Application of Bayesian Networks to Risk Assessment

Various approaches are used to estimate and predict risks. One of the most prevalent methods for risk assessment is the Cox's proportional hazard (CPH) model (Cox, 1972), a popular statistical technique used in risk estimation and survival analysis. The weaknesses of this approach are: (1) the underlying model can be only learned from data and is not readily amenable to refinement based on expert knowledge (2) the CPH model rests on several assumptions simplifying the interactions between the risk factors and the predicted outcome. While these assumptions are reasonable and the CPH model has been successfully used for decades, it is interesting to question them with a possible benefit in terms of model accuracy. This dissertation focuses on theoretical and practical aspects of risk assessment based on Bayesian networks (Pearl, 1988) as an alternative approach to the CPH model. The dissertation makes three contributions: (1) I propose a Bayesian network interpretation of the CPH (BN-Cox) model, a process of using existing CPH models as data sources for parameter estimation in Bayesian networks when original data are not available, and discuss methods for modeling such model computationally tractable (2) I empirically demonstrate in both context-sensitivity of the strength of influences of individual risk factors on the outcome variables in both Bayesian network model and the CPH model, and finally, (3) I propose and evaluate methods for enhancing the quality of Bayesian network parameters learned from small data sets, by means of priors

Cost effectiveness of first-line oral therapies for pulmonary arterial hypertension: A modelling study

Background: In recent years, a significant number of costly oral therapies have become available for the treatment of pulmonary arterial hypertension (PAH). Funding decisions for these therapies requires weighing up their effectiveness and costs. Objective: The aim of this study was to assess the cost effectiveness of monotherapy with oral PAH-specific therapies versus supportive care as initial therapy for patients with functional class (FC) II and III PAH in Canada. Methods: A cost-utility analysis, from the perspective of a healthcare system and based on a Markov model, was designed to estimate the costs and quality-adjusted life-years (QALYs) associated with bosentan, ambrisentan, riociguat, tadalafil, sildenafil and supportive care for PAH in treatment-naïve patients. Separate analyses were conducted for cohorts of patients commencing therapy at FC II and III PAH. Transition probabilities, based on the relative risk of improving and worsening in FC with treatment versus placebo, were derived from a recent network meta-analysis. Utility values and costs were obtained from published data and clinical expert opinion. Extensive sensitivity analyses were conducted. Results: Analysis suggests that sildenafil is the most cost-effective therapy for PAH in patients with FC II or III. Sildenafil was both the least costly and most effective therapy, thereby dominating all other treatments. Tadalafil was also less costly and more effective than supportive care in FC II and III; however, sildenafil was dominant over tadalafil. Even given the uncertainty within the clinical inputs, the probabilistic sensitivity analysis showed that apart from sildenafil and tadalafil, the other PAH therapies had negligible probability of being the most cost effective. Conclusion: The results show that initiation of therapy with sildenafil is likely the most cost-effective strategy in PAH patients with either FC II or III disease.This research was supported by funds from the Canadian Agency for Drugs and Technologies in Health (CADTH)

C-Reactive Protein Polymorphism and Serum Levels as an Independent Risk Factor in Sickle Cell Disease

This study explored the relationship of a dinucleotide repeat polymorphism in the intron of the CRP gene and serum CRP levels as independent risk factors for end-organ dysfunction (mild vs. severe) in adults with sickle cell disease. The pathogenesis of secondary complications of sickle cell disease is complex and poorly understood. Predicting the severity of these complications could assist in therapeutic decision-making. The study measured serum CRP levels and the number of CA intron repeats located on the CRP gene in 29 adults (31.74 ± 11.54 years) with sickle cell disease The hemoglobin genotypes were distributed as Hgb SS 48.6% (17 of n = 29), Hgb SC 20.0% (7 of n = 29), Sβ° 10.3% (3 of n = 29), and Sβ+ 6.9% (2 of n = 29). The sample was categorized as mild (n = 9) no end-organ dysfunction vs. severe (n = 21) documented end-organ dysfunction. The severe group was sub-categorized by specific organ dysfunctions, 9 with pulmonary hypertension, 6 with renal dysfunction and 6 with cerebral vascular accident. Examination of serum CRP levels found no significant association with severe end-stage organ dysfunction. There was no significant association between serum CRP level and the polymorphism. However, a significant negative correlation (rho = -0.401, p = 0.031) was found between glomerular filtration rates and CAhigh repeats (≥17). Previous studies have found an association of genetic variations in the CRP gene polymorphism to serum CRP levels. While this pilot study found no evidence of this association, the findings provide some rationale for further investigation of the repeat polymorphism in the CRP gene and its association with renal end-organ dysfunction

