1Universitäts-Herzzentrum Freiburg / Bad Krozingen Freiburg im Breisgau, Deutschland; 2Universitäts-Herzzentrum Freiburg / Bad Krozingen Klinik für Kardiologie und Angiologie Bad Krozingen, Deutschland; 3Artemed Klinikum St. Josefskrankenhaus Innere Medizin - Kardiologie Freiburg im Breisgau, Deutschland; 4Universitäts-Herzzentrum Freiburg / Bad Krozingen Klinik für Kardiologie und Angiologie II Bad Krozingen, Deutschland; 5Universitäts-Herzzentrum Freiburg / Bad Krozingen Klinische Pharmakologie Bad Krozingen, Deutschland; 6Universitäts-Herzzentrum Freiburg - Bad Krozingen Innere Medizin III, Kardiologie und Angiologie Freiburg im Breisgau, Deutschland; 7Universitäts-Herzzentrum Freiburg / Bad Krozingen Rhythmologie Bad Krozingen, Deutschland; 8Luzerner Kantonsspital Herzzentrum Luzern, Schweiz
Background: Heart failure (HF) is a worldwide epidemic affecting more than 64 million people. Given its massive burden both socially and economically, early identification of individuals at risk for complications is important.
Purpose: To test the predictive performance of non-invasive models from 12-lead digitalized electrocardiography (ECG) and cycle ergometer for adverse outcome in individuals with heart failure.
Methods: 598 Patients scheduled for right heart catheterization due to unexplained dyspnea were screened. All ECG recorded at admission were further analysed using the software “ECG-Precision-Analysis V-1-2023” to measure the duration of amplified p-wave (APWD at 175mm/sec, 80mm/mV) and to confirm the diagnosis of inter-atrial block. Ergometer was performed in a supine position with increasing intensity adapted to individual capacity (25 or 50W). The maximum physical capacity (Watt-max) was determined. Follow-up (FU) was scheduled at 12-,36-, 60- and 120-month after discharge with the primary endpoint as MACCE (a composite of new onset of AF, ischemic stroke and peripheral embolism) in at baseline event-free patients and the secondary endpoint as all-cause mortality in the total cohort. Three machine learning (ML) algorithms (COX regression, BSR and LASSO regression) were used to select candidate variables and establish predictive models for MACCE and all-cause mortality. Model evaluation was performed in terms of discriminatory power using area under the curve (AUC) for each endpoint.
Results: Prediction models developed using machine learning (ML) algorithms (COX regression, BSR and LASSO regression) identified Watt-max and/or APWD as core components for MACCE and mortality prediction . APWD and Watt-max were key components of ML models for prediction of thromboembolic risk and AF (MACCE) and reached accurate diagnostic performances (c-statistics up to 0.74), moreover, another model consisted of APWD and aIAB (advanced inter-atrial block) was established to serve as a reference to ML models with moderate performance for MACCE (mean c-statistics<0.70). All ML models for prediction of all-cause mortality included Watt-max and achieved c-statistics > 0.75.
Conclusions: In patients with heart failure, the combination of APWD, aIAB and maximum excercice capacity allow to identify patients at risk for AF and stroke, whereas mortality is predicted by maximum exercise capacity only.