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Big data analytics in adult congenital heart disease: why coding matters

With their study ‘Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients’, Diller et al. demonstrate that machine learning algorithms can tackle challenges within the complex field of congenital heart disease (CHD). They compared the outcomes of deep learning (DL)-algorithms in diagnosing and evaluating disease complexity and New York Heart Association (NYHA) class with that of a test sample, and achieved an accuracy of 91.1, 97.0, and 90.6%, respectively. These results are promising and the authors can be complimented with this ‘first in ACHD’ (adult CHD) use of these tools. The authors conclude that their study illustrates the applicability of machine learning algorithms in estimating prognosis and potentially even in guiding therapeutic management of ACHD. Moreover, they state that these DL-algorithms can easily be scaled to multi-institutional datasets to further improve their accuracy and thereby, ultimately, may be used as online-based decisionmaking tools. These conclusions deserve some reflections. We agree with the authors that there is a desperate need for data on ACHD, best illustrated by the European Society of Cardiology Guidelines that, apart from two exceptions, only contain level C recommendations. There are likely several reasons for this scarcity: (i) a lack of consensus regarding ACHD nomenclature, (ii) absence of clinical care pathway harmonization across expert centers, (iii) the heterogeneity of ACHD in terms of the initial diagnosis, and (iv) the diagnosisand sometimes institution-specific interventions that all have changed substantially over the years. Furthermore, complications of ACHD including heart failure, arrhythmias, and death often occur late in life, making it difficult to link outcome to the initial diagnosis and management. Taken together, ACHD is not a rare disease, with an estimated 2.3 million and ± 1 million patients affected in Europe and the US, respectively. In Europe, the management of these patients is currently scattered across many small, regional hospitals, with only a few larger centers. However, even in the latter, patients with a specific diagnosis are too limited to apply sensible statistics. To overcome these issues, multiple national—the Dutch CONCOR registry (http://concor.net), the German Kompetenz Network (www.kompetenznetz-ahf.de), and the Swedish Registry of Congenital Heart Disease (www.ucr.uu. se/swedcon)—and international registries including the Congenital Heart Surgeons Association (https://www.echsacongenitaldb.org) have been generated. Nonetheless, the ‘conventional way’ of setting up and filling databases is labour-intensive and thereby expensive. Introducing electronic health records provides a unique opportunity to overcome this limitation by automatic data collection and extraction from routine clinical care on a large scale. Future development of algorithms with the capacity to scan digital patient files, structure unstructured data (e.g. free text), and extract symptoms, diagnoses, applied interventions, hospital readmission rates, and long-term clinical outcomes will lead to a considerable reduction in the costs of setting up and keeping these registries up to date. Additionally, other data sources currently lacking feasibility, including medical imaging, sensor, and electrocardiogram data, could be implemented in these algorithms in the future. Having access to and being able to analyse an amount of data that is far greater than used in conventional medical analyses, ‘big data’, from all imaginable sources that include both possible determinants for outcome as well as the outcome data itself, one might get a better grip on this extremely complicated and diverse patient category (see Take home figure). There still are substantial practical issues that make generalization of this approach challenging in ACHD, as well as other diseases. Firstly, two different systems of coding ACHD diagnoses are still used in clinical practice, despite the ongoing efforts of the International Pediatric Cardiology Coding Committee for over 15