Objectives To establish a score system derived from clinical, echocardiographic and electrocardiographic factors and evaluate its clinical value for cardiac resynchronisation therapy (CRT) patient selection.
Methods A total of 90 patients receiving CRT (60.8 ± 12.9 years) were enrolled. All patients underwent standard EKG and echocardiography evaluation before implantation and during follow-up. The parameters measured by EKG were: duration of QRS, morphology of QRS. Left ventricular end-systolic and end-diastolic dimensions (LVESD, LVEDD), end-systolic and end-diastolic volumes (LVESV, LVEDV), left ventricular ejection fraction (LVEF), interventricular mechanical delay (IVMD), tricuspid annular plane systolic excursion (TAPSE), degree of mitral and tricuspid regurgitation and pulmonary artery pressure (PAH) were calculated by echocardiography. Global longitudinal (LS), circumferential (CS) and radial strain (RS) values were analysed based on speckle tracking imaging. We measured standard deviation of time to reach minimum regional volume of 16 segments (systolic dyssynchrony index, SDI) related to the heart cycle from real-time three-dimensional echocardiography. A patient selection score system was generated by the clinical, echocardiographic and electrocardiographic parameters achieving a significance level by univariate and multivariate regression model. The Wald test was used to evaluate te weight of the variables to predict CRT response. A positive response to CRT was a LVESV decrease of ≥15% and not reaching primary clinical endpoint (death or rehospitalisation for heart failure) at the end of follow-up.
Results Thirty-seven patients were CRT non-responders (41.11%) and 53 were responder (58.89%). A 4-point score system was generated based on TAPSE ≥ 14.8mm, SDI ≥ 12.62%, LS ≤ -7.22% and QRSd ≥ 171 ms. The sensitivity and specificity for prediction a positive response to CRT at a score ≥3 were 0.792 and 0.946, respectively (AUC: 0.945, 95% CI: 0.902-0.988, p < 0.001)
Conclusions A patient selection score system based on the integration of TAPSE, SDI, LS and QRSd can help to predict positive response to CRT effectively and reliably.
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