Generating Reliable and Responsive Observational Evidence: Reducing Pre-analysis Bias

A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators. This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator. We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians’ information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service

A Systems-Level Approach to Understand The Seasonal Factors Of Early Development With Clinical and Pharmacological Applications

Major developmental defects occur in 100,000 to 200,000 children born each year in the United States of America. 97% of these defects are from unidentified causes. Many fetal outcomes (e.g., developmental defects), result from interactions between genetic and environmental factors. The lifetime effects from prenatal exposures with low impact (e.g., air pollution) are often understudied. Even when these exposures are studied, the focus is often placed on immediate effects of the exposure (e.g., fetal anomalies, miscarriage rates) leaving lifetime effects largely unexplored. This makes prolonged (or lifetime) effects of low-impact exposures an understudied research area. Included in this set of low-impact exposures is seasonal variance at birth. This thesis measures the effects of seasonal variance at birth on lifetime disease risk at both the population-level and molecular-levels. Four aims, comprising this thesis study, were conducted that utilize data from pharmacology, clinical care (Electronic Health Records) and genetics. These aims included: 1.) Development of an Algorithm to Reveal Diseases with a Prenatal/Perinatal Seasonality Component (described in chapter 2); 2.) Investigation of Climate Variables that Affect Lifetime Disease Risk By Altering Environmental Drivers (described in chapters 3 and 4); 3.) Discovery of Genes Involved in Birth Season – Disease Effects (described in chapter 5) and 4.) Investigation of Pharmacological Inhibitors As Phenocopies of the Birth Season – Disease Effect (described in chapters 6 and 7). Knowledge gained from these four areas, through seven distinct studies, establishes that birth season is a causal risk factor in a number of common diseases including cardiovascular diseases

Machine Learning Methods for Causal Inference with Observational Biomedical Data

Causal inference -- the process of drawing a conclusion about the impact of an exposure on an outcome -- is foundational to biomedicine, where it is used to guide intervention. The current gold-standard approach for causal inference is randomized experimentation, such as randomized controlled trials (RCTs). Yet, randomized experiments, including RCTs, often enforce strict eligibility criteria that impede the generalizability of causal knowledge to the real world. Observational data, such as the electronic health record (EHR), is often regarded as a more representative source from which to generate causal knowledge. However, observational data is non-randomized, and therefore causal estimates from this source are susceptible to bias from confounders. This weakness complicates two central tasks of causal inference: the replication or evaluation of existing causal knowledge and the generation of new causal knowledge. In this dissertation I (i) address the feasibility of observational data to replicate existing causal knowledge and (ii) present new methods for the generation of causal knowledge with observational data, with a focus on the causal tasks of comparing an outcome between two cohorts and the estimation of attributable risks of exposures in a causal system

Emerging Methods for Use of Real-World Clinical Data for Cardiovascular Outcomes Research

Title from PDF of title page, viewed September 14, 2022Dissertation advisors: Kim G. Smolderen and John A. SpertusVitaIncludes bibliographical references (pages 71-77)Dissertation (Ph.D.)--Department of Biomedical and Health Informatics, Department of Global Entrepreneurship and Innovation. University of Missouri--Kansas City, 2021The purpose of this dissertation is to describe methods for use of real-world data resources to study quality of care and outcomes for patients with critical limb ischemia. We used the Cerner Health Facts de-identified EHR database to 1) exclude patient records except those with critical limb ischemia from clinical sites in the Health Facts database, 2) document variability in patient outcomes after critical limb ischemia care, and 3) document variability in evidence-based medical therapy for the treatment of critical limb ischemia. We derived a data mart from the Health Facts database and identified 31,490 unique patients seen in 79,359 unique encounters at 233 unique clinical sites in the Health Facts database between 2010 and 2017. Of these, 20,204 encounters included endovascular peripheral vascular intervention. Within 30 days of the intervention, 2.8% of patient encounters resulted in a major amputation. We documented the association of modifiable patient factors with 30-day amputation and significant variation in 30-day amputation rates at the clinical site level. In addition to procedural quality outcomes, we examined rates of guideline directed medical therapy—medications indicated to reduce risk of adverse outcomes in all patients with critical limb ischemia. Only 27.2% of patient encounters documented complete medical therapy while 72.4% documented some component of therapy. As with 30-day amputation outcomes, rates of the medical therapy quality metric varied widely between sites with a median rate of 38.2% and interquartile range of 16.3-60.1%. This work demonstrates the use of a national, EHR database for cardiovascular outcomes research. We documented 30-day amputation outcomes after peripheral vascular intervention--a metric of CLI treatment outcomes. We also documented quality of care—guideline directed medical therapy—surrounding an inpatient encounter for CLI. We documented site variability for both treatment outcomes and quality of care to inform future quality improvement work in the treatment of CLI nationally.General introduction -- Aim 1: Methods for identifying clinical sites with comprehensive data for cardiovascular outcomes research in a national electronic health record database -- Aim 2A: Variability in 30-day major amputation rates following endovascular peripheral vascular intervention for critical limb Ischemia -- Aim 2B: Rates of guideline directed medical therapy in patients with critical limb Ischemia and major amputation -- Assessing patient preferences for shared decision-making in peripheral artery disease -- General discussio

