Predictors and treatment response with cardiac resynchronization therapy in patients with heart failure characterized by dyssynchrony: a pre-defined analysis from the CARE-HF trial.

AIMS The cardiac resynchronization therapy in heart failure trial (CARE-HF) demonstrated that cardiac resynchronization therapy (CRT) reduces morbidity and mortality in patients with heart failure and cardiac dyssynchrony. The aim of this study was to develop a prognostic model to evaluate the relationship between prospectively defined patient characteristics and treatment on the trial primary outcome of death from any cause or unplanned hospitalization for a major cardiovascular event. METHODS AND RESULTS A total of 813 patients were enrolled in the CARE-HF study and were followed for a mean of 29.4 months. A Cox Proportional Hazards Model was fitted to identify predictors of the primary outcome and any predictors that modified the effect of CRT. Ischaemic aetiology, more severe mitral regurgitation and increased N-terminal pro-brain natriuretic peptide, were associated with an increased risk of death or unplanned cardiovascular hospitalization irrespective of cardiac resynchronization [Hazard ratio (HR) 1.89, 95% CI 1.45-2.46, HR 1.71, 95% CI 1.38-2.12 and HR 1.31, 95% CI 1.17-1.47, respectively] and increasing systolic blood pressure with a decreasing risk of an event (HR 0.99, 95% CI 0.98-1.00). The benefits of cardiac resynchronization were modified by systolic blood pressure and interventricular mechanical delay (IVMD). Patients with increasing systolic blood pressure appear to receive reduced benefit from CRT (HR 1.02, 95% CI 1.00-1.03), whereas those patients with more severe IVMD appear to benefit more from treatment (HR 0.99, 95% CI 0.98-1.00). CONCLUSION Patients with echocardiographic evidence of more severe cardiac dyssynchrony and low systolic blood pressure obtain greater benefit from CRT, although benefits were substantial across the range of subjects included in the trial.

[1]  N. Freemantle,et al.  Baseline echocardiographic characteristics of heart failure patients enrolled in a large European multicentre trial (CArdiac REsynchronisation Heart Failure study). , 2006, European journal of echocardiography : the journal of the Working Group on Echocardiography of the European Society of Cardiology.

[2]  Patricia L. Smith Splines as a Useful and Convenient Statistical Tool , 1979 .

[3]  Elisa T. Lee,et al.  Statistical Methods for Survival Data Analysis , 1994, IEEE Transactions on Reliability.

[4]  J. Lewis,et al.  Statistical principles for clinical trials (ICH E9): an introductory note on an international guideline. , 1999, Statistics in medicine.

[5]  D. Poirier Piecewise Regression Using Cubic Splines , 1973 .

[6]  Theo Stijnen,et al.  A goodness-of-fit test for Cox's proportional hazards model based on martingale residuals , 1998 .

[7]  J. Cleland,et al.  Is the prognosis of heart failure improving? , 1999, Journal of the American College of Cardiology.

[8]  N Freemantle,et al.  The CARE‐HF study (CArdiac REsynchronisation in Heart Failure study): rationale, design and end‐points , 2001, European journal of heart failure.

[9]  F. Harrell,et al.  Regression models for prognostic prediction: advantages, problems, and suggested solutions. , 1985, Cancer treatment reports.

[10]  J. Daubert,et al.  Do we have reasons to be enthusiastic about pacing to treat advanced heart failure? , 1999, European journal of heart failure.

[11]  N. Freemantle,et al.  Cardiac resynchronisation for patients with heart failure due to left ventricular systolic dysfunction — a systematic review and meta‐analysis , 2006, European journal of heart failure.

[12]  D. Delurgio,et al.  Cardiac resynchronization in chronic heart failure. , 2002, The New England journal of medicine.

[13]  Elisa T. Lee,et al.  Statistical Methods for Survival Data Analysis , 1994, IEEE Transactions on Reliability.

[14]  P. V. Rao,et al.  Applied Survival Analysis: Regression Modeling of Time to Event Data , 2000 .

[15]  H. Akaike A new look at the statistical model identification , 1974 .

[16]  J. Spinelli,et al.  Pacing for heart failure: selection of patients, techniques and benefits , 1999, European journal of heart failure.

[17]  D. DeMets,et al.  Cardiac-resynchronization therapy with or without an implantable defibrillator in advanced chronic heart failure. , 2004, The New England journal of medicine.

[18]  Milton Packer,et al.  Cardiac resynchronization in chronic heart failure. , 2002, The New England journal of medicine.

[19]  J. Sterne,et al.  Development and validation of a prognostic model for survival time data: application to prognosis of HIV positive patients treated with antiretroviral therapy , 2004, Statistics in medicine.

[20]  P Royston,et al.  Choice of scale for cubic smoothing spline models in medical applications. , 2000, Statistics in medicine.

[21]  A. Dobson,et al.  How well does B-type natriuretic peptide predict death and cardiac events in patients with heart failure: systematic review , 2005, BMJ : British Medical Journal.

[22]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[23]  Frank E. Harrell,et al.  The restricted cubic spline hazard model , 1990 .

[24]  J. Daubert,et al.  The effect of cardiac resynchronization on morbidity and mortality in heart failure. , 2005, The New England journal of medicine.

[25]  I. W. Wright Splines in Statistics , 1983 .

[26]  Paolo Rizzon,et al.  Ventricular asynchrony predicts a better outcome in patients with chronic heart failure receiving cardiac resynchronization therapy. , 2005, Journal of the American College of Cardiology.

[27]  Catherine Klersy,et al.  Interventricular and intraventricular dyssynchrony are common in heart failure patients, regardless of QRS duration. , 2004, European heart journal.

[28]  D. Gibson,et al.  Differing effects of right ventricular pacing and left bundle branch block on left ventricular function. , 1993, British heart journal.

[29]  Patrick Royston,et al.  Simplifying a prognostic model: a simulation study based on clinical data , 2002, Statistics in medicine.

[30]  D. Barnett,et al.  Plasma N-terminal pro-brain natriuretic peptide and the ECG in the assessment of left-ventricular systolic dysfunction in a high risk population. , 1999, European heart journal.

[31]  P. Grambsch,et al.  Martingale-based residuals for survival models , 1990 .

[32]  Bristow Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure (COMPANION) : Cardiac-resynchronization therapy with or without an implantable defibrillator in advanced chronic heart failure , 2004 .

[33]  John O'Quigley,et al.  Statistical methods for survival data analysis. (2nd edition). Elisa Lee, John Wiley, New York, 1992. no. of pages: XII + 482. price: £47.50. ISBN: 0-471-61592-7 , 1994 .