Heart failure is a complex condition involving multiple organs beyond the cardiovascular system, all influencing disease progression and prognosis. Accurate risk assessment requires considering multiple variables, as no single parameter alone provides a complete prognostic picture. This has led to the development of prognostic models combining clinical and laboratory parameters. Some of these models incorporate cardiopulmonary exercise testing (CPET), which provides key prognostic indicators. Since the 1990s, CPET has been recommended in heart failure management guidelines due to its strong prognostic value when combined with clinical data. However, existing risk models often exclude important predictors such as ventilatory parameters from CPET (VE/VCO₂), renal function, and hemoglobin levels. To address this gap, in 2012 the investigators developed the MECKI (Metabolic Exercise test data combined with Cardiac and Kidney Indexes) score, integrating oxygen consumption, ventilatory efficiency, and easily accessible biochemical and echocardiographic parameters. Unlike previous models requiring extensive data collection, MECKI is based on only six variables, making it practical and effective. Recent studies suggest the need to update the cutoff values and parameters used for risk stratification, as new therapies and treatment strategies may significantly alter prognostic accuracy in different patient populations. This study aims to expand and refine the MECKI score by updating the patient dataset, optimizing its performance in specific subgroups, and aligning it with emerging therapeutic approaches. Additionally, the investigators will evaluate whether the model's risk accuracy varies in advanced-stage patients, those with comorbidities, or under different treatment regimens. This could lead to correction factors that enhance the score's predictive power across diverse clinical scenarios, further improving its applicability and reliability in heart failure management.