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Eur Heart J. 2019 Jan 26. doi: 10.1093/eurheartj/ehy915. [Epub ahead of print]

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

Author information

Adult Congenital Heart Centre, National Centre for Pulmonary Hypertension, Royal Brompton Hospital, Sydney Street, London, UK.
National Heart and Lung Institute, Imperial College School of Medicine, Dovehouse Street, London, UK.
Department of Cardiology III - Adult Congenital and Valvular Heart Disease, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster, Germany.
Competence Network for Congenital Heart Defects, DZHK (German Centre for Cardiovascular Research), Augustenburger Platz 1, Berlin, Germany.
Division of Valvular Heart Disease and Adult Congenital Heart Disease, Department of Cardiovascular Medicine, University Hospital Centre Zagreb, Kispaticeva 12, Zagreb, Croatia.
Division of Paediatric Cardiology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster, Germany.



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.


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.


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