few which have not been featured in risk models in the past. The analyses presented were conducted in three contemporary clinical trial datasets in HFrEF – ATMOSPHERE, PARADIGM-HF and DAPA-HF. I used a variety of statistical measures to assess the association between 3 commonly used markers – LVEF, sex & chronic obstructive pulmonary disease (COPD) and 4 uncommonly/previously unused markers – geography & ethnicity, income inequality and frailty – and common clinical outcomes examined in HF. Different outcomes were tested – including cardiovascular, non-cardiovascular & all-cause death and first & recurrent HF, cardiovascular & all-cause hospitalisations. Cox regression was used to study the association between LVEF and COPD with various clinical outcomes. I used competing risk regression to study the other markers of prognosis and their association with clinical outcomes. In the DAPA-HF cohort, each 5% decrease in LVEF was associated with a 20% higher risk of HF hospitalisation (95% CI 1.13 – 1.27) and a 20% higher risk of cardiovascular death (95% CI 1.13 – 1.28). The risks of the same outcomes in those with COPD was 78% (95% CI 1.44 – 2.20) and 28% (95% CI 1.00 – 1.63) respectively. The rest of the analyses were carried out in a pooled cohort of the ATMOSPHERE and PARADIGM-HF trials. Women had a 19% lower risk of HF hospitalisation (95% CI 0.74 – 0.90) and 26% lower risk of cardiovascular death (95% CI 0.67 – 0.81). Among the Asian countries, the highest and lowest risk of hospitalisation for HF was seen in patients belonging to Taiwan (1.88; 95% CI 1.46 – 2.42) and India (0.44; 95% CI 0.36 – 0.54) respectively. In the same patients living in the Philippines had the highest risk of cardiovascular death (sHR 1.87; 95% CI 1.36 – 2.57) and the lowest risk of the same outcome was seen in those living in Japan (subdistribution hazard ratio (sHR) 0.68; 95% CI 0.46 – 0.98). When levels of income inequality were examined, patients lining in countries with the greatest inequality had a 57% higher risk of hospitalisation for HF (95% CI 1.36 – 1.81) and the risk of cardiovascular death was 50% greater (95% CI 1.29 – 1.74) compared to patients living in countries with the lowest income inequality. Using an acceptable method, I found that 69% of the population in ATMOSPHERE and PARADIGM-HF were frail. In the same population, the frailest patients carried a 89% higher risk of HF hospitalisation (95% CI 1.69 – 2.11) and the sHR for cardiovascular death was 2.14 (95% CI 1.92 – 2.38). All the above listed associations were statistically significant. In conclusion, I found that a select set of traditionally featured markers in prognostic models in HF remained strong predictors of hospitalisation and mortality in contemporary set of HF populations. In addition, several non-clinical and clinical markers that have infrequently featured in previous prognostic markers also carry significant value in measuring risk of clinical outcomes in HF. The inclusion of such markers may improve the predictive ability and clinical applicability of prognostic models in HF in the future

Pharmacocinétique de population du candesartan chez des patients atteints d’insuffisance cardiaque chronique