The Secondary Use of Longitudinal Critical Care Data

Aims To examine the strengths and limitations of a novel United Kingdom (UK) critical care data resource that repurposes routinely collected physiological data for research. Exemplar clinical research studies will be developed to explore the unique longitudinal nature of the resource. Objectives - To evaluate the suitability of the National Institute for Health Research (NIHR) Critical Care theme of the Health Informatics Collaborative (CCHIC) data model as a representation of the Electronic Health Record (EHR) for secondary research use. - To conduct a data quality evaluation of data stored within the CC-HIC research database. - To use the CC-HIC research database to conduct two clinical research studies that make use of the longitudinal data supported by the CC-HIC: - The association between cumulative exposure to excess oxygen and outcomes in the critically ill. - The association between different morphologies of longitudinal physiology—in particular organ dysfunction—and outcomes in sepsis. The CC-HIC The EHR is now routinely used for the delivery of patient care throughout the United Kingdom (UK). This has presented the opportunity to learn from a large volume of routinely collected data. The CC-HIC data model represents 255 distinct clinical concepts including demographics, outcomes and granular longitudinal physiology. This model is used to harmonise EHR data of 12 contributing Intensive Care Units (ICUs). This thesis evaluates the suitability of the CC-HIC data model in this role and the quality of data within. While representing an important first step in this field, the CC-HIC data model lacks the necessary normalisation and semantic expressivity to excel in this role. The quality of the CC-HIC research database was variable between contributing sites. High levels of missing data, missing meta-data, non-standardised units and temporal drop out of submitted data are amongst the most challenging features to tackle. It is the principal finding of this thesis that the CC-HIC should transition towards implementing internationally agreed standards for interoperability. Exemplar Clinical Studies Two exemplar studies are presented, each designed to make use of the longitudinal data made available by the CC-HIC and address domains that are both contemporaneous and of importance to the critical care community. Exposure to Excess Oxygen Longitudinal data from the CC-HIC cohort were used to explore the association between the cumulative exposure to excess oxygen and outcomes in the critically ill. A small (likely less than 1% absolute risk reduction) dose-independent association was found between exposure to excess oxygen and mortality. The lack of dosedependency challenges a causal interpretation of these findings. Physiological Morphologies in Sepsis The joint modelling paradigm was applied to explore the different longitudinal profiles of organ failure in sepsis, while accounting for informative censoring from patient death. The rate of change of organ failure was found to play a more significan't role in outcomes than the absolute value of organ failure at a given moment. This has important implications for how the critical care community views the evolution of physiology in sepsis. DECOVID The Decoding COVID-19 (DECOVID) project is presented as future work. DECOVID is a collaborative data sharing project that pools clinical data from two large NHS trusts in England. Many of the lessons learnt from the prior work with the CC-HIC fed into the development of the DECOVID data model and its quality evaluation

Phenome-wide association study (PheWAS) on the genetic determinants of serum urate level and disease outcomes in UK Biobank

