Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality.

BACKGROUND: Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transthoracic echocardiography and to evaluate the prognostic implications. METHODS: The algorithm was trained on 76,342 patients, validated in 22,825 patients, and tested in 20,960 patients. It was then externally validated using data from a different hospital (n = 556). Finally, a prospective cohort of handheld point-of-care ultrasound devices (n = 319; ClinicalTrials.gov identifier NCT05455541) was used to confirm the findings. A multivariate Cox regression model was used to investigate the association between age estimation and chronologic age with overall survival. RESULTS: The mean absolute error in age estimation was 4.9 years, with a Pearson correlation coefficient of 0.922. The probabilistic value of sex had an overall accuracy of 96.1% and an area under the curve of 0.993. External validation and prospective study cohorts yielded consistent results. Finally, survival analysis demonstrated that age prediction ≥5 years vs chronologic age was associated with an independent 34% increased risk for death during follow-up (P < .001). CONCLUSIONS: Applying artificial intelligence to standard transthoracic echocardiography allows the prediction of sex and the estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age.

Integration of implantable device therapy in patients with heart failure. A clinical consensus statement from the Heart Failure Association (HFA) and European Heart Rhythm Association (EHRA) of the European Society of Cardiology (ESC).

Implantable devices form an integral part of the management of patients with heart failure (HF) and provide adjunctive therapies in addition to cornerstone drug treatment. Although the number of these devices is growing, only few are supported by robust evidence. Current devices aim to improve haemodynamics, improve reverse remodelling, or provide electrical therapy. A number of these devices have guideline recommendations and some have been shown to improve outcomes such as cardiac resynchronization therapy, implantable cardioverter-defibrillators and long-term mechanical support. For others, more evidence is still needed before large-scale implementation can be strongly advised. Of note, devices and drugs can work synergistically in HF as improved disease control with devices can allow for further optimization of drug therapy. Therefore, some devices might already be considered early in the disease trajectory of HF patients, while others might only be reserved for advanced HF. As such, device therapy should be integrated into HF care programmes. Unfortunately, implementation of devices, including those with the greatest evidence, in clinical care pathways is still suboptimal. This clinical consensus document of the Heart Failure Association (HFA) and European Heart Rhythm Association (EHRA) of the European Society of Cardiology (ESC) describes the physiological rationale behind device-provided therapy and also device-guided management, offers an overview of current implantable device options recommended by the guidelines and proposes a new integrated model of device therapy as a part of HF care.