Biomarker discovery in heart failure Citation for published version (APA): Davarzani, N. (2018). Biomarker discovery in heart failure. Maastricht University. https://doi.org/10.26481/dis.20180702nd Document status and date: Published: 01/01/2018 DOI: 10.26481/dis.20180702nd Document Version: Publisher's PDF, also known as Version of record Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. 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If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement: www.umlib.nl/taverne-license Take down policy If you believe that this document breaches copyright please contact us at: [email protected] providing details and we will investigate your claim. Download date: 30 Sep. 2021 BIOMARKER DISCOVERY IN HEART FAILURE Naser Davarzani BIOMARKER DISCOVERY IN HEART FAILURE DISSERTATION to obtain the degree of Doctor at Maastricht University, on the authority of the Rector Magnificus, Prof. dr. Rianne M. Letschert, in accordance with the decision of the Board of Deans, to be defended in public on Monday 2 July 2018 at 12:00 hours by Naser Davarzani Supervisors: Prof. dr. ir. R. L. M. Peeters Prof. H. P. Brunner-La Rocca Co-supervisors: Dr. E. N. Smirnov Dr. J. M. H. Karel Assessment Committee: Prof. dr. G. B. Weiss (chair) Prof. dr. I. C. W. Arts Prof. dr. G. Molenberghs (Hasselt University) Prof. dr. C. E. M¨uller (University Hospital Basel) Dr. V.P.M. van Empel This research was funded in part by the transnational University Limburg (tUL). Dissertation Series No. 2018-15 The research reported in this thesis has been carried out under the auspices of SIKS, the Dutch Research School for Information and Knowledge Systems. ISBN 978-94-6233-994-1 © Naser Davarzani, 2018. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronically, mechanically, photo- copying, recording or otherwise, without prior permission of the author. Contents Contents 1 1 Introduction 3 1.1 Conceptual Framework 5 1.2 Objectives of the thesis 6 1.3 Structure of the Thesis 9 2 Background 11 2.1 The TIME-CHF Study 12 2.2 Modelling Repeated Events 16 2.3 Bayesian Inference 18 2.4 Logistic-GEE Models 22 3 NT-proBNP-Guided Therapy Reduces Repeated Heart Failure Hos- pitalizations 25 3.1 Introduction 27 3.2 Methods 28 3.3 Results 29 3.4 Discussion 33 3.5 Conclusions 36 4 Cutpoint Estimation for Continuous Covariates Dichotomization 37 4.1 Introduction 39 4.2 Dichotomization Using Linear Regression Models 41 4.3 Simulation study 44 4.4 Application to the TIME-CHF Study 49 4.5 Discussion 52 5 Generalized Ranking Accuracy for Logistic-GEE Models 54 5.1 Introduction 56 5.2 Generalized Ranking Accuracy 59 5.3 Simulation study 65 5.4 Application to the TIME-CHF Study 75 5.5 Conclusion 78 2 Contents 6 Novel Concept to Guide Systolic Heart Failure Medication by Re- peated Biomarker Testing 80 6.1 Introduction 82 6.2 Methods 83 6.3 Results 86 6.4 Discussion 90 7 Conclusions 95 7.1 Answer to the Research Questions 96 7.2 Directions for Future Research 99 References 102 Publication List 118 Summary 120 Valorization 123 Acknowledgements 125 About the Author 127 SIKS Dissertation Series 128 1 Introduction 4 Chapter 1. Introduction Adequately utilized, data in the medical field can be an inexhaustible source of know- ledge to fuel a learning health care system and help clinicians in making the right decision. The design of data analysis methods in medical studies is changing fundamentally due to the complexities generated in medical data, such as having too many variables or missing data and more specifically, having repeated correlated measures in time. Therefore, the need to develop new methods to address questions that can deal with these complexit- ies is emerging. However, the issue with newly developed methods may be the lack of fully-fledged validation of these techniques that does not enable us be certain about the obtained results and to fully know how to extend the obtained results to future data. Heart failure (HF) is recognised as a frequent disease in Western countries that causes increasing public health problems and is the topic of many clinical studies. HF is the main cause of hospital admission among elderly in developed countries [51, 83] and it is very likely that prevalence will further