M. Domingo

El índice de masa corporal influye en la mortalidad de los pacientes con insuficiencia cardiaca

Introduccion y objetivos La obesidad es un factor de riesgo independiente de que se desarrolle insuficiencia cardiaca. Paradojicamente, se ha observado una mayor supervivencia en los pacientes obesos con insuficiencia cardiaca. El objetivo del estudio es analizar la relacion entre el indice de masa corporal (IMC) y la mortalidad a 2 anos en una poblacion ambulatoria de pacientes con insuficiencia cardiaca de diferentes etiologias en una unidad especializada. Metodos Se analizo el indice de masa corporal en la primera visita y la supervivencia a 2 anos en 501 pacientes (el 73% varones; mediana de edad, 68 anos). La principal etiologia de la insuficiencia cardiaca fue la cardiopatia isquemica (59%). La fraccion de eyeccion media fue del 30%. Los pacientes fueron clasificados en funcion de su indice de masa corporal en 4 grupos: bajo peso (IMCxa0 Resultados La mortalidad a 2 anos difirio significativamente (pxa0 Conclusiones Un mayor IMC tiene relacion con menor mortalidad por todas las causas a los 2 anos de seguimiento. Nuestros resultados contribuyen a confirmar, en una poblacion general con insuficiencia cardiaca de diferentes etiologias, la relacion paradojica entre la obesidad y el pronostico de la insuficiencia cardiaca.

Does body mass index influence mortality in patients with heart failure

INTRODUCTION AND OBJECTIVESnObesity is an independent risk factor for congestive heart failure. Paradoxically, improved survival has been observed in obese heart failure patients. The objective of this study was to analyze the relationship between body mass index (BMI) and the 2-year mortality rate in outpatients with heart failure of different etiologies who were attending a heart failure unit.nnnMETHODSnBaseline BMI and survival status at 2-year follow-up were recorded in 501 patients (73% men, median age 68 years). Heart failure etiology was mainly ischemic heart disease, present in 59%. The patients median ejection fraction was 30%. They were divided into four groups according to BMI: low weight (<20.5), normal weight (20.5 to <25.5), overweight (25.5 to <30), and obese (>/=30).nnnRESULTSnThe mortality rate at 2 years differed significantly (P< .001) between the groups: 46.7% for low-weight patients, 27.8% for normal-weight patients, 18.7% for overweight patients, and 16% for obese patients. After adjusting for age, sex, heart failure etiology, functional class, ejection fraction, hypertension, diabetes, estimated creatinine clearance rate, plasma hemoglobin level, and treatment received, BMI remained an independent predictor of reduced mortality at 2 years (odds ratio=0.92 [0.88-0.97]).nnnCONCLUSIONSnA high BMI has been associated with lower all-cause mortality rates at 2-year follow-up. Our findings in a broad population of patients with heart failure of different etiologies further confirm the existence of a paradoxical relationship between obesity and heart failure outcome.

Advanced interatrial block predicts new-onset atrial fibrillation and ischemic stroke in patients with heart failure: The “Bayes' Syndrome-HF” study

AIMSnAdvanced interatrial block (IAB) is characterized by a prolonged (≥120u202fms) and bimodal P wave in the inferior leads. The association between advanced IAB and atrial fibrillation (AF) is known as Bayes Syndrome, and there is scarce information about it in heart failure (HF). We examined the prevalence of IAB and whether advanced IAB could predict new-onset AF and/or stroke in HF patients.nnnMETHODS AND RESULTSnThe prospective observational Bayes Syndrome-HF study included consecutive outpatients with chronic HF. The primary endpoints were new-onset AF, ischemic stroke, and the composite of both. A secondary endpoint included all-cause death alone or in combination with the primary endpoint. Comprehensive multivariable Cox regression analyses were performed. Among 1050 consecutive patients, 536 (51.0%) were in sinus rhythm, 464 with a measurable P wave are the focus of this study. Two-hundred and sixty patients (56.0%) had normal atrial conduction, 95 (20.5%) partial IAB, and 109 (23.5%) advanced IAB. During a mean follow-up of 4.5u202f±u202f2.1u202fyears, 235 patients experienced all-cause death, new-onset AF, or stroke. In multivariable comprehensive Cox regression analyses, advanced IAB was associated with new-onset AF (HR 2.71 [1.61-4.56], Pu202f<u202f0.001), ischemic stroke (HR 3.02 [1.07-8.53], Pu202f=u202f0.04), and the composite of both (HR 2.42 [1.41-4.15], Pu202f<u202f0.001).nnnCONCLUSIONSnIn patients with HF advanced IAB predicts new-onset AF and ischemic stroke. Future studies must assess whether anticoagulant treatment in Bayes Syndrome leads to better outcomes in HF.

Long-term mortality risk stratification in heart failure using classification and regression trees with a combination of biomarkers

Background: Mortality remains high in heart failure (HF). Several statistical models can be used in order to evaluate the additive usefulness of the combination of biomarkers reflecting different pathophysiological pathways.nnObjectives: To assess the performance of SPSS Classification and Regression trees (CART) for risk of death stratification using serum biomarkers.nnPatients and methods: We analyzed 876 consecutive outpatients (72% men, median age 70.4 years, main etiology of HF ischemic heart disease (52.7%), median LVEF 34%). A combination of biomarkers reflecting myocyte injury (hs-cTnT), myocardial stretch (NT-proBNP) and ventricular fibrosis and remodelling (ST2) was used.nnResults: During a median follow-up of 4 years, 370 patients died. Using semi-automatic CART (only selecting the minimum cases for parental (80) and filial (40) nodes), 12 nodes were obtained. At first step, hs-cTnT (cutoff point 16 ng/L) yielded 2 nodes with mortality rates of 18.4% (node 1) and 57.1% (node 2). At second step, ST2 (cut points 45 ng/L for low hs-cTnT and 91.6 ng/L for high hs-cTnT) yielded nodes with mortality rates of 13.9% (node 3), 35.7% (node 4), 54.2% (node 5), and 87.5% (node 6). At third step, hs-cTnT emerged again significant to further split node 3, and node 5 was divided by NTproBNP levels (cutoff point 1846.5 ng/L). The last step used again ST2 to split node 9. The mortality in the terminal branch nodes ranged from 3.3% (node 7) to 87.2% (node 6). Agreement between predicted and observed death was 70%.nn![Figure][1] nnCART for total mortalitynnnnnnConclusions: Using a simple CART decision tree with the biomarkers hs-cTnT, ST2 and NTproBNP good stratification of risk of death was easily achieved in chronic HF outpatients.nn [1]: pending:yes