Literature DB >> 29031415

Usefulness of the CRT-SCORE for Shared Decision Making in Cardiac Resynchronization Therapy in Patients With a Left Ventricular Ejection Fraction of ≤35.

Ulas Höke1, Bart Mertens2, Mand J H Khidir3, Martin J Schalij3, Jeroen J Bax3, Victoria Delgado3, Nina Ajmone Marsan4.   

Abstract

Individualized estimation of prognosis after cardiac resynchronization therapy (CRT) remains challenging. Our aim was to develop a multiparametric prognostic risk score (CRT-SCORE) that could be used for patient-specific clinical shared decision making about CRT implantation. The CRT-SCORE was derived from an ongoing CRT registry, including 1,053 consecutive patients (age 67 ± 10 years, 76% male). Using preimplantation variables, 100 multiple imputed datasets were generated for model calibration. Based on multivariate Cox regression models, cross-validated linear prognostic scores were calculated, as well as survival fractions at 1 and 5 years. Specifically, the CRT-SCORE was calculated using atrioventricular junction ablation, age, gender, etiology, New York Heart Association class, diabetes, hemoglobin level, renal function, left bundle branch block, QRS duration, atrial fibrillation, left ventricular systolic and diastolic functions, and mitral regurgitation, and showed a good discriminative ability (areas under the curve 0.773 at 1 year and 0.748 at 5 years). During the long-term follow-up (median 60 months, interquartile range 31 to 85), all-cause mortality was observed in 494 (47%) patients. Based on the distribution of the CRT-SCORE, lower- and higher-risk patient groups were identified. Estimated mean survival rates of 98% at 1 year and 92% at 5 years were observed in the lowest 5% risk group (L5 CRT-SCORE: -4.42 to -1.60), whereas the highest 5% risk group (H5 CRT-SCORE: 1.44 to 2.89) showed poor survival rates: 78% at 1 year and 22% at 5 years. In conclusion, the CRT-SCORE allows accurate prediction of 1- and 5-year survival rates after CRT using readily available and CRT-specific clinical, electrocardiographic, and echocardiographic parameters. The model may assist clinicians in counseling patients and in decision making.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2017        PMID: 29031415     DOI: 10.1016/j.amjcard.2017.08.019

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  8 in total

Review 1.  Shared Decision Making in Cardiac Electrophysiology Procedures and Arrhythmia Management.

Authors:  Mina K Chung; Angela Fagerlin; Paul J Wang; Tinuola B Ajayi; Larry A Allen; Tina Baykaner; Emelia J Benjamin; Megan Branda; Kerri L Cavanaugh; Lin Y Chen; George H Crossley; Rebecca K Delaney; Lee L Eckhardt; Kathleen L Grady; Ian G Hargraves; Mellanie True Hills; Matthew M Kalscheur; Daniel B Kramer; Marleen Kunneman; Rachel Lampert; Aisha T Langford; Krystina B Lewis; Ying Lu; John M Mandrola; Kathryn Martinez; Daniel D Matlock; Sarah R McCarthy; Victor M Montori; Peter A Noseworthy; Kate M Orland; Elissa Ozanne; Rod Passman; Krishna Pundi; Dan M Roden; Elizabeth V Saarel; Monika M Schmidt; Samuel F Sears; Dawn Stacey; Randall S Stafford; Benjamin A Steinberg; Sojin Youn Wass; Jennifer M Wright
Journal:  Circ Arrhythm Electrophysiol       Date:  2021-12-06

2.  Construction and assessment of prediction rules for binary outcome in the presence of missing predictor data using multiple imputation and cross-validation: Methodological approach and data-based evaluation.

Authors:  Bart J A Mertens; Erika Banzato; Liesbeth C de Wreede
Journal:  Biom J       Date:  2020-02-13       Impact factor: 2.207

3.  The VALID-CRT risk score reliably predicts response and outcome of cardiac resynchronization therapy in a real-world population.

Authors:  Emanuele Bertaglia; Giuseppe Arena; Domenico Pecora; Albino Reggiani; Antonio D'Onofrio; Pietro Palmisano; Antonio De Simone; Salvatore I Caico; Massimiliano Marini; Giampiero Maglia; Anna Ferraro; Francesco Solimene; Antonella Cecchetto; Maurizio Malacrida; Giovanni L Botto; Maurizio Lunati; Giuseppe Stabile
Journal:  Clin Cardiol       Date:  2019-07-13       Impact factor: 2.882

4.  Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score.

Authors:  Márton Tokodi; Walter Richard Schwertner; Attila Kovács; Zoltán Tősér; Levente Staub; András Sárkány; Bálint Károly Lakatos; Anett Behon; András Mihály Boros; Péter Perge; Valentina Kutyifa; Gábor Széplaki; László Gellér; Béla Merkely; Annamária Kosztin
Journal:  Eur Heart J       Date:  2020-05-07       Impact factor: 29.983

5.  Global longitudinal strain as a prognostic marker in cardiac resynchronisation therapy: A systematic review.

Authors:  Vinesh Appadurai; Nicholas D'Elia; Thomas Mew; Stephen Tomlinson; Jonathan Chan; Christian Hamilton-Craig; Gregory M Scalia
Journal:  Int J Cardiol Heart Vasc       Date:  2021-07-31

6.  Influence of hemoglobin concentration on the in-hospital outcomes in newly diagnosed heart failure patients with atrial fibrillation: Finding from CCC-AF (improving care for cardiovascular disease in China-atrial fibrillation) project.

Authors:  Mengya Dong; Chenbo Xu; Juan Zhou; Zuyi Yuan
Journal:  Medicine (Baltimore)       Date:  2022-03-04       Impact factor: 1.817

7.  Validation of Three European Risk Scores to Predict Long-Term Outcomes for Patients Receiving Cardiac Resynchronization Therapy in an Asian Population.

Authors:  Shengwen Yang; Zhimin Liu; Wenran Li; Yiran Hu; Shangyu Liu; Ran Jing; Wei Hua
Journal:  J Cardiovasc Transl Res       Date:  2020-05-05       Impact factor: 4.132

8.  Comparing the Modified Frailty Index with conventional scores for prediction of cardiac resynchronization therapy response in patients with heart failure.

Authors:  Ajay Raj; Ranjit Kumar Nath; Bhagya Narayan Pandit; Ajay Pratap Singh; Neeraj Pandit; Puneet Aggarwal
Journal:  J Frailty Sarcopenia Falls       Date:  2021-06-01
  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.