Aim The aim of this study was to investigate the relationship between gender and survival of patients with heart failure, using data from both randomized trials and observational studies, and the relative contribution of age, left ventricular systolic function, aetiology, and diabetes to differences in prognosis between men and women. Methods and results Data from 31 studies (41 949 patients; 28 052 men, 13 897 women) from the Meta‐Analysis Global Group In Chronic Heart Failure (MAGGIC) individual patient meta‐analysis were used. We performed survival analysis to assess the association of gender with mortality, adjusting for predictors of mortality, including age, reduced or preserved ejection fraction (EF), and ischaemic or non‐ischaemic aetiology. Women were older [70.5 (standard deviation 12.1) vs. 65.6 (standard deviation 11.6) years], more likely to have a history of hypertension (49.9% vs. 40.0%), and less likely to have a history of ischaemic heart disease (46.3% vs. 58.7%) and reduced EF (62.6% vs. 81.6%) compared with men. During 3 years follow‐up, 3521 (25%) women and 7232 (26%) men died. After adjustment, male gender was an independent predictor of mortality, and the better prognosis associated with female gender was more marked in patients with heart failure of non‐ischaemic, compared with ischaemic, aetiology (P‐value for interaction = 0.03) and in patients without, compared with those with, diabetes (P‐value for interaction <0.0001). Conclusion This large, individual patient data meta‐analysis has demonstrated that survival is better for women with heart failure compared with men, irrespective of EF. This survival benefit is slightly more marked in non‐ischaemic heart failure but is attenuated by concomitant diabetes.

Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.