Facilitating and Enhancing Biomedical Knowledge Translation: An in Silico Approach to Patient-centered Pharmacogenomic Outcomes Research

Current research paradigms such as traditional randomized control trials mostly rely on relatively narrow efficacy data which results in high internal validity and low external validity. Given this fact and the need to address many complex real-world healthcare questions in short periods of time, alternative research designs and approaches should be considered in translational research. In silico modeling studies, along with longitudinal observational studies, are considered as appropriate feasible means to address the slow pace of translational research. Taking into consideration this fact, there is a need for an approach that tests newly discovered genetic tests, via an in silico enhanced translational research model (iS-TR) to conduct patient-centered outcomes research and comparative effectiveness research studies (PCOR CER). In this dissertation, it was hypothesized that retrospective EMR analysis and subsequent mathematical modeling and simulation prediction could facilitate and accelerate the process of generating and translating pharmacogenomic knowledge on comparative effectiveness of anticoagulation treatment plan(s) tailored to well defined target populations which eventually results in a decrease in overall adverse risk and improve individual and population outcomes. To test this hypothesis, a simulation modeling framework (iS-TR) was proposed which takes advantage of the value of longitudinal electronic medical records (EMRs) to provide an effective approach to translate pharmacogenomic anticoagulation knowledge and conduct PCOR CER studies. The accuracy of the model was demonstrated by reproducing the outcomes of two major randomized clinical trials for individualizing warfarin dosing. A substantial, hospital healthcare use case that demonstrates the value of iS-TR when addressing real world anticoagulation PCOR CER challenges was also presented

Risk assessment and mortality prediction in patients with venous thromboembolism using big data and machine learning.

Venous thromboembolism (VTE) is the third most common cardiovascular condition that affects mainly hospitalized and cancer patients and it is associated with high morbidity and mortality. Some patients need immediate treatment and monitoring in intensive care units (ICU). Moreover, cancer patients are at increased risk of developing VTE, especially in the immediate period after ICU hospitalization. It is crucial to predict which of the cancer patients will develop VTE, as well as early and late mortality in these high-risk patients and recognize possible treatable factors in order to improve survival. Several scoring and predictive models have been developed for these purposes, but with limited generalizability and they are mostly effective in the prediction of in-hospital mortality. They have several limitations, for example they use data recorded only on the first day of admission. Moreover, no score exists so far to predict late mortality in ICU patients. With the advanced use of electronic health records, open-source big- data medical databases and machine learning, predictive modelling could be utilized and become a powerful tool to guide clinical decision. The aim of the study was to explore the use and performance of various machine learning algorithms (ML) in order to predict two research questions: (i) VTE risk in ICU hospitalized cancer patients after discharge and, (ii) early and late mortality in VTE patients hospitalized in ICU. For that reason, a freely accessible database MIMIC-III has been used that contains a vast amount of various time-series healthcare data from thousands of patients, making it ideal for ML based forecasting. Since it provides information even after discharge from ICU, it gives an opportunity to predict late mortality. Two groups of datasets were extracted from the database: D1, consisted of 4,699 patients with cancer who were admitted to ICU and stratified in two groups based on whether they were readmitted to ICU within 90 days with a diagnosis of VTE or not. The ML classification task was to predict which of the cancer patients originally admitted to ICU will be readmitted with VTE within 90 days. D2, consisted of 2,468 patients who were admitted to ICU with a VTE diagnosis and stratified in three groups, based on their outcome, that is, died during their first ICU admission (early mortality group), died after their discharge from ICU or in a later admission (late mortality group) and remained alive for months after their admission in ICU. In this case, two ML classification tasks were constructed, first to build a model that distinguishes early mortality and second, a model that distinguishes late mortality. A very wide range of features were selected, that includes demographic information, clinical and laboratory data, prescriptions, procedures, well established comorbidity and severity scores as well as information coming from written notes. Clinically relevant entities from free medical notes were extracted using the sequence annotator SABER and then they were fitted into a Latent Dirichlet Allocation (LDA) model of 50 topics. In total, 1,471 features were extracted for each patient, grouped in 8 categories, each representing a different type of medical assessment. Automated ML platform that easily handles with-high dimensional, noisy and missing data, as well as Monte Carlo simulations based on Random Forests with hyperparameter tuning and class- balancing with Synthetic Minority Oversampling Technique (SMOTE) were trained in parallel. Due to the highly imbalanced nature of the first dataset (“cancer patients with thrombosis”), neither of the ML approaches were able to predict DVT in cancer patients even after the use of SMOTE method. As far as it concerns the prediction of early mortality in ICU patients with VTE, the best ML model chosen to predict early mortality was Random Forests (AUC=0,92). Regarding late mortality, the best ML model was again Random Forests. Nevertheless, the task of predicting late mortality was less efficient even with the holistic approach (AUC=0,82). Significant clinically relevant predictive features of early and late mortality were cancer, age, treatment with warfarin, and red cell transfusions, whereas known severity scores performed well only in the prediction of early mortality. The contribution of this study to the current knowledge was multi-leveled, as it explored the performance of various ML approaches in a big-data driven research approach, using multiple formats of data from structured to unstructured medical notes, it examined the effect of balancing techniques in highly imbalanced datasets, such as the case of medical datasets, and finally discovered possibly new biomarkers. Early mortality in critically-ill patients with VTE can be easily predicted by ML techniques, whereas in the case of late mortality, which is a more difficult task, and where medical scores are still lacking, ML could probably outperform classic statistical methods. There is a need for more precise and reliable tools in order to overcome the nature of highly imbalanced medical datasets, such as the case of “cancer patients with thrombosis” dataset. This study showed that automated ML approaches have similar performance with manual selection and parametrization of ML models, which is highly promising in the setting of healthcare “big-data” medical databases