Contexte: L’insuffisance cardiaque (IC) est un syndrome clinique complexe regroupant un large spectre de mécanismes pathologiques qui peuvent altérer le fonctionnement de multiples organes, affectant ainsi la pharmacocinétique (PK) des médicaments. La modélisation pharmacocinétique de population (Pop-PK) consiste à appliquer des modèles non linéaires à effets mixtes dans le but de décrire l’exposition au traitement et quantifier la variabilité au niveau des paramètres PK. Objectif: Ce travail vise à évaluer par approche populationnelle la PK du candesartan en IC et à déterminer les covariables décrivant d’une façon statistiquement et cliniquement significative la variabilité au niveau de la clairance. Méthodes: Les données d’une étude pharmacogénomique ouverte, multicentrique et prospective ont été récupérées pour amorcer notre analyse. Le processus de modélisation et les simulations nécessaires sont réalisés à l’aide du logiciel NONMEM (Nonlinear Mixed Effects Modeling). Les covariables préliminaires ont été sélectionnées par des tests statistiques tels que la régression linéaire et l’ANOVA. Enfin, l’élaboration du modèle final est effectuée en utilisant le processus de sélection séquentielle « forward/backward ». Résultats: Un total de 281 patients caucasiens ont été inclus pour développer le modèle Pop-PK. Les données du candesartan ont été caractérisées par un modèle à un compartiment avec absorption de premier ordre et temps de latence. Le poids, l'âge, la fraction N-terminale du pro-peptide natriurétique de type b (NT_proBNP), le débit de filtration glomérulaire (DFG), le diabète, l'utilisation du furosémide et le sexe étaient les covariables sélectionnées préliminairement pour la clairance apparente (CL/F). Le modèle final développé pour la clairance apparente est représenté par l'équation suivante : CL/F (L/h) = 8.63*(Poids/82.45)0.963 * (DFG/74)0.56 * (0.682) Diabète * EXP0.138 Les simulations ont révélé qu'une diminution importante de la clairance orale (diminution de plus que 25 %) est obtenue en combinant les facteurs significatifs retenus dans le modèle final (patients ayant un faible poids corporel avec une insuffisance rénale modérée à sévère et patients diabétiques avec une insuffisance rénale faible à modérée). Nous avons constaté que les patients ayant ces combinaisons dans notre base de données présentaient des concentrations comparables à celles des autres patients malgré qu’ils aient toléré de plus faibles doses pendant la titration. Conclusion: La modélisation PK de population a servi comme une approche efficace pour caractériser la PK du candesartan en IC et pour identifier une sous-population à risque d’une exposition élevée. Le poids, le DFG et le diabète sont des prédicteurs indépendants de la clairance du candesartan en IC. Considérant ces facteurs, une approche plus individualisée de l'administration du candesartan est nécessaire chez les patients atteints d’IC.Context: Heart failure (HF) is a clinical condition that causes pathological changes all over the body affecting hence the pharmacokinetic of drugs. Population pharmacokinetic modeling (Pop-PK) consists in applying non-linear mixed-effects models to characterize treatment exposure and quantify PK parameters variability. Objective: The aim of this study was to investigate the pharmacokinetic (PK) of candesartan in HF patients while examining statistically and clinically significant covariates on estimated clearance using population pharmacokinetics (Pop-PK) modeling approach. Methods: Data from a prospective, multicenter, open label, pharmacogenomic study were available for this analysis. Modeling and simulations were conducted using Nonlinear Mixed-Effect Modeling software NONMEM. Preliminary selection of covariates was accomplished with statistical tests (linear regression and ANOVA). Final model development was performed using forward/backward selection approach on the preliminarily selected covariates. Results: A total of 281 Caucasian patients were included to develop the Pop-PK model. Candesartan data were characterized by a 1 compartment model with first order absorption and lag time. Weight, age, N-terminal pro b-type natriuretic peptide (NT_proBNP), estimated glomerular filtration rate (eGFR), diabetes, use of furosemide and sex were the preliminarily selected covariates for apparent clearance (CL/F). The final model developed for apparent clearance is represented by the following equation: CL/F (L/h) = 8.63*(Weight/82.45)0.963 * (eGFR/74)0.56 * (0.682) Diabetes * EXP0.138 Simulations revealed that an important decrease in oral clearance (decrease of more than 25%) is obtained with the combination of the significant factors retained in the final model (patients having low weight with moderately to severely impaired renal function and diabetic with mildly to moderately impaired renal function). Patients having these combinations in our database were found to achieve comparable concentrations to the rest of patients despite tolerating only lower doses. Conclusion: Population pharmacokinetic modeling provided an effective approach to characterize the PK of candesartan in HF and to identify a subpopulation at potential risk of high exposure. Weight, eGFR and diabetes are independent predictors of candesartan clearance in patients with HF. Considering these factors, a more individualized approach of candesartan dosing is needed in HF patients