increase in most countries [50, 72]. Regardless of the main causes of hospitalization, these admissions impose enormous health care costs related to relatively long hospital stays [81, 127]. Therefore, precisely diagnosing and monitoring progression of disease in HF patients may have a major role in reducing dis- ease burden and health care costs. Currently, diagnosing HF and monitoring patients are mainly based on clinical signs and symptoms. However, management of patients based on clinical signs and symptoms can be challenging as signs and symptoms of HF are not specific [111]. Moreover, monitoring progression of the disease can be very challenging and it is often impossible to select individuals who are most likely to need hospitalization, or to progress to mortality, and thus who are in need of more intensive treatment. There- fore, new tools are required to better monitor patients and to define the most effective treatment. Biomarkers are promising in this regard, as ideally, they predict outcome quite well. Moreover, some, e.g., Brain Natriuretic Peptide (BNP) and N-terminal pro-B-type Natriuretic Peptide (NT-proBNP), have shown to be of diagnostic value in HF [37, 146]. Due to the fact that biomarkers reflect different aspect of disease and state of body, they may not only have diagnostic and prognostic usages, but also therapeutic implications. In order to move towards individualized therapy, implementing biomarkers to guide man- agement of HF can help select patients in need of more intensive therapy and improve therapeutic decision making. So far, however, only very few biomarkers are being used in daily practice due to the lack of direct therapeutic consequences based on biomarker measurement. This thesis investigates the diagnostic and prognostic role of biomarkers and their us- ages in establishing a guidance in medical HF therapy in individual HF patients. The analyses are based on data feom the Trial of Intensified versus standard Medical therapy in Elderly patients with Congestive Heart Failure (TIME-CHF) [19, 102] study, as it con- tains multiple biomarker measurements over time together with detailed information on medication, allowing one to investigate the interaction between biomarkers and treatment 1.1. Conceptual Framework 5 effects. 1.1 Conceptual Framework 1.1.1 Definition of Heart Failure Heart failure (HF), sometimes referred to as Congestive Heart Failure (CHF), occurs when the heart muscle does not pump sufficient blood to fulfill the requirements of the body as well as it should. In a clinical definition, HF is a clinical syndrome with according symptoms and signs, such as edema (accumulation of fluid causing swelling in the leg), dyspnea (shortness of breath, excessive tiredness), fatigue, together with proof of an un- derlying functional and/or structural hearth disease [84, 85]. Problems of HF increase with age [116] and is the most important reason for hospit- alization in patients aged ≥ 65 years [83, 122]. HF has a high prevalence such that 1% to 2% of the population and up to 10% of the population aged ≥ 70 years suffer from CHF [90, 110]. HF hospitalizations have a great impact on disease burden, particularly quality of life, and on health care costs [81]. The diagnosis of HF is difficult, especially when relying on signs and symptoms only [111, 85]. In addition to physical examinations, diagnostic tests support the diagnosis of HF, e.g., chest X-ray laboratory tests, electrocardiography, and echocardiography. How- ever, diagnosing HF remains difficult, especially in elderly patients. This is due to the fact that in older age, HF patients usually have multiple comorbidities such as renal failure, diabetes, pulmonary disorders, cancer, anemia, cerebrovascular and peripheral arterial occlusive disease. In addition to comorbidities, in elderly patients various other factors such as sedentary lifestyle may mitigate the signs of HF. Hence investigating the relation- ship between other markers, e.g. biomarkers, and the severity and diagnosis of HF seems necessary and helpful, especially in elderly patients. 1.1.2 Biomarkers in Heart Failure Biomarkers usually refer to measurable circulating markers in the blood that may indicate some biological processes in the body.