Donor genetic variants as risk factors for thrombosis after liver transplantation:A genome-wide association study

Thrombosis after liver transplantation substantially impairs graft‐ and patient survival. Inevitably, heritable disorders of coagulation originating in the donor liver are transmitted by transplantation. We hypothesized that genetic variants in donor thrombophilia genes are associated with increased risk of posttransplant thrombosis. We genotyped 775 donors for adult recipients and 310 donors for pediatric recipients transplanted between 1993 and 2018. We determined the association between known donor thrombophilia gene variants and recipient posttransplant thrombosis. In addition, we performed a genome‐wide association study (GWAS) and meta‐analyzed 1085 liver transplantations. In our donor cohort, known thrombosis risk loci were not associated with posttransplant thrombosis, suggesting that it is unnecessary to exclude liver donors based on thrombosis‐susceptible polymorphisms. By performing a meta‐GWAS from children and adults, we identified 280 variants in 55 loci at suggestive genetic significance threshold. Downstream prioritization strategies identified biologically plausible candidate genes, among which were AK4 (rs11208611‐T, p = 4.22 × 10(−05)) which encodes a protein that regulates cellular ATP levels and concurrent activation of AMPK and mTOR, and RGS5 (rs10917696‐C, p = 2.62 × 10(−05)) which is involved in vascular development. We provide evidence that common genetic variants in the donor, but not previously known thrombophilia‐related variants, are associated with increased risk of thrombosis after liver transplantation

Towards a framework for comparing functionalities of multimorbidity clinical decision support: A literature-based feature set and benchmark cases.

Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guidelines offer conflicting advice. A number of research groups are developing computer-interpretable guideline (CIG) modeling formalisms that integrate recommendations from multiple Clinical Practice Guidelines (CPGs) for knowledge-based multimorbidity decision support. In this paper we describe work towards the development of a framework for comparing the different approaches to multimorbidity CIG-based clinical decision support (MGCDS). We present (1) a set of features for MGCDS, which were derived using a literature review and evaluated by physicians using a survey, and (2) a set of benchmarking case studies, which illustrate the clinical application of these features. This work represents the first necessary step in a broader research program aimed at the development of a benchmark framework that allows for standardized and comparable MGCDS evaluations, which will facilitate the assessment of functionalities of MGCDS, as well as highlight important gaps in the state-of-the-art. We also outline our future work on developing the framework, specifically, (3) a standard for reporting MGCDS solutions for the benchmark case studies, and (4) criteria for evaluating these MGCDS solutions. We plan to conduct a large-scale comparison study of existing MGCDS based on the comparative framework

Transforming Clinical Practice Guideline Usage Through the Use of a Clinical Decision Support System: An Explorative Study at the University Medical Centre Utrecht

Medical treatments require a lot of knowledge and skills. To safeguard the quality of healthcare in general, Clinical Practice Guidelines (CPG) are written. Different studies show that the quality of healthcare improves by using CPGs. Based on the advancements in IT, a CPG could best be supported through the use of a Clinical Decision Support System (CDSS). In this paper, we seek to transform the use of several CPGs with regards to anti-clotting medicine and treatments through the utilization of a CDSS at the University Medical Centre Utrecht (UMCU) in the Netherlands. Data analysis shows that many of the included CPGs overlap and that the utilization of a CDSS for the determination of anti-clotting medicine and treatments could result in more effective and efficient decision making. Additionally, during the validation of the CDSS, we derived the attitude of the stakeholders towards the use of a CPG in a pilot study comprising a CDSS and identified several success factors that should be taken into account when designing, validating, and implementing CPGs into CDSS

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