An intelligent classifier for prognosis of cardiac resynchronization therapy based on speckle-tracking echocardiograms

PURPOSE Predicting response after cardiac resynchronization therapy (CRT) has been a challenge of cardiologists. About 30% of selected patients based on the standard selection criteria for CRT do not show response after receiving the treatment. This study is aimed to build an intelligent classifier to assist in identifying potential CRT responders by speckle-tracking radial strain based on echocardiograms. METHODS AND MATERIALS The echocardiograms analyzed were acquired before CRT from 26 patients who have received CRT. Sequential forward selection was performed on the parameters obtained by peak-strain timing and phase space reconstruction on speckle-tracking radial strain to find an optimal set of features for creating intelligent classifiers. Support vector machine (SVM) with a linear, quadratic, and polynominal kernel were tested to build classifiers to identify potential responders and non-responders for CRT by selected features. RESULTS Based on random sub-sampling validation, the best classification performance is correct rate about 95% with 96-97% sensitivity and 93-94% specificity achieved by applying SVM with a quadratic kernel on a set of 3 parameters. The selected 3 parameters contain both indexes extracted by peak-strain timing and phase space reconstruction. CONCLUSIONS An intelligent classifier with an averaged correct rate, sensitivity and specificity above 90% for assisting in identifying CRT responders is built by speckle-tracking radial strain. The classifier can be applied to provide objective suggestion for patient selection of CRT.

[1]  Shigeo Abe,et al.  Support Vector Machines for Pattern Classification (Advances in Pattern Recognition) , 2005 .

[2]  Jeroen J. Bax,et al.  Results of the Predictors of Response to CRT (PROSPECT) Trial , 2008, Circulation.

[3]  John Gorcsan,et al.  Utility of echocardiographic radial strain imaging to quantify left ventricular dyssynchrony and predict acute response to cardiac resynchronization therapy. , 2005, The American journal of cardiology.

[4]  A. Støylen,et al.  Noninvasive myocardial strain measurement by speckle tracking echocardiography: validation against sonomicrometry and tagged magnetic resonance imaging. , 2006, Journal of the American College of Cardiology.

[5]  Catherine Klersy,et al.  Comparison of eight echocardiographic methods for determining the prevalence of mechanical dyssynchrony and site of latest mechanical contraction in patients scheduled for cardiac resynchronization therapy. , 2009, The American journal of cardiology.

[6]  Richard J. Povinelli,et al.  Identification of ECG Arrhythmias Using Phase Space Reconstruction , 2001, PKDD.

[7]  Richard J. Povinelli,et al.  A reconstructed phase space approach for distinguishing ischemic from non-ischemic ST changes using Holter ECG data , 2003, Computers in Cardiology, 2003.

[8]  P. Nihoyannopoulos,et al.  Strain and strain rate deformation parameters: from tissue Doppler to 2D speckle tracking , 2008, The International Journal of Cardiovascular Imaging.

[9]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[10]  G. Sutherland,et al.  Patient Selection and Echocardiographic Assessment of Dyssynchrony in Cardiac Resynchronization Therapy Crt: from Origins to Routine Clinical Practice Selection of Candidates in Crt Clinical Trials Defining Response to Therapy Contemporary Reviews in Cardiovascular Medicine , 2022 .

[11]  Richard B Devereux,et al.  Recommendations for chamber quantification: a report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardio , 2005, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[12]  D. Hayes,et al.  Book Review Cardiac Resynchronization Therapy in Heart Failure , 2011 .

[13]  X Ning,et al.  Nonlinear dynamic characteristics analysis of synchronous 12-lead ECG signals. , 2000, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

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

[15]  S. Saba,et al.  Dyssynchrony by speckle-tracking echocardiography and response to cardiac resynchronization therapy: results of the Speckle Tracking and Resynchronization (STAR) study , 2010, European heart journal.

[16]  K. Albouaini,et al.  Cardiac resynchronisation therapy: evidence based benefits and patient selection. , 2008, European journal of internal medicine.

[17]  Maxime Cannesson,et al.  Novel Speckle-Tracking Radial Strain From Routine Black-and-White Echocardiographic Images to Quantify Dyssynchrony and Predict Response to Cardiac Resynchronization Therapy , 2006, Circulation.

[18]  S. Silver,et al.  Heart Failure , 1937, The New England journal of medicine.

[19]  Shemy Carasso,et al.  Left ventricular strain patterns in dilated cardiomyopathy predict response to cardiac resynchronization therapy: timing is not everything. , 2009, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[20]  D. Kass,et al.  Predicting cardiac resynchronization response by QRS duration: the long and short of it. , 2003, Journal of the American College of Cardiology.

[21]  S. P. Lake,et al.  Evaluation of a decision support system to predict preoperative investigations. , 2008, British journal of anaesthesia.

[22]  H. Chan,et al.  Phase space analysis of myocardial coordination related to left ventricular ejection fraction by echocardiographic speckle-tracking radial strain. , 2012, Medical engineering & physics.

[23]  Jinbo Bi,et al.  Automated heart abnormality detection using sparse linear classifiers. , 2007, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[24]  Jeroen J. Bax,et al.  Left ventricular dyssynchrony predicts response and prognosis after cardiac resynchronization therapy. , 2004, Journal of the American College of Cardiology.

[25]  Chu-Pak Lau,et al.  Predictors of left ventricular reverse remodeling after cardiac resynchronization therapy for heart failure secondary to idiopathic dilated or ischemic cardiomyopathy. , 2003, The American journal of cardiology.

[26]  Bertrand Clarke,et al.  Principles and Theory for Data Mining and Machine Learning , 2009 .