MARKERS OF CARDIOVASCULAR DYSFUNCTION AFTER PREGNANCIES COMPLICATED BY PRE-ECLAMPSIA

Pre-eclampsia (PE) is a hypertensive disorder of pregnancy characterized by maternal systemic endothelial dysfunction. While the clinical manifestations resolve soon after delivery, a large body of epidemiological evidence indicates significant long-term maternal risk for cardiovascular disease (CVD) after PE. The mechanisms by which PE and future CVD are associated are unclear, although shared constitutional risk factors likely contribute to the features of endothelial dysfunction characteristic to both. We postulate that PE offers a window of opportunity for the identification of unique markers of dysfunction in the earliest stages of disease that may be used to validate cardiovascular risk screening in the early postpartum period. The studies presented in this thesis provide evidence of changes in circulating factors in women with a recent history of PE. Using blood samples collected within the first year of pregnancy, unique patterns of microRNA expression, enrichment of coagulation system proteins and endothelial progenitor cell dysfunction were described. Many of the described changes appear to be independent of cardiovascular risk. In addition to alterations in circulating factors however, longitudinal postpartum assessments demonstrated that microvascular and cardiac abnormalities were evident in the early periods postpartum after a pre-eclamptic pregnancy. Collectively, the data presented in this thesis reveal that physiological alterations in women with a recent history of PE are not necessarily dependent on clinical parameters of cardiovascular risk, and that resulting dysfunction may be demonstrated within the first year postpartum. Importantly, the biomarkers presented herein are all demonstrated elsewhere in the literature to benefit from lifestyle modification and risk reduction. In closing, the findings of this thesis support a need for cardiovascular risk screening based on obstetrical history, namely after pregnancies complicated by PE.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.