Shear wave echocardiography

In this thesis we demonstrate that the assessment of the diastolic function of the left ventricle withclassical echocardiography remain

Investigating quality of care for diabetes mellitus, congestive heart failure and chronic kidney disease in Ontario’s Family Health Group and Family Health Organization models

Background and objectives: In Ontario, primary care reform was initiated in the early 2000s with an aim to improve the quality of primary care. Hence, the provincial government restructured family physicians’ remuneration package. Prior to the reform, most physicians received majority of their income through fee-for-service (FFS). In Ontario, Family Health Group (FHG) and Family Health Organization (FHO) are dominant post-reform primary care models that remunerate family physicians through blended FFS and blended capitation, respectively. In three studies, we compared physicians in FHGs and FHOs in terms of their care provision for persons with diabetes mellitus (1st study), congestive heart failure (CHF) (2nd study) and chronic kidney disease (CKD) (3rd study). Methods: All data were obtained from the ICES (formerly known as the Institute for Clinical Evaluative Sciences). For the first and second studies, we employed propensity score-based weights and fixed effects regressions on a balanced panel of physicians spanning 10 years; all analyses were conducted at the physician level. In these two studies, the comparison was between physicians in FHG who never switched to FHO or other models (i.e., non-switchers); switchers were physicians who switched from FHG to FHO. For the third study, we performed two cross-sectional analyses at the physician level; lack of data availability for patients with CKD over time deterred us from conducting longitudinal analyses as in the first two studies. Results: We found that switching from FHG to FHO was associated with an improvement in some aspects of diabetes care. We found that CHF care—in terms of physicians’ follow-up of patients who are discharged—was not different between switchers and non-switchers. We found that some aspects of CKD care were better with physicians in FHG relative to their counterparts in FHO. Conclusions: Compared to blended FFS, blended capitation payment is associated with a small but statistically significant improvement in some aspects of diabetes care. Our findings suggest that follow-up care for patients with CHF is similar in Ontario’s blended FFS and blended capitation models. Though we found that blended FFS is associated with greater adherence to some CKD process measures, future studies could employ longitudinal regressions to account for more confounding

Heart failure syndrome and predicting response to cardiac resynchronisation therapy.

Heart failure results from the heart pumping insufficient quantities of blood to meet the body’s metabolic requirements. This condition affects around 600,000 people in the United Kingdom and carries with it a significant morbidity and mortality. Patients typically complain of reduced exercise capacity and a poor quality of life. Whilst there are various pharmaceutical options available to clinicians, none directly augment cardiac function. Cardiac resynchronisation therapy (CRT) is proven to reverse the progression of left ventricular systolic dysfunction, the most common cause of heart failure. The device resynchronises inefficient cardiac function, reducing symptoms and improving stroke volume and life expectancy. However, only two thirds of patients typically derive benefit from this pacemaker, it being unclear why. Finding a sensitive and specific predictor of response would be invaluable, preventing potential harm to patients, reducing waste and targeting the patient groups who will derive benefit. In this body of work, the heart failure syndrome is delineated; the evidence underpinning CRT discussed and the difficulties in defining response outlined. There are 2 main research themes in this body of work, measuring and predicting response to CRT. In the former, the role of patient specific three-­‐dimensional computational models and biophysical properties are investigated, and, in the latter, the influence of CRT on the heart failure syndrome using biomarkers. It is concluded that CRT response can be predicted using patient specific computational models of the left ventricle, but they are too complex for routine clinical use. Biophysical markers have more merit in the immediate future, being simper and quicker, with measures of endothelial and skeletal muscle function, demonstrating promise in a small cohort of patients. Finally, there exists a significant level of undiagnosed pathology in this patient group, such as hyperuricaemia and hyperparathyroidism, but it remains unclear what impact CRT has on this comorbidity

