Altmetric

Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients

File Description SizeFormat 
EURHEARTJ-D-18-02222_Authors_Copy.docxFile embargoed until 26 January 2020623.33 kBUnknown    Request a copy
Title: Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients
Authors: Diller, G-P
Kempny, A
Babu-Narayan, SV
Henrichs, M
Brida, M
Uebing, A
Lammers, AE
Baumgartner, H
Li, W
Wort, SJ
Dimopoulos, K
Gatzoulis, MA
Item Type: Journal Article
Abstract: Aims: To assess the utility of machine learning algorithms on estimating prognosis and guiding therapy in a large cohort of patients with adult congenital heart disease (ACHD) or pulmonary hypertension at a single, tertiary centre. Methods and results: We included 10 019 adult patients (age 36.3 ± 17.3 years) under follow-up at our institution between 2000 and 2018. Clinical and demographic data, ECG parameters, cardiopulmonary exercise testing, and selected laboratory markers where collected and included in deep learning (DL) algorithms. Specific DL-models were built based on raw data to categorize diagnostic group, disease complexity, and New York Heart Association (NYHA) class. In addition, models were developed to estimate need for discussion at multidisciplinary team (MDT) meetings and to gauge prognosis of individual patients. Overall, the DL-algorithms-based on over 44 000 medical records-categorized diagnosis, disease complexity, and NYHA class with an accuracy of 91.1%, 97.0%, and 90.6%, respectively in the test sample. Similarly, patient presentation at MDT-meetings was predicted with a test sample accuracy of 90.2%. During a median follow-up time of 8 years, 785 patients died. The automatically derived disease severity-score derived from clinical information was related to survival on Cox analysis independently of demographic, exercise, laboratory, and ECG parameters. Conclusion: We present herewith the utility of machine learning algorithms trained on large datasets to estimate prognosis and potentially to guide therapy in ACHD. Due to the largely automated process involved, these DL-algorithms can easily be scaled to multi-institutional datasets to further improve accuracy and ultimately serve as online based decision-making tools.
Issue Date: 1-Apr-2019
Date of Acceptance: 31-Dec-2018
URI: http://hdl.handle.net/10044/1/68541
DOI: https://dx.doi.org/10.1093/eurheartj/ehy915
ISSN: 1522-9645
Publisher: Oxford University Press (OUP)
Start Page: 1069
End Page: 1077
Journal / Book Title: European Heart Journal
Volume: 40
Issue: 13
Copyright Statement: Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com. This is a pre-copy-editing, author-produced version of an article accepted for publication in European Heart Journal following peer review. The definitive publisher-authenticated version is available online at: https://academic.oup.com/eurheartj/article/40/13/1069/5301309
Sponsor/Funder: British Heart Foundation
Funder's Grant Number: FS/11/38/28864
Keywords: 1102 Cardiovascular Medicine And Haematology
Cardiovascular System & Hematology
Publication Status: Published
Conference Place: England
Embargo Date: 2020-01-26
Online Publication Date: 2019-01-26
Appears in Collections:National Heart and Lung Institute



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons