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Life expectancy of patients with congenital heart disease (CHD) has increased in recent decades; however, late complications remain frequent and difficult to predict. Progress in data science has spurred the development of decision support systems and could aid physicians in predicting clinical deterioration and in the management of CHD patients. Newly developed artificial intelligence (AI) algorithms have shown performances comparable to humans in clinical diagnostics using statistical and computational algorithms and are expected to partly surpass human intelligence in the near future. Although much research on AI has been performed in patients with acquired heart disease, little data is available with respect to research on AI in patients with CHD. Learning algorithms in patients with CHD have shown to be promising in the interpretation of ECG, cardiac imaging, and the prediction of surgical outcome. However, current learning algorithms are not accurate enough to be implemented into daily clinical practice. Data on AI possibilities remain scarce in patients with CHD, and studies on large data sets are warranted to increase sensitivity, specificity, accuracy, and clinical relevance of these algorithms.

Background: Ventricular systolic and diastolic function, as measured by echocardiography, are diminished in patients with a systemic right ventricle (RV). As the clinical implications of these finding remained unknown, we aimed to identify echocardiographic parameters of systolic and diastolic ventricular function that are independent determinants of the clinical condition in these patients. Methods: Forty-six adult patients (61% male; mean age 33 [range 18-69] years) with a systemic RV underwent echocardiography to assess qualitative and quantitative systolic and diastolic function of the systemic RV and the subpulmonary left ventricle (LV). Uni- and multivariate linear regression analyses were performed to identify independent echocardiographic determinants for NYHA class, maximal exercise capacity (V'O-2peak) and NT-proBNP levels. Results: We found qualitative assessment of RV and LV function to be significantly associated with NYHA class (RV: beta = 0.26; P = 0.05 and LV: beta = 0.82; P

In this thesis we investigate different aspects of eHealth for hypertension and cardiovascular disease, with a focus on remote monitoring programs for chronic care. We use the Dutch HartWacht program for patients with hypertension, cardiac arrhythmias and heart failure as an example that has been implemented in routine clinical care. We first focus on hypertension and identify areas that are attractive for future implementation of eHealth because of poor hypertension control. In the following chapters we present economical, legal and technical challenges that accompany eHealth implementation, in each chapter followed by potential solutions and opportunities. We identify success factors for cost-effective eHealth, provide a roadmap for GDPR-compliant solutions, present a novel technique for heartbeat detection through a bracelet and describe a protocol for efficient data handling in remote monitoring programs. In the second part of this thesis, we zoom in on the patients participating in eHealth programs. We evaluate the impact on quality of life of patients participating in the HartWacht program for cardiac arrhythmias and demonstrate equivalence compared to usual care. We then describe the feasibility of the HartWacht program for patients with hypertension in reducing blood pressure and present rationale, design and cohort profile of the Effectiveness of home-Monitoring of blood pressure in PAtients with difficult to Treat HYpertension (EMPATHY) trial. We conclude with an evaluation of the impact of the COVID-19 pandemic on the uptake of eHealth in primary care in the Netherlands.