No deterioration in function or volumetric parameters in elite athletes after SARS-CoV-2 infections compared to non-infected athletes: results from the ELITE cohort

Background/Introduction: SARS-CoV-2 (subclinical) myocarditis has been demonstrated in up to 5% in athletes, and is currently a topic being intensively investigated. However, more subtle changes in function and volumetric parameters have been less well documented, especially in elite athletes, who perform at the highest levels of sports, with potentially the most outspoken adaptation. Purpose: To determine differences in cardiac function and volumetric parameters using cardiac magnetic resonance imaging (CMR) in elite athletes recovered from a SARS-CoV-2 infection as compared to non-infected elite athletes (controls). Methods: We included elite athletes from the ELITE (Evaluation of Lifetime Intensive Top-level sports and Exercise) cohort, who voluntary undergo cardiovascular pre-participation screenings, which includes cardiac magnetic resonance imaging (CMR). SARS-CoV-2 infection was diagnosed with a positive-PCR or antibody test (if unvaccinated). The primary outcome was the incidence of structural cardiac changes on CMR, defined as LV/RV BSA indexed-EDV (EDVi), LV/RV BSA indexed-ESV (ESVi), LV/RV EF, presence of pathological late gadolinium enhancement (LGE) (excluding hinge point fibrosis), and T1 times. Results: We included 234 elite athletes, mean age 27 (±7), 39% female, with main athletic disciplines (≥10 hours/week) of cycling (24%), field hockey (13%), and water polo (12%). In total 69 elite athletes had documented SARS-CoV-2 infection, and 165 were documented as not exposed to SARS-CoV-2. The majority reported mild symptoms 61/69 (88%), 1/69 (1%) severe symptoms, and 7/69 (11%) no symptoms. Mean time between infection and CMR was 2.8 (±2) months. CMR showed no significant difference between elite athletes with SARS-CoV-2 and without (Table) in mean LVEDVi (117±19 vs 120±19 ml/m2, p=0.29), LVESVi (50.6±11 vs 53.2±11 ml/m2, p=0.12), LVEF (56.9% ±5 vs 55.8% ±5, p=0.14), RVEDVi (120±20 vs 122±19 ml/m2, p=0.56), RVESVi (54.5±11 vs 56.2±11 ml/m2, p=0.29), and RVEF (54.6% ±4 vs 53.9% ±5, p=0.23). In 4/69 (4.7%) vs 1/165 (1.3%) pathological non-ischemic pattern of myocardial LGE was present (≤20% of total LV mass), of which one athlete (1.2%) showed increased T1 time, all with no deterioration in right and left ventricle function and volumetric parameters (Figure) after SARS-CoV-2 infection. All athletes made a full recovery and returned to elite competitive sports. Conclusion(s): This cross-sectional study of elite athletes demonstrates that infection with SARS-CoV-2 is not associated with deterioration in cardiac function and volumetric parameters on CMR compared with non-infected athletes, also in the small subset of athletes with pathological LGE patterns after SARS-CoV-2 infection. Prospective studies with long-term follow-up are needed to establish whether intensive sports is associated with long-term cardiac deleterious effects in elite athletes exposed to SARS-CoV-2. Funding Acknowledgement: Type of funding sources: Foundation. Main funding source(s): Dutch Heart FoundationDutch National Olympic Committee & National Sports Federation (NOC*NSF)

SARS-CoV-2 associated cardiac abnormalities in elite athletes: results from the prospective, longitudinal COMMIT cohort

Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Dutch National Olympic Committee & National Sports Federation (NOC*NSF) Dutch Heart Foundation Background/Introduction Active myocarditis is regarded as an absolute contra-indication to competitive sports. Subclinical SARS-CoV-2 myocarditis/myocardial damage has been demonstrated 2-5% in athletes. However, the prognosis in elite athletes after SARS-CoV-2 cardiac involvement, with potentially detrimental effects on recovery, is currently unknown. Purpose We aimed to investigate the prevalence and clinical course of cardiac abnormalities in elite athletes after SARS-CoV-2 infection. Methods We retrospectively and prospectively included elite athletes in the COMMIT (COvid-19 Myocardial Manifestations in Intensive Top-level sports) cohort. Outcomes of interest were 1) incidence and clinical course of cardiac abnormalities on CMR, defined as reduced EF, increased EDV, presence of late gadolinium enhancement (LGE) (excluding hinge point fibrosis), increased T1 and/or T2 time);2) clinically important arrhythmias defined as premature ventricular complex, (non-)sustained ventricular tachycardia on exercise ECG or 4-8 days Holter monitoring;3) cardiac- symptoms/ events. SARS-CoV-2 infection was diagnosed with a positive- PCR or antibody test if unvaccinated. Results We included 85 elite SARS-CoV-2 recovered athletes (34% women), mean age 26.5 (±7) years, with main athletic disciplines (≥10 hours/week) football (27%), cycling (12%), water polo (9%), field hockey (9%), and rowing (8%). Mean time between infection and CMR was 2.6 months (±3). Mean CMR LVEDV/BSA was 120.6 ml/m2 (±21), LVEF 57.3% (±5), RVEDV/BSA 126.2 ml/m2 (±22), RVEF 54% (±4), and 1/85 (1.2%) showed increased T1 time after infection. In 4/85 (4.7%) myocardial LGE was present (Figure 1 and 2). In cases with LGE, after 11 (±2) months of follow-up, one demonstrated complete resolution (i.e. no LGE present) after 3 months. One case showed persistent inflammation on three sequential CMRs (1, 3, 6 months post-COVID-19);at 9 months CMR demonstrated no inflammation, but persistent LGE. Two elite athletes had unchanged LGE, one at 3 months, and one at 5 and 9 months. No clinically important arrhythmias were found in athletes with LGE. At a mean follow-up of 7.8 (±3.3) months, no symptoms/events were reported, and all had returned to sports. Pre-/post-SARS-CoV-2 infection CMR was available in 13/85 athletes;in this subgroup, no pathologic LGE or clinically important changes in ventricular volumes/function were found. Conclusion This longitudinal cohort of elite athletes demonstrates that infection with SARS-CoV-2 is associated with 4.7% of myocardial abnormalities, with varying clinical courses. There were no important arrhythmias, and we found no evidence of deleterious effects of sports after COVID-19. Prospective studies with comprehensive arrhythmia monitoring and long-term follow-up are needed to establish whether intensive sports is associated with long-term deleterious cardiac effects. Figure 1. Figure 2.

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.

Post-COVID-19 peri-and myocardial manifestations are less common in athletes than in healthy non-athletes: Findings from a systematic review

Background/Introduction: The prognosis of peri-and myocarditis can be negatively influenced by intensive exercise and sports. Therefore sustained cardiac involvement after recovery from COVID-19 in athletes is of particular relevance for the prevention of sudden cardiac arrest/sudden cardiac death (SCA/SCD). To date, only small sample-size studies are available, or studies predominantly focusing on hospitalized and severely ill patients. We aimed to address this knowledge gap in a comprehensive, systematic review of peri-/myocardial involvement after SARS-CoV-2 infection in athletes versus healthy non-athletes. Purpose: Quantification of peri-/myocardial involvement and risks of SCA/SCD after SARS-CoV-2 infection in athletes as compared with healthy non-athletes. Methods: We performed a systematic search with a combination of key terms in Medline (Ovid), Embase (Ovid) and Scopus (through March 8th 2021). To capture potential non-peer-reviewed COVID-19 SCA/SCD reports we performed monthly scoping internet searches. Inclusion criteria: athletes/non-athletes, with cardiovascular magnetic resonance (CMR) or echocardiography after recovery from SARS-CoV-2 infection, including arrhythmia outcomes. Exclusion criteria: study population ≥1 individual comorbidity and mean age <18/>64 years. Results: We included 16 manuscripts (933 papers reviewed) comprising 1129 athletes (284 college/student-, 807 professional-and 38 elite athletes) and 382 healthy non-athletes. Athletes vs non-athletes reported myocarditis on echocardiography and/or CMR in 0-15% vs 45-60%, LGE in 0-46% vs 0-74% (Figure 1), and pericardial effusion in 8-58% vs 0-47% (Figure 2). Weighted means of diagnosed myocarditis were 3% in athletes (3.5% college/student-, and 0% elite athletes) and 56.6% in non-athletes. No important arrhythmias were reported. Systematic internet query identified 2 collapsed post-COVID-19 athletes during exercise, 1 lethal. Ten studies (n=1301) reporting post-recovery troponin T/I found no clear relationship with cardiac abnormalities. Summary/Conclusions: Rates of peri-/myocardial abnormalities in athletes/ healthy non-athletes after SARS-CoV-2 infection are variable, ranging from 0-74%, and predominantly seen on CMR. Athletes have a lower risk of peri-/myocardial involvement, and myocarditis (0-3.5% vs 56.5%) than non-athletes after SARS-CoV-2 infection. Risks of SCA/SCD appear low, but data are lacking. Troponin screenings seems unreliable to identify athletes at risk for myocardial involvement. Prospective studies, with pre-COVID-19 imaging (CMR), in athletes, including follow-up and arrhythmia monitoring, are urgently needed.

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.