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

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.

Keywords: Adult congenital heart disease; Deep learning; Machine learning; Mortality; Prognostication.

Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.

Figures

Take home figure

Model overview illustrating the combination of deep learning architecture and semiparametric survival model. The deep learning network accepts raw text input and predicts main diagnostic group, disease complexity, and New York Heart Association class. In addition, a disease severity score modelled on 5-year mortality is provided that is combined with additional variables in a multivariate Cox model to provide prognostic information. The speech bubbles illustrate the type of input accepted by the deep learning network. Input examples shown are from different patients and slightly modified to avoid patient confidentiality issues.

Figure 1

Model overview illustrating the deep learning architecture. The deep learning network accepts raw text input and predicts main diagnostic group, disease complexity, and New York Heart Association class. Networks are trained to recognize patient specific diagnostic and symptom patterns compatible with presentation at multidisciplinary meetings and specific medical therapy.

Figure 2

Results of the receiver operating curve analysis for predicting need for multidisciplinary discussion, beta-blocker, ACE-inhibitor/angiotensin receptor blocker, and anticoagulation in the training and validation sample, respectively.