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.

The benefit of vaccination against COVID-19 outweighs the potential risk of myocarditis and pericarditis.

Vaccines against coronavirus 2019 disease (COVID-19) have shown to be greatly effective in preventing viral spread, serious illness and death from this infectious disease and are therefore critical for the management of the COVID-19 pandemic. However, the listing of myocarditis and pericarditis as possible rare side effects of the messenger RNA (mRNA) vaccines against COVID-19 by regulatory agencies has sparked discussion on the vaccines' safety. The most important published cohort studies to date demonstrat that myocarditis is a very rare side effect after COVID-19 mRNA vaccination, with an incidence of approximately 1-4 cases per 100,000 vaccinated persons. Young males (16-29 years) appear to be at highest risk, predominantly after receiving the second dose. The disease course is self-limiting in a vast majority of cases: 95% of patients show a rapid resolution of symptoms and normalisation of cardiac biomarkers, electro- and echocardiographic findings within days. Importantly, the available data suggest that the incidence rate of myocarditis in the context of COVID-19 is much greater than the risk of this side effect following vaccination. We conclude that the benefit of vaccination against COVID-19 outweighs the potential risk of myocarditis and pericarditis in both adolescents and adults. Prospective follow-up of patients who have developed these complications after vaccination is required to assess long-term outcomes.

Age is the main determinant of COVID-19 related in-hospital mortality with minimal impact of pre-existing comorbidities, a retrospective cohort study.

BACKGROUND: Age and comorbidities increase COVID-19 related in-hospital mortality risk, but the extent by which comorbidities mediate the impact of age remains unknown. METHODS: In this multicenter retrospective cohort study with data from 45 Dutch hospitals, 4806 proven COVID-19 patients hospitalized in Dutch hospitals (between February and July 2020) from the CAPACITY-COVID registry were included (age 69[58-77]years, 64% men). The primary outcome was defined as a combination of in-hospital mortality or discharge with palliative care. Logistic regression analysis was performed to analyze the associations between sex, age, and comorbidities with the primary outcome. The effect of comorbidities on the relation of age with the primary outcome was evaluated using mediation analysis. RESULTS: In-hospital COVID-19 related mortality occurred in 1108 (23%) patients, 836 (76%) were aged ≥70 years (70+). Both age 70+ and female sex were univariably associated with outcome (odds ratio [OR]4.68, 95%confidence interval [4.02-5.45], OR0.68[0.59-0.79], respectively;both p<  0.001). All comorbidities were univariably associated with outcome (p<0.001), and all but dyslipidemia remained significant after adjustment for age70+ and sex. The impact of comorbidities was attenuated after age-spline adjustment, only leaving female sex, diabetes mellitus (DM), chronic kidney disease (CKD), and chronic pulmonary obstructive disease (COPD) significantly associated (female OR0.65[0.55-0.75], DM OR1.47[1.26-1.72], CKD OR1.61[1.32-1.97], COPD OR1.30[1.07-1.59]). Pre-existing comorbidities in older patients negligibly (<6% in all comorbidities) mediated the association between higher age and outcome. CONCLUSIONS: Age is the main determinant of COVID-19 related in-hospital mortality, with negligible mediation effect of pre-existing comorbidities. TRIAL REGISTRATION: CAPACITY-COVID ( NCT04325412 ).

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

Background The electrocardiogram (ECG) is an easy to assess, widely available and inexpensive tool that is frequently used during the work-up of hospitalized COVID-19 patients. So far, no study has been conducted to evaluate if ECG-based machine learning models are able to predict allcause in-hospital mortality in COVID-19 patients. Purpose With this study, we aim to evaluate the value of using the ECG to predict in-hospital all-cause mortality of COVID-19 patients by analyzing the ECG at hospital admission, comparing a logistic regression based approach and a DNN based approach. Secondly, we aim to identify specific ECG features associated with mortality in patients diagnosed with COVID-19. Methods and results We studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw-format 12-lead ECGs recorded after admission (<72 hours) were collected, manually assessed, and annotated using pre-defined ECG features. Using data from five out of seven centers (n = 634), two mortality prediction models were developed: (a) a logistic regression model using manually annotated ECG features, and (b) a pre-trained deep neural network (DNN) using the raw ECG waveforms. Data from two other centers (n = 248) were used for external validation. Performance of both prediction models was similar, with a mean area under the receiver operating curve of 0.69 [95%CI 0.55- 0.82] for the logistic regression model and 0.71 [95%CI 0.59-0.81] for the DNN in the external validation cohort. After adjustment for age and sex, ventricular rate (OR 1.13 [95% CI 1.01-1.27] per 10 ms increase), right bundle branch block (3.26 [95% CI 1.15-9.50]), ST-depression (2.78 [95% CI 1.03-7.70]) and low QRS voltages (3.09 [95% CI 1.02-9.38]) remained as significant predictors for mortality. Conclusion: This study shows that ECG-based prediction models at admission may be a valuable addition to the initial risk stratification in admitted COVID-19 patients. The DNN model showed similar performance to the logistic regression that needs time-consuming manual annotation. Several ECG features associated with mortality were identified.

Impact of cardiovascular disease and cardiovascular risk factors in hospitalised COVID-19 patients.

BACKGROUND: Hospitalised COVID-19 patients with underlying cardiovascular disease (CVD) and cardiovascular risk factors appear to be at risk of poor outcome. It is unknown if these patients should be considered a vulnerable group in healthcare delivery and healthcare recommendations during the COVID-19 pandemic. METHODS: A systematic literature search was performed to answer the following question: In which hospitalised patients with proven COVID-19 and with underlying CVD and cardiovascular risk factors should doctors be alert to a poor outcome? Relevant outcome measures were mortality and intensive care unit admission. Medline and Embase databases were searched using relevant search terms until 9 June 2020. After systematic analysis, 8 studies were included. RESULTS: Based on the literature search, there was insufficient evidence that CVD and cardiovascular risk factors are significant predictors of mortality and poor outcome in hospitalised patients with COVID-19. Due to differences in methodology, the level of evidence of all studies was graded 'very low' according to the Grading Recommendations Assessment, Development and Evaluation methodology. It is expected that in the near future, two multinational and multicentre European registries (CAPACITY-COVID and LEOSS) will offer more insight into outcome in COVID-19 patients. CONCLUSION: This literature review demonstrated there was insufficient evidence to identify CVD and cardiovascular risk factors as important predictors of poor outcome in hospitalised COVID-19 patients. However, patients with CVD and cardiovascular risk factors remain vulnerable to infectious disease outbreaks. As such, governmental and public health COVID-19 recommendations for vulnerable groups apply to these patients.