IntroductionElevated serum uric acid (SUA) concentration, known as hyperuricaemia, is a common abnormity in individuals with metabolic disorders. There is increasing evidence supporting the link between high SUA level and the increased risk of a wide range of clinical disorders, including hypertension, cardiovascular diseases (CVD), chronic renal diseases and metabolic syndrome. Although there are considerable research efforts in understanding the pathogenic pathways of high SUA level and the related clinical consequences, their causal relationships have not been established except for gout. Like other complex traits, genetic determinants play a substantial role (an estimated heritability of 40-70%) in the regulation of SUA level. Investigating the role of genetic variants related to SUA in various diseases might provide evidence for the above hypothesis which links uric acid to clinical disorders. Method Umbrella review was carried out first to provide a comprehensive overview on the range of health outcomes in relation to SUA level by incorporating evidence from systematic reviews and meta-analyses of observational studies, meta-analyses of randomised controlled trials (RCTs), and Mendelian randomisation (MR) studies. The umbrella review summarised the range of related health outcomes, the magnitude, direction and significance of identified associations and effects, and classified the evidence into four categories (class I [convincing], II [highly suggestive], III [suggestive], and IV [weak]) with assessment of multiple sources of biases. Then, a MR-PheWAS (Phenome-wide association study incorporated with Mendelian randomisation [MR]) was performed to investigate the associations between the 31 SUA genetic risk variants and a very wide range of disease outcomes by using the interim release data of UK Biobank (n=120,091). The SUA genetic risk loci were employed as instruments individually. The framework of phenome was defined by the PheCODE schema using the International Classification of Diseases (ICD) diagnosis codes documented in the health records of UK Biobank. Phenome-wide association test was performed first to identify any association across the SUA genetic risk loci and the phenome; MR design and HEIDI (heterogeneity in dependent instruments) tests were then applied to distinguish the PheWAS associations that were due to causality, pleiotropy or genetic linkage.To validate the MR-PheWAS findings, an enlarged Phenome-wide Mendelian randomisation (PWMR) analysis were performed by using data from the full UK Biobank cohort (n=339,256). A weighted polygenic risk score (GRS), incorporating effect estimates of multiple genetic risk loci, was employed as a proxy of the SUA level. The framework of phenome was defined by both the PheCODE schema and an alternative Tree-structured phenotypic model (TreeWAS) for analysis. Significant associations from these analyses were taken forward for replication in different populations by analysing data from various GWAS consortia documented in the MR-base database. Sensitivity analyses examining the pleiotropic effects of urate genetic risk loci on a set of metabolic traits were performed to explore any causal effects and pleiotropic associations.ResultsThe umbrella review included 101 articles and comprised 144 meta-analyses of observational studies, 31 meta-analyses of randomised controlled trials and 107 Mendelian randomisation studies. This remarkable assembly of evidence explored 136 unique health outcomes and reported convincing (class I) evidence for the causal role of SUA in gout and nephrolithiasis. Furthermore, highly suggestive (class II) evidence was reported for five health outcomes, in which high SUA level was associated with increased risk of heart failure, hypertension, impaired fasting glucose or diabetes, chronic kidney disease, and coronary heart disease mortality in the general population. The remaining 129 associations were classified as either suggestive or weak. The MR-PheWAS (using the interim release cohort) identified 25 disease groups/ outcomes to be associated with SUA genetic risk loci after multiple testing correction (p<8.6 ×10-5). The MR IVW (inverse variance weighted) analysis implicated a causal role of SUA level in three disease groups: inflammatory polyarthropathies (OR=1.22, 95% CI: 1.11 to 1.34), hypertensive disease (OR=1.08, 95% CI: 1.03 to 1.14) and disorders of metabolism (OR=1.07, 95% CI: 1.01 to 1.14); and four disease outcomes: gout (OR=4.88, 95% CI: 3.91 to 6.09), essential hypertension (OR=1.08, 95% CI: 1.03 to 1.14), myocardial infarction (OR=1.16, 95% CI: 1.03 to 1.30) and coeliac disease (OR=1.41, 95% CI: 1.05 to 1.89). After balancing pleiotropic effects in MR Egger analysis, only gout and its encompassing disease group of inflammatory polyarthropathies were considered to be causally associated with SUA level. The analysis also highlighted a locus (ATXN2/S2HB3) that may influence SUA level and multiple cardiovascular and autoimmune diseases via pleiotropy.The PWMR analysis, using data from the full UK Biobank cohort (n=339,256), examining the association with 1,431 disease outcomes, identified 13 phecodes that were associated with the weighted GRS of SUA level with the p value passing the significance threshold of PheWAS (p<3.4×10-4). These phecodes represent 4 disease groups: inflammatory polyarthropathies (OR=1.28; 95% CI: 1.21 to 1.35; p=4.97×10-19), hypertensive disease (OR=1.08; 95% CI: 1.05 to 1.11; p=6.02×10-7), circulatory disease (OR=1.05; 95% CI: 1.02 to 1.07; p=3.29×10-4) and metabolic disorders (OR=1.07; 95% CI: 1.03 to 1.11; p= 3.33×10-4), and 9 disease outcomes: gout (OR=5.37; 95% CI: 4.67 to 6.18; p= 4.27×10-123), gouty arthropathy (OR=5.11; 95% CI: 2.45 to 10.66; p=1.39×10-5), pyogenic arthritis (OR=2.10; 95% CI: 1.41 to 3.14; p=2.87×10-4), essential hypertension (OR=1.08; 95% CI: 1.05 to 1.11; p=6.62×10-7), coronary atherosclerosis (OR=1.10; 95% CI: 1.05 to 1.15; p=1.17×10-5), ischaemic heart disease (OR=1.10, 95% CI: 1.05 to 1.15; p=1.73×10-5), chronic ischaemic heart disease (OR=1.10, 95% CI: 1.05 to 1.15; p=1.52×10-5), myocardial infarction (OR=1.15, 95% CI=1.07 to 1.23, p=5.23×10-5), and hypercholesterolaemia (OR=1.08, 95% CI: 1.04 to 1.13, p=3.34×10-4). Findings from the TreeWAS analysis were generally consistent with that of PheWAS, with a number of more sub-phenotypes being identified. Results from IVW MR suggested that genetically determined high serum urate level was associated with increased risk of gout (OR=4.53, 95%CI: 3.64-5.64, p=9.66×10-42), CHD (OR=1.10, 95%CI: 1.02 to 1.19, p=0.009), myocardial infarction (OR=1.11, 95%CI:1.02 to 1.20, p=0.011) and decreased level of HDL-c (OR=0.93, 95%CI:0.88 to 0.98, p=0.004), but had no effect on RA (OR=0.92, 95%CI: 0.84 to 1.01, p=0.085) and ischaemic stroke (OR=1.03, 95%CI: 0.93 to 1.14, P= 0.582). Egger MR indicated pleiotropic effects on the causal estimates of DBP (P_pleiotropy=0.014), SBP (P_pleiotropy=0.003), CHD (P_pleiotropy=0.008), myocardial infarction (P_pleiotropy=0.008) and HDL-c (P_pleiotropy=0.016). When balancing out the potential pleiotropic effects in Egger MR, a causal effect can only be verified for gout (OR=4.17, 95%CI: 3.03 to 5.74, P_effect=1.27×〖10〗^(-9); P_pleiotropy=0.485). Sensitivity analyses on the GRSs of different groups of pleiotropic loci support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observed associations with cardiovascular/metabolic diseases. ConclusionsThis thesis presents a comprehensive investigation on the health outcomes in relation to SUA level. The causal relationship between high SUA level and gout is robustly verified in this thesis with consistent evidence from the umbrella review, the MR-PheWAS and the PWMR. The association of high SUA level with hypertension and heart diseases is supported by both the evidence from umbrella review and analyses conducted in this thesis, however, given the caveat of pleiotropy in the causal inference, a conclusion of causality on hypertension and heart diseases is not robust enough based on the current findings. Furthermore, the epidemiological evidence from the umbrella review indicated that high SUA level was associated with several components of metabolic disorders, and the analyses of the UK Biobank data identified a significant association with metabolic disorders and a sub-phenotype (hypercholesterolaemia). The causal inference in this study is limited by the common difficulty of pleiotropy caused by the use of multiple genetic instruments. Although we have performed sensitivity analysis by excluding the key pleiotropic locus, unmeasured pleiotropy and biases are still possible. In particular, unbalanced pleiotropy is recognised as an issue for the causal connections on the association between SUA level and hypertension. Other potential causal relevance of SUA level with respiratory diseases and ocular diseases is also worthy of further investigation. Overall, when taken together the findings from umbrella review, MR-PheWAS, PheWAS/TreeWAS analysis, MR replication and sensitivity analysis conducted in this thesis, I conclude that there are robust associations between urate and several disease groups, including gout, hypertensive diseases, heart diseases and metabolic disorders, but the causal role of urate only exists in gout. This study indicates that the observed associations between urate and cardiovascular/metabolic diseases are probably derived from the pleiotropic effects of genetic variants on urate and metabolic traits. Further investigation of therapies targeting the shared biological pathways between urate and metabolic traits may be beneficial for the treatment of gout and the primary prevention of cardiovascular/metabolic diseases

Enabling cardiovascular multimodal, high dimensional, integrative analytics

While traditionally the understanding of cardiovascular morbidity relied on the acquisition and interpretation of health data, the advances in health technologies has enabled us to collect far larger amount of health data. This thesis explores the application of advanced analytics that utilise powerful mechanisms for integrating health data across different modalities and dimensions into a single and holistic environment to better understand different diseases, with a focus on cardiovascular conditions. Different statistical methodologies are applied across a number of case studies supported by a novel methodology to integrate and simplify data collection. The work culminates in the different dataset modalities explaining different effects on morbidity: blood biomarkers, electrocardiogram recordings, RNA-Seq measurements, and different population effects piece together the understanding of a person morbidity. More specifically, explainable artificial intelligence methods were employed on structured datasets from patients with atrial fibrillation to improve the screening for the disease. Omics datasets, including RNA-sequencing and genotype datasets, were examined and new biomarkers were discovered allowing a better understanding of atrial fibrillation. Electrocardiogram signal data were used to assess the early risk prediction of heart failure, enabling clinicians to use this novel approach to estimate future incidences. Population-level data were applied to the identification of associations and temporal trajectory of diseases to better understand disease dependencies in different clinical cohorts