Heart failure detection in electrocardiograms using artificial intelligence and pragmatic labelling.

The diagnosis of heart failure (HF) is resource-intensive, leading to severe underdiagnosis. This study proposes the use of a deep learning model to detect HF solely from electrocardiograms. HF has limited validity in diagnosis codes, lowering the viability of direct application of supervised learning. However, validating the codes with measured levels of circulating N-terminal proB-type natriuretic peptide (NT-proBNP) during training mitigates the impact of label noise. We developed and prospectively validated a neural network for HF detection, using a development cohort of 25,300 patients and an independent test cohort of 43,727 patients. The model achieved an AUC of 0.86 (95% CI 0.847-0.864) for hospital-diagnosed HF, which increased to 0.91 (95% CI 0.900-0.913) with age-adjusted NT-proBNP thresholds and 0.96 (95% CI 0.951-0.963) with stricter limits of 125 and 1000 ng/L. The model was externally validated using the MIMIC-IV cohort of 161,352 patients, yielding AUC values of 0.87 (95% CI 0.861-0.871), 0.90 (95% CI 0.895-0.908), and 0.96 (95% CI 0.950-0.964) with the same labelling strategies. Model predictions were further validated using echocardiographic assessments of diastolic function grade and the H2FPEF score, demonstrating that the model accurately captures both diastolic and systolic function. The model has been released as open source.