Abstract:
Structure of the thesis. The work comprises 228 pages of electronic text and is structured into: an introduction, six chapters of original research, conclusions and recommendations, a bibliographic index (369 titles), 65 figures, 36 tables and 16 annexes. The research findings have been presented in xx scientific publications. Keywords: heart failure, phenotyping, comorbidities, cardiac remodeling, advanced imaging, artificial intelligence, evaluation matrix, cardiovascular risk. Field of study: Cardiology.
Purpose of the study. To elucidate the clinical-evolutionary features and management strategies of chronic heart failure through the integration of advanced echocardiographic techniques and artificial intelligence algorithms, with the aim of developing a individualized evaluation matrix.
Objectives of the study. To describe the clinical, hemodynamic, biological and evolutionary characteristics of patients with chronic heart failure; to identify specific types and phenotypes of heart failure and analyze their clinical evolution; to assess the role of conventional and advanced echocardiography in diagnosis, risk stratification and follow-up; to evaluate the impact of comorbidities on clinical presentation, hemodynamics and outcomes; to analyze therapeutic strategies applied to heart failure patients in comparison with current international guidelines; to assess the contribution of artificial intelligence tools to risk stratification across different heart failure phenotypes; and to develop an integrated digital matrix for individualized management by combining clinical-hemodynamic parameters, advanced echocardiographic techniques and artificial intelligence algorithms.
Scientific novelty and originality. The thesis investigates heart failure from a multidimensional perspective, integrating clinical data with advanced imaging and analytical methods, including multivariable modelling and unsupervised clustering. An original individualized evaluation matrix has been developed, capable of differentiating clinically relevant phenotypes and identifying patients at high risk. The study introduces an innovative model that correlates cardiac remodeling with comorbidities, biomarkers, and speckle-tracking parameters, achieving for the first time a systematic integration of clinical, metabolic and imaging indicators within a unified analytical architecture with direct applicability in risk stratification.
The important scientific problem solved. The thesis establishes and validates a holistic model for assessing heart failure, enabling the definition of clinical phenotypes and the identification of patients at increased risk of hospitalization and mortality, thereby supporting individualized therapeutic decision-making.
Theoretical significance. The findings elucidate contemporary mechanisms of heart failure evolution in the context of comorbidities and contribute to updating phenotyping concepts through the integration of imaging, clinical and functional markers. The work deepens understanding of the relationship between cardiac remodeling, ventricular dysfunction and the heterogeneity of disease phenotypes.
Practical value. The developed evaluation matrix provides a practical tool for risk stratification, treatment individualization and optimization of clinical management. The results can be applied in professional training programs, clinical registries and the development of digital decision-support instruments. Implementation of results. The outcomes have been incorporated into the teaching and clinical activities of the Cardiology Departments of the “Nicolae Testemițanu” State University of Medicine and Pharmacy, in the clinical practice of the Institute of Cardiology, and in initiatives aimed at digitalizing cardiac patient evaluation, forming the basis for algorithmic prototypes for risk stratification in heart failure patients.
