Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning.

BACKGROUND AND PURPOSE: The electrocardiogram (ECG) is frequently obtained in the work-up of COVID-19 patients. So far, no study has evaluated whether ECG-based machine learning models have added value to predict in-hospital mortality specifically in COVID-19 patients. METHODS: Using data from the CAPACITY-COVID registry, we studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw format 12-lead ECGs recorded within 72 h of admission were studied. With data from five hospitals (n = 634), three models were developed: (a) a logistic regression baseline model using age and sex, (b) a least absolute shrinkage and selection operator (LASSO) model using age, sex and human annotated ECG features, and (c) a pre-trained deep neural network (DNN) using age, sex and the raw ECG waveforms. Data from two hospitals (n = 248) was used for external validation. RESULTS: Performances for models a, b and c were comparable with an area under the receiver operating curve of 0.73 (95% confidence interval [CI] 0.65-0.79), 0.76 (95% CI 0.68-0.82) and 0.77 (95% CI 0.70-0.83) respectively. Predictors of mortality in the LASSO model were age, low QRS voltage, ST depression, premature atrial complexes, sex, increased ventricular rate, and right bundle branch block. CONCLUSION: This study shows that the ECG-based prediction models could be helpful for the initial risk stratification of patients diagnosed with COVID-19, and that several ECG abnormalities are associated with in-hospital all-cause mortality of COVID-19 patients. Moreover, this proof-of-principle study shows that the use of pre-trained DNNs for ECG analysis does not underperform compared with time-consuming manual annotation of ECG features.

Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping review.

Aims: Patients with congestive heart failure (HF) are prone to clinical deterioration leading to hospital admissions, burdening both patients and the healthcare system. Predicting hospital admission in this patient group could enable timely intervention, with subsequent reduction of these admissions. To date, hospital admission prediction remains challenging. Increasing amounts of acquired data and development of artificial intelligence (AI) technology allow for the creation of reliable hospital prediction algorithms for HF patients. This scoping review describes the current literature on strategies and performance of AI-based algorithms for prediction of hospital admission in patients with HF. Methods and results: PubMed, EMBASE, and the Web of Science were used to search for articles using machine learning (ML) and deep learning methods to predict hospitalization in patients with HF. After eligibility screening, 23 articles were included. Sixteen articles predicted 30-day hospital (re-)admission resulting in an area under the curve (AUC) ranging from 0.61 to 0.79. Six studies predicted hospital admission over longer time periods ranging from 6 months to 3 years, with AUC's ranging from 0.65 to 0.78. One study prospectively evaluated performance of a disposable sensory patch at home after hospitalization which resulted in an AUC of 0.89 for unplanned hospital admission prediction. Conclusion: AI has the potential to enable prediction of hospital admission in HF patients. Improvement of data management, adding new data sources such as telemonitoring data and ML models and prospective and external validation of current models must be performed before clinical applicability is possible.