Dr Hidde Bleijendaal (Amsterdam University Medical Center, the Netherlands) presented a study aimed to evaluate whether ECG-based machine learning models are able to predict all-cause, in-hospital mortality in COVID-19 patients and to identify ECG features associated with mortality [1]. Included were 882 patients admitted with COVID-19 in 7 different Dutch hospitals. Raw-format 12-lead ECGs recorded after admission (<72 hours) were collected, manually assessed, and annotated using pre-defined ECG features. Using data from 5 of the 7 centres (n=634), 2 mortality prediction models were developed: a logistic regression model using manually annotated ECG features, and a pre-trained deep neural network (DNN) using the raw ECG waveforms. Data from 2 other centres (n=248) were used for external validation. Furthermore, a baseline model was created using only age and sex to evaluate the added value of ECG.
The performance of both prediction models was similar, with a mean area under the receiver operating curve of 0.76 for the logistic regression model and 0.77 for the DNN in the external validation cohort versus 0.76 in the baseline model. After adjustment for age and sex, the following ECG features remained as significant predictors for mortality in COVID-19 patients:
- increased ventricular rate,
- right bundle branch block,
- ST-depression, and
- low QRS voltages.
Dr Bleijendaal concluded: “predication of mortality in this dataset in COVID-19 patients is mostly based on age and sex. The added value of the ECG seems to be present, but limited.”
- Bleijendaal H. Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning. 2021 EHRA Congress, 23-25 April.
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