Sickle cell disease is a monogenic blood disorder in which the clinical course and disease severity vary widely among patients. In order for physicians to make more informed decisions regarding the treatment and management of disease, it would be useful to be able to predict disease severity. We focus on two primary modulators of disease severity in sickle cell patients, hemolysis and fetal hemoglobin (HbF). This dissertation evaluates methodology to identify genetic variants associated with severity of sickle cell disease and develops new methodology of genetic risk prediction to predict disease severity in sickle cell patients based on levels of HbF. Hemolysis is a trait that is influenced by multiple correlated phenotypes (lactate dehydrogenase, reticulocytes, bilirubin and aspartate transaminase). There are several approaches to statistical analyses of multiple correlated phenotypes. The first part of this dissertation evaluates the use of principal component analysis (PCA) and compares it to the alternative approach of examining the results of multiple univariate phenotypes individually. We will focus on the question of if and under what conditions we gain more power using a summarized phenotype from PCA in a genome wide association study (GWAS) rather than conducting multiple individual GWAS. We find that the there is more power gained from the PCA approach when there is a strong intercorrelation between the phenotypes. The second part of this dissertation proposes a novel method of genetic risk prediction for continuous traits using an ensemble of genetic models. We aim to show through a simulation and prediction of HbF that the proposed method is more robust to the inclusion of false positives and yields more stable predictions than computing a GRS and 10 fold cross validation. The third part of this dissertation introduces a Bayesian-based clustering approach to produce clusters of sickle cell anemia patients based on their "predicted genetic profiles" of HbF. We then examine the genetic profiles of individuals in the extreme clusters to determine which genes contribute more prominently to the genetic profile so that we may potentially identify genes that are highly influential in the regulation of extremely high and low values of HbF.

Background: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term 'phenotype' has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case 'phenotype pipeline' (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. Results: In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. Conclusions: We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies.

[ES] La fibrilación auricular (FA) es la arritmia cardíaca más común y afecta a más de 33 millones de pacientes en el mundo. A menudo se encuentra en unidades de cuidados intensivos, donde se asocia con hospitalizaciones prolongadas, mayores costos de atención médica, riesgo elevado de tromboembolismo y mayor mortalidad. La FA tiene diversas causas y mecanismos y se considera una enfermedad heterogénea. Puede ser causada por comorbilidades cardíacas y no cardíacas, como trastornos endocrinos, pulmonares y metabólicos, genética e inflamación. La abundancia de mecanismos fisiopatológicos asociados con la FA ha llevado a la comprensión de que los pacientes con FA son considerablemente heterogéneos. Esta heterogeneidad entre las poblaciones de pacientes se ha identificado previamente como un impedimento no abordado en los estudios epidemiológicos. Existen pautas para el tratamiento y manejo de la FA para la población general, pero no son directamente aplicables a las poblaciones de la UCI debido a los diferentes mecanismos, riesgos y efectividad de los tratamientos de la FA. Además, falta evidencia sólida sobre estrategias de tratamiento óptimas, lo que resulta en una falta de consenso entre los tomadores de decisiones clínicas y diferentes enfoques de tratamiento en las instituciones clínicas. Esta tesis doctoral informa el proceso de desarrollo de un método de estratificación para pacientes con FA en el entorno de cuidados críticos. Se desarrollan, comparan y emplean nuevos algoritmos de agrupamiento semisupervisados para identificar fenotipos de FA. Se comparan los efectos del tratamiento de fármacos antiarrítmicos comunes entre fenotipos y se realiza una evaluación de usabilidad para identificar la aplicabilidad clínica de los métodos desarrollados. / [CA] La fibril·lació auricular (FA) és l'arítmia cardíaca més comú i afecta més de 33 milions de pacients al món. Sovint es troba en unitats de cures intensives, on s'associa amb hospitalitzacions prolongades, majors costos d'atenció mèdica, risc elevat de tromboembolisme i més mortalitat. La FA té diverses causes i mecanismes i es considera una malaltia heterogènia. Pot ser causada per comorbiditats cardíaques i no cardíaques, com ara trastorns endocrins, pulmonars i metabòlics, genètica i inflamació. L'abundància de mecanismes fisiopatològics associats a la FA ha portat a la comprensió que els pacients amb FA són considerablement heterogenis. Aquesta heterogeneïtat entre les poblacions de pacients s'ha identificat prèviament com un impediment no abordat als estudis epidemiològics. Hi ha pautes per al tractament i maneig de la FA per a la població general, però no són directament aplicables a les poblacions de la UCI a causa dels diferents mecanismes, riscos i efectivitat dels tractaments de la FA. A més, manca evidència sòlida sobre estratègies de tractament òptimes, la qual cosa resulta en una manca de consens entre els prenedors de decisions clíniques i diferents enfocaments de tractament a les institucions clíniques. Aquesta tesi doctoral informa el procés de desenvolupament d'un mètode d'estratificació per a pacients amb FA a l'entorn de cures crítiques. Es desenvolupen, comparen i fan servir nous algorismes d'agrupament semisupervisats per identificar fenotips de FA. Es comparen els efectes del tractament de fàrmacs antiarítmics comuns entre fenotips i es fa una avaluació d'usabilitat per identificar l'aplicabilitat clínica dels mètodes desenvolupats. / [EN] Atrial fibrillation (AF) is the most commonly encountered cardiac arrhythmia, affecting over 33 million patients in the world. It is often encountered in intensive care units, where it is associated with prolonged hospitalisation, increased healthcare costs, elevated risk of thromboembolism, and higher mortality. AF has diverse causes and mechanisms, and is considered to be a heterogeneous disease. It may be caused by cardiac and non-cardiac comorbidities, such as endocrine, pulmonary, and metabolic disorders, genetics, and inflammation. The abundance of pathophysiological mechanisms associated with AF has led to the realization that AF patients are considerably heterogeneous. This heterogeneity among patient populations have previously been identified as an unaddressed impediment in epidemiological studies. Guidelines for the treatment and management of AF exist for the general population but are not directly applicable to ICU populations due to different AF mechanisms, risks, and effectiveness of treatments. Further, strong evidence for optimal treatment strategies is missing, resulting in a lack of consensus among clinical decision-makers, and different treatment approaches across clinical institutions. This doctoral thesis reports the process of developing a stratification method for AF patients in the critical care setting. Novel semi-supervised clustering algorithms are developed, benchmarked, and employed to identify AF phenotypes. Treatment effects of common antiarrhythmic drugs are compared among phenotypes, and a usability assessment is performed to identify the clinical applicability of the developed methods. / Lacki, AS. (2024). Clinical Decision Support System for the Multiparametric Stratification of Atrial Fibrillation Patients in Critical Care [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/212511