Elsevier

International Journal of Cardiology

Volume 167, Issue 6, 10 September 2013, Pages 2710-2718
International Journal of Cardiology

Metabolic exercise test data combined with cardiac and kidney indexes, the MECKI score: A multiparametric approach to heart failure prognosis

https://doi.org/10.1016/j.ijcard.2012.06.113Get rights and content

Abstract

Objectives

We built and validated a new heart failure (HF) prognostic model which integrates cardiopulmonary exercise test (CPET) parameters with easy-to-obtain clinical, laboratory, and echocardiographic variables.

Background

HF prognostication is a challenging medical judgment, constrained by a magnitude of uncertainty.

Methods

Our risk model was derived from a cohort of 2716 systolic HF patients followed in 13 Italian centers. Median follow up was 1041 days (range 4–5185). Cox proportional hazard regression analysis with stepwise selection of variables was used, followed by cross-validation procedure. The study end-point was a composite of cardiovascular death and urgent heart transplant.

Results

Six variables (hemoglobin, Na+, kidney function by means of MDRD, left ventricle ejection fraction [echocardiography], peak oxygen consumption [% pred] and VE/VCO2 slope) out of the several evaluated resulted independently related to prognosis. A score was built from Metabolic Exercise Cardiac Kidney Indexes, the MECKI score, which identified the risk of study end-point with AUC values of 0.804 (0.754–0.852) at 1 year, 0.789 (0.750–0.828) at 2 years, 0.762 (0.726–0.799) at 3 years and 0.760 (0.724–0.796) at 4 years.

Conclusions

This is the first large-scale multicenter study where a prognostic score, the MECKI score, has been built for systolic HF patients considering CPET data combined with clinical, laboratory and echocardiographic measurements. In the present population, the MECKI score has been successfully validated, performing very high AUC.

Introduction

The time course of heart failure (HF) is often insidious, and it is influenced by several factors, including functional, neurohumoral and compensatory mechanisms, concomitant diseases as well as psychological well-being, environmental and genetic factors with variable expression and penetrance. Therefore, prognostication is a challenging medical judgment, constrained by a magnitude of uncertainty. Cardiopulmonary exercise test (CPET) is a well recognized, valuable and accurate tool for risk stratification in HF. Among several CPET-derived variables, peak VO2 [1], [2], VE/VCO2 relationship [1], [3], [4], [5], and their combination [6], [7] have been identified as predictors of HF prognosis, and they are used for timing of heart transplant [2], [8], [9]. Although the wealth of information derived from CPET for HF prognosis has been informative and exciting, risk stratification with CPET-derived parameters need to be integrated into clinical practice, and combining them with demographic data, medical history, laboratory values and HF treatment background might be helpful. This aspect has been scantily investigated and analyzed. Indeed, at present, only HF survival score (HFSS) [10] and HF-Action Predictive Risk Score Model [11] include peak VO2 (the former) and exercise duration at CPET (the latter), among other clinical parameters [10], [12], [13], [14]. However, neither HF-Action Predictive Risk Score Model nor HFSS include ventilatory parameters [3], [4] and hemoglobin [15], [16], both holding a prognostic value in HF.

Hence, the purpose of the present work was to build a new risk score for systolic HF, integrating measures with potential prognostic value from CPET with established clinical, laboratory and echocardiographic risk factors, in a sizeable multicenter cohort, recruited and followed by experienced HF units in order to identify patients at risk of cardiovascular death and urgent heart transplant. To do so, we used a robust database derived from leading heart failure clinics in Italy.

Section snippets

Population

Study cohort consisted of 2716 consecutive systolic HF patients, recruited and prospectively followed in 13 Italian HF centers (see Appendix 2). The first patient was recruited in February 1993 and the last one in September 2009. At enrollment, patients were evaluated, and clinical history, physical, laboratory, ECG, echocardiographic, and CPET data were collected. Inclusion criteria were: previous or present HF symptoms (NYHA functional classes I–III, stage C of ACC/AHA classification) and

Statistical analysis

Categorical variables were presented, such as frequency and percentage, and they were compared by chi-square test. Numerical variables were summarized as means ± SD, or medians and interquartile range when their distribution was markedly non normal, specifically LVeDV and LVeDS. Unpaired t-test or non parametric Mann–Whitney test were used when appropriate for between-group comparison. A p < 0.05 was used to define statistical significance.

Predictors for the study end-point were identified by

Results

Patients' demographic, laboratory, echocardiographic and CPET data are reported in Table 1, as well as the number of observations available for each variable. HF treatment, at study run-in, included: beta-blockers in 81% of patients, ACE-inhibitors in 79%, ARB- Blockers in 14%, Diuretics in 80%, antialdosteonic drugs in 49%, anticoagulants in 34%, digitalis in 33%, amiodarone in 26%, antiplatelet drugs in 44%. Albeit the presence of a scheduled major cardiovascular treatment was a study

Discussion

Many HF risk stratification tools were developed, each differing in the type of sample from which it was derived and validated, the variable used for risk stratification, their utility in predicting mortality at varying time points, and their ease of use. However, their application in daily clinical practice is limited by their complexity [23], albeit a few easier to use approaches have been proposed [24] or because they are considered as suboptimal in particular settings [23]. We developed a

Acknowledgment

The authors of this manuscript have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology.

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