Machine learning-based prediction of 1-year all-cause mortality in patients undergoing CRT implantation: validation of the SEMMELWEIS-CRT score in the European CRT Survey I dataset

Author:

Tokodi Márton12ORCID,Kosztin Annamária1ORCID,Kovács Attila12ORCID,Gellér László1ORCID,Schwertner Walter Richard1ORCID,Veres Boglárka1ORCID,Behon Anett1ORCID,Lober Christiane3,Bogale Nigussie4ORCID,Linde Cecilia5ORCID,Normand Camilla67ORCID,Dickstein Kenneth68ORCID,Merkely Béla1ORCID

Affiliation:

1. Heart and Vascular Centre, Semmelweis University , 68 Városmajor Street, 1122 Budapest , Hungary

2. Department of Surgical Research and Techniques, Semmelweis University , 68 Városmajor Street, 1122 Budapest , Hungary

3. Institut für Herzinfarktforschung , Ludwigshafen , Germany

4. Department of Heart Disease, Haukeland University Hospital , Bergen , Norway

5. Division of Cardiology, Department of Medicine, Karolinska Institutet , Stockholm , Sweden

6. Cardiology Division, Stavanger University Hospital , Stavanger , Norway

7. Department of Quality and Health Technology, University of Stavanger , Stavanger , Norway

8. Institute of Internal Medicine, University of Bergen , Bergen , Norway

Abstract

Abstract Aims We aimed to externally validate the SEMMELWEIS-CRT score for predicting 1-year all-cause mortality in the European Cardiac Resynchronization Therapy (CRT) Survey I dataset—a large multi-centre cohort of patients undergoing CRT implantation. Methods and results The SEMMELWEIS-CRT score is a machine learning-based tool trained for predicting all-cause mortality in patients undergoing CRT implantation. This tool demonstrated impressive performance during internal validation but has not yet been validated externally. To this end, we applied it to the data of 1367 patients from the European CRT Survey I dataset. The SEMMELWEIS-CRT predicted 1-year mortality with an area under the receiver operating characteristic curve (AUC) of 0.729 (0.682–0.776), which concurred with the performance measured during internal validation [AUC: 0.768 (0.674–0.861), P = 0.466]. Moreover, the SEMMELWEIS-CRT score outperformed multiple conventional statistics-based risk scores, and we demonstrated that a higher predicted probability is not only associated with a higher risk of death [odds ratio (OR): 1.081 (1.061–1.101), P < 0.001] but also with an increased risk of hospitalizations for any cause [OR: 1.013 (1.002–1.025), P = 0.020] or for heart failure [OR: 1.033 (1.015–1.052), P < 0.001], a less than 5% improvement in left ventricular ejection fraction [OR: 1.033 (1.021–1.047), P < 0.001], and lack of improvement in New York Heart Association functional class compared with baseline [OR: 1.018 (1.006–1.029), P = 0.003]. Conclusion In the European CRT Survey I dataset, the SEMMELWEIS-CRT score predicted 1-year all-cause mortality with good discriminatory power, which confirms the generalizability and demonstrates the potential clinical utility of this machine learning-based risk stratification tool.

Funder

European Union

Ministry of Innovation and Technology of Hungary from the National Research

Development and Innovation Fund

National Research, Development, and Innovation Office of Hungary

New National Excellence Program

Ministry of Culture and Innovation in Hungary from the National Research

Publisher

Oxford University Press (OUP)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3