An Informatics Roadmap Toward a FAIR Understanding of Mitochondrial Biology and Rare Mitochondrial Disease

Mitochondrial biology is integral to our fundamental understanding of human health and many diseases. They exist in every human cell type except for red blood cells and have critical functions in metabolism, oxidative phosphorylation, oxidation-reduction, and as signaling hubs responsible for mediating protective mechanisms. Rare mitochondrial diseases (RMDs) are devastating and complex, affect multiple organ systems, and disproportionately impact young children. Despite copious existing knowledge and increased public interest, the knowledge is fragmented and difficult to access. Clinical case reports (CCRs) on RMDs contain valuable clinical insights, but they are scarce and lack the metadata necessary to facilitate their discovery among the two million CCRs on PubMed. The unstructured text data of CCRs is also ill-suited to computational approaches, limiting our ability to derive the knowledge contained within.To address these issues, I assembled all available informatics tools and resources with mitochondrial components and used them to contribute to Gene Wiki pages that enable easy access to mitochondrial knowledge for researchers, students, clinicians, and patients. Through these efforts, I made mitochondrial gene, protein, and disease knowledge widely accessible with contributions of over 4MB of content across 541 Gene Wiki pages. Concurrently, I used Gene Wiki as an educational platform to train over 50 students in the biosciences and pre-medical studies in mitochondrial biology and disease, as well as instilling effective research and writing methods in biomedicine.To impose structure on CCRs and render them FAIR (Findable, Accessible, Interoperable, Reusable), I developed and applied a standardized metadata template to RMD CCRs and codified patient symptomology with the International Statistical Classification of Disease and Related Health Problems (ICD) system. I created the open-source, cloud-based MitoCases RMD Knowledge Platform (http://mitocases.org/) to house data on 384 RMD CCRs, including 4,561 instances of 952 unique ICD codes. Supplementing CCRs with structured metadata amplifies machine-readable information content and provides a distinct improvement in searching for CCRs as compared to indexing by title and abstract. Finally, I employed these resources to conduct a thorough review of Barth syndrome and characterized the diversity of presentations, range of genetic etiologies, and treatment paradigms

A grounded theory study exploring healthcare professionals' experiences of decision making when managing the care of patients diagnosed with end stage heart failure

Background End of life care has been a topic which has been debated, discussed and strategized over the last ten years as part of the government initiative to improve care for those patients with long term conditions requiring palliative and end of life care. (DH 2008, DH 2010). Studies show that despite these government and local recommendations’ heart failure patients are still not being given the opportunity to access individualised end of life care and the services they require to support themselves and their families at the end of life. Aim The aim of this study was to explore the decision-making process between healthcare professionals and patients in an acute medical setting when it came to making end of life decisions. Method A constructivist grounded theory was conducted over a 12-month period in a District General Hospital in the North West of England. A purposeful sample of 15 nurses, 11 doctors and 16 patients were recruited from the acute medical setting. Data was collected using semi structured interviews and focus groups. The interviews were recorded and transcribed and data was analysed using the constant comparison and QSR NVivo. Findings Four theoretical categories emerged from the data to explain how healthcare professionals and patients negotiated the process of decision making when considering end of life care. These four categories; signposting symptoms, organising care, being informed and recognising dying were found to revolve around a core category ‘vicious cycle of care’ which was fast paced, turbulent and time limited. This cycle was found to disable the process of decision making between the healthcare professional and patient resulting in missed opportunity for the patient to transition to palliative care. Conclusion The emerging theory ‘vicious cycle of care’ offers an explanation as to why decisions were not made by healthcare professionals to transition patients with end stage heart failure to palliative care. Further work needs to be undertaken with healthcare professionals and patients to map out a ‘cycle of care’ which identifies key stages in the terminal stage of heart failure and correctly signposts the patient to the right healthcare care professional for intervention. Further research is required with General Practitioners to further explore the barriers to providing end of life care for heart failure patients