Molecular Risk Factors of Pulmonary Arterial Hypertension

The overall objective of the research presented in this dissertation was to investigate molecular risk factors of susceptibility to estrogenic chemicals, polychlorinated biphenyls (PCBs), hormone replacement therapy, and oral contraceptives and how that leads to the development of pulmonary arterial hypertension (PAH). Environmental and molecular risk factors for PAH are not clearly understood. This is a major hurdle for the development of new therapy against PAH as well as understanding individual susceptibility to this disease. Gender has been shown to impact the prevalence of PAH. Although controversial, estrogens have been implicated to be a risk factor for PAH. Thus, we hypothesize that women exposed to estrogenic chemicals are at increased risk of developing PAH when endocrine disrupting chemicals interact with unopposed estrogen to worsen pulmonary arterial disease. In support of this hypothesis, we have accomplished the following: Microarray data on PAH were collected and subsequent meta-analysis was conducted using genome-wide association and environment-wide association approaches on published studies as well as GEO and NHANES data. All PCB geometric mean concentrations found higher levels in people at risk of PAH than people not at risk of PAH. The sum of non-dioxin-like PCBs and the sum of dioxin-like PCBs were significantly higher in people at risk of PAH than people not at risk of PAH. Also, different levels of LOD (including PCBs concentration \u3eLOD, \u3e 50th percentile, 50th-75th percentile, and ≥75th percentile) were significantly higher in people at risk of PAH than people not at risk of PAH. We reported that females used estrogen pills and oral contraceptive were associated with risk of PAH. However, females used progestin and estrogen/progestin pills were not at risk of PAH. Molecular risk factor analysis using machine learning approaches revealed that VAMP2, LAMA5, POLR2C, VEGFB, and PRKCH genes are causal genes of PAH pathogenesis. Gene ontology and pathway analysis of PAH showed that genes involved in the apoptosis pathway, p53 pathway, Ras Pathway, T-cell activation, TGF-beta pathway, VEGF pathway, and Wnt pathway appear to be significantly associated with PAH. Documenting the exposure to estrogenic chemicals among the general U.S. population, and identifying agents and molecular risk factors associated with PAH have the potential to fill research gaps and facilitate our understanding of the complex role environmental chemicals play in producing toxicity in the lungs

A genome-wide association study in chronic thromboembolic pulmonary hypertension and the ADAMTS13-VWF axis

Chronic thromboembolic pulmonary hypertension (CTEPH) is an important and severe consequence of pulmonary embolism (PE), resulting from failure of thrombus resolution. Identifying genetic risk factors for CTEPH would provide important insights into pathobiology and might allow risk-stratification following PE. A genome-wide association study (GWAS) was performed in 1250 CTEPH patients, 1492 healthy controls and ~7 million single-nucleotide polymorphisms to identify novel disease loci. The ABO locus was identified as the most significant common variant genetic association with CTEPH in both a discovery and validation cohort. The A1 subgroup of ABO was enriched in CTEPH and this may result in multiple functional consequences including variation in plasma von Willebrand factor (VWF) levels. Abnormalities in haemostasis are implicated in CTEPH pathobiology, including elevated levels of VWF, which is cleaved by ADAMTS13 (a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13). The ADAMTS13-VWF axis was investigated in 208 CTEPH patients including its relationship to ABO blood groups and ADAMTS13 genetic variants. Plasma ADAMTS13 levels are markedly reduced in CTEPH. This is independent of pulmonary hypertension, disease severity or systemic inflammation. Plasma VWF levels were confirmed to be markedly increased in CTEPH. These findings implicate dysregulation of the ADAMTS13-VWF axis in CTEPH pathobiology

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

<p>Abstract</p> <p>Background</p> <p>Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.</p> <p>Methods</p> <p>Eight models were developed: Bayes linear and quadratic models, <it>k</it>-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.</p> <p>Results</p> <p>Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and <it>k</it>-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, <it>k</it>-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.</p> <p>Conclusion</p> <p>Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.</p

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19

Integrative Multiomics to Dissect the Lung Transcriptional Landscape of Pulmonary Arterial Hypertension

Pulmonary arterial hypertension (PAH) remains an incurable and often fatal disease despite currently available therapies. Multiomics systems biology analysis can shed new light on PAH pathobiology and inform translational research efforts. Using RNA sequencing on the largest PAH lung biobank to date (96 disease and 52 control), we aim to identify gene co-expression network modules associated with PAH and potential therapeutic targets. Co-expression network analysis was performed to identify modules of co-expressed genes which were then assessed for and prioritized by importance in PAH, regulatory role, and therapeutic potential via integration with clinicopathologic data, human genome-wide association studies (GWAS) of PAH, lung Bayesian regulatory networks, single-cell RNA-sequencing data, and pharmacotranscriptomic profiles. We identified a co-expression module of 266 genes, called the pink module, which may be a response to the underlying disease process to counteract disease progression in PAH. This module was associated not only with PAH severity such as increased PVR and intimal thickness, but also with compensated PAH such as lower number of hospitalizations, WHO functional class and NT-proBNP. GWAS integration demonstrated the pink module is enriched for PAH-associated genetic variation in multiple cohorts. Regulatory network analysis revealed that BMPR2 regulates the main target of FDA-approved riociguat, GUCY1A2, in the pink module. Analysis of pathway enrichment and pink hub genes (i.e. ANTXR1 and SFRP4) suggests the pink module inhibits Wnt signaling and epithelial-mesenchymal transition. Cell type deconvolution showed the pink module correlates with higher vascular cell fractions (i.e. myofibroblasts). A pharmacotranscriptomic screen discovered ubiquitin-specific peptidases (USPs) as potential therapeutic targets to mimic the pink module signature. Our multiomics integrative study uncovered a novel gene subnetwork associated with clinicopathologic severity, genetic risk, specific vascular cell types, and new therapeutic targets in PAH. Future studies are warranted to investigate the role and therapeutic potential of the pink module and targeting USPs in PAH