Heart failure is a complex condition affecting multiple organs beyond the cardiovascular system, influencing disease progression and prognosis. It has become increasingly evident that accurate risk assessment requires considering multiple variables, as no single parameter alone is sufficient for prognosis.
These findings have led to the identification and study of prognostic parameters that, when combined, allow for a more precise risk estimation and identification of high-risk patients. Various prognostic scores have been developed, utilizing algorithms that integrate multiple variables to estimate an individual's mortality risk. Some scores are based on clinical evaluation and comorbidities, others on laboratory findings, baroreflex sensitivity, heart rate, sleep abnormalities, echocardiographic imaging, or cardiopulmonary exercise testing (CPET), either alone or in combination with other factors.
CPET provides several parameters strongly correlated with prognosis. Since the 1990s, its use-alongside clinical data-has been recommended in heart failure management guidelines. More recently, in addition to peak oxygen consumption, the VE/VCO₂ slope has been recognized as a key prognostic marker, reflecting ventilatory efficiency and ventilation-perfusion mismatch, and has been included in heart transplant assessment criteria.
Current risk models in heart failure often omit important prognostic parameters, such as ventilatory indices from CPET, renal function, and hemoglobin levels. Among the numerous prognostic scores available, only the HF Survival Score (HFSS) and the HF Action Predictive Risk Score Model incorporate exercise-related parameters (peak VO₂ in the former and exercise duration in the latter), yet both neglect ventilatory aspects. Even the widely used Seattle Score does not include exercise-related variables.
In 2012, the researchers 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 just six key variables, making it both practical and effective.
Recent studies indicate the need to review and update the cutoff values and parameters used in prognostic models, as the introduction of new therapies and treatment strategies may significantly impact their predictive power in specific patient populations.
Study Objectives and Purpose The aim of this study is to expand and update the patient dataset to further develop the MECKI score, optimizing its application in patient subgroups and adapting it to new therapies and treatments introduced in clinical practice.
Additionally, the researchers seek to determine whether risk prediction accuracy varies in advanced-stage patients, those with comorbidities, or those receiving different treatments. This could lead to the development of correction factors for the MECKI score, improving its predictive power and applicability across different clinical scenarios.
Study Population Patients with systolic heart failure, consecutively enrolled and followed at multiple Heart Failure Units across Italy.
Patients undergo assessment through medical history collection, physical examination, laboratory tests, ECG, transthoracic echocardiography, and cardiopulmonary exercise testing (CPET).
Follow-up will be conducted according to the protocol of the respective Heart Failure Unit. The follow-up period ends at the last evaluation at the reference center, or upon the patient's death or heart transplantation.
