ABSTRACT
Background: MessengerRNA (mRNA) COVID-19 vaccination has been associated with a higher-than-expected occurrence of acute myocarditis. Scarce information is available on mid-term prognosis and changes in cardiac function, volumes, and tissue characterization on cardiac magnetic resonance (CMR). Method(s): Retrospective, multicenter study including patients with a definite diagnosis of acute myocarditis within 30 days from mRNA COVID-19 vaccination, with a confirmed myocarditis diagnosis based on endomyocardial biopsy (EMB) or autopsy or by the coexistence of positive biomarkers (troponin >99th upper reference limit or elevated creatine kinase myocardial band [CK-MB]) and cardiac MRI findings consistent with AM according to the 2018 updated Lake Louise Criteria. Result(s): 77 patients (median age 25 years [IQR 20-35], 15% female) were included and followed-up for 147 days [IQR 74-215]. Follow-up CMR was available in n=49 patients and showed no changes in biventricular ejection fraction (EF) as compared to CMR at diagnosis (left ventricular EF: 59%[55-65]vs. 60%[57-64], p=0.507, right ventricular EF: 56%[52-62]vs. 57%[52-61], p=0.563, respectively). Late gadolinium enhancement was present in all patients at diagnosis and persisted in only n=39 (79.6%) at follow-up (p=0.001), generally sparing the anterior wall and the septum. N=10 (20.4%) had a persistent edema based on T2-weighted short tau inversion recovery (STIR) sequences, with predominant involvement of inferior or inferiorlateral walls. The proportion of patients with increased T1 and T2 mapping signals significantly decreased at follow-up (n=13 (68%) vs. n=4 (13%),p<0.001, and n=21 (84%) vs. n=3 (10%),p<0.001, respectively), as well as the presence of pericardial effusion (n=16 (33%) vs. n=3 (6%),p=0.004). No differences in morpho-functional CMR parameters based on the type of vaccine administered were found (BNT162b2 Pfizer/BioNTech, n=36, 73.5%, m-RNA-1273 Moderna, n=13, 26.5%). Among patients with available follow-up (N=75, 97.4%), no major adverse cardiovascular events nor myocarditis recurrence or death were reported. Conclusion(s): At mid-term follow-up, patients who experienced an acute myocarditis after a mRNA COVID-19 vaccine had preserved biventricular EF. The rate and localization of residual scar or edema on CMR is in line with classic viral myocarditis with a good prognosis. This new piece of information should further reassure patients who experience acute myocarditis after mRNA COVID-19 vaccination.
ABSTRACT
Aims: Several risk factors have been identified to predict worse outcomes in patients affected by SARS-CoV-2 infection. Prediction models are needed to optimize clinical management and to early stratify patients at a higher mortality risk. Machine learning (ML) algorithms represent a novel approach to identify a prediction model with a good discriminatory capacity to be easily used in clinical practice. Methods and results: The Cardio-COVID is a multicentre observational study that involved a cohort of consecutive adult Caucasian patients with laboratory-confirmed COVID-19 [by real time reverse transcriptase-polymerase chain reaction (RT-PCR)] who were hospitalized in 13 Italian cardiology units from 1 March to 9 April 2020. Patients were followed-up after the COVID-19 diagnosis and all causes in-hospital mortality or discharge were ascertained until 23 April 2020. Variables with more than 20% of missing values were excluded. The Lasso procedure was used with a λ=0.07 for reducing the covariates number. Mortality was estimated by means of a Random Forest (RF). The dataset was randomly divided in two subsamples with the same percentage of death/alive people of the entire sample: training set contained 80% of the data and test set the remaining 20%. The training set was used in the calibration procedure where a RF models in-hospital mortality with the covariates selected by Lasso. Its accuracy was measured by means of the ROC curve, obtaining AUC, sensitivity, specificity, and related 95% confidence interval (CI) computed with 10 000 stratified bootstrap replicates. From the RF the relative Variable Importance Measure (relVIM) was extracted to understand which of the selected variables had the greatest impact on outcome, providing a ranking from the most (relVIM=100) to the less important variable. The model obtained was compared with the Gradient Boosting Machine (GBM) and with the logistic regression, where the predictions were cross validated. Finally, to understand if each model has the same performance in sample (training) and out of sample (test), the two AUCs were compared by means of the DeLong's test. Among 701 patients enrolled (mean age 67.2±13.2 years, 69.5% males), 165 (23.5%) died during a median hospitalization of 15 (IQR, 9-24) days. Variables selected by the Lasso were: age, Oxygen saturation, PaO2/FiO2, Creatinine Clearance and elevated Troponin. Compared with those who survived, deceased patients were older, had a lower blood oxygenation, a lower creatinine clearance levels and higher prevalence of elevated Troponin (all P<0.001). Training set included 561 patients and test set 140 patients. The best performance out of sample was provided by the RF with an AUC of 0.78 (95% CI: 0.68-0.88) and a sensitivity of 0.88 (95% CI: 0.58-1.00). Moreover, RF is the unique methodology that provided similar performance in sample and out of sample (DeLong test P=0.78). On the contrary, prediction model was less accurate by using GBM and logistic regression. The relVIM ranked the variables from the most to the less important in predicting the outcome as follows: clearance creatinine, PaO2/FiO2, age, oxygen saturation, and elevated Troponin. Conclusions: In a large COVID-19 population, we showed that a customizable MLbased score derived from clinical variables, is feasible and effective for the prediction of in-hospital mortality.
ABSTRACT
INTRODUCTION: COVID-19 infection is commonly complicated with pro-thrombotic state and endothelial dysfunction. While several studies reported a high incidence of venous thromboembolic events. The occurrence of arterial thromboses are yet rarely described and could be underestimated. OBJECTIVES: To describe the clinical and biological characteristics of COVID-19 patients presenting with an associated arterial thromboembolic event. MATERIAL AND METHODS: We performed a retrospective multicentric study in 3 centers between France and Italy. All patients with a confirmed SARS-CoV-2 infection and arterial thromboembolic events were included in the analysis. RESULTS: From March 8th to April 25th 2020, we identified 20 patients (24 events) with arterial thromboembolic events over 209 admitted patients (9.6%) with severe COVID-19 infection. Arterial thrombotic events included acute coronary occlusions (n = 9), stroke (n = 6), limb ischemia (n = 3), splenic infarcts (n = 3), aortic thrombosis (n = 2) and occlusive mesenteric ischemia (n = 1). At the time of the event, 10/20 (50%) of patients received thromboprohylaxis, 2/20 (10%) were receiving treatment dose anticoagulation and 5/20 (25%) were receiving antiplatelet therapy. CONCLUSION: Our observations suggest that serious arterial thrombotic events might occur in Covid-19 patients. However, the exact incidence of such events and the best way to prevent them yet remains to be investigated.