An electrocardiography score predicts heart failure hospitalization or death beyond that of cardiovascular magnetic resonance imaging

Background: The electrocardiogram (ECG) and cardiovascular magnetic resonance imaging (CMR) both provide powerful prognostic information. The aim was to determine the relative prognostic value of ECG and CMR, respectively. Methods: Consecutive patients (n=783) undergoing CMR and resting 12-lead ECG with a QRS duration <120 ms were included. CMR measures included feature tracking global longitudinal strain (GLS), extracellular volume fraction (ECV), left ventricular mass and volumes, and ischemic and non-ischemic scar size. Prognosis scores for one-year event-free survival were derived using continuous ECG or CMR measures, and multinomial logistic regression, and compared with regards to the combined outcome of survival free from hospitalization for heart failure or death. Results: Patients (median [interquartile range] age 55 [43-64] years, 44% female) had 155 events during 5.7 [4.4-6.6] years. The ECG prognosis score included 1) the frontal plane QRS-T angle, and 2) the heart rate corrected QT duration (QTc) (log-rank 55, p<0.001). The CMR prognosis score included 1) GLS, and 2) ECV (log-rank 85, p<0.001). The combination of positive scores for both ECG and CMR yielded the highest prognostic value (log-rank 105, p<0.001). Multivariable analysis showed an association with outcomes for both the ECG prognosis score (log-rank 8.4, hazard ratio [95% confidence interval] 1.29 [1.09-1.54], p=0.004) and the CMR prognosis score (log-rank 47, hazard ratio 1.90 [1.58-2.28], p<0.001). Conclusions: An ECG prognosis score predicted outcomes independently of, and beyond CMR. Combining the results of ECG and CMR using both prognosis scores improved the overall prognostic performance.

The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging

Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatric cardiovascular MRI, adult cardiovascular MRI and other radiologic experience.