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Introduction: Cardiac injury occurs in about 20-30% of patients hospitalized for COVID-19 and influence the prognosis but many aspects like the role of age and magnitude of cardiac damage in determining the prognosis, remains vague. Hypothesis: Age and magnitude of cardiac damage may influence the mortality of patients hospitalized for COVID-19. Methods: We considered all patients consecutively admitted at a third-level European Hospital for COVID-19 between February and June 2020. Cardiac injury was defined as a high-sensitivity cardiac Troponin I (hs-cTnI) value greater than the upper reference limit (URL) of 47 ng/dL. Firstly, we analyzed the data by hs-cTnI across age tertiles (<62 years, 62-73 years and >73 years). Then, we compared patients with no-damage, mid-damage (hs-cTnI up to 10-fold URL) and high-damage (more than 10-fold URL). The primary endpoint was in-hospital mortality. Results: We enrolled 543 patients (median age 69, 67% males);hs-cTnI was available in 509. The survival was lower in elderly patients and high levels of hs-cTnI worsened the prognosis across all age tertiles (Fig. 1A). Surprisingly, the magnitude of cardiac damage did not influence the overall in-hospital mortality (Fig. 1B), but patients with high-damage died earlier (survival at 15 days: 86% nodamage vs 61% mid-damage vs 49% high-damage;p<0.001). Of note, among patients with highdamage, only 7 received coronary angiography, cardiac magnetic resonance or heart biopsy. Conclusions: Cardiac injury dramatically increased the mortality across all ages in patients hospitalized for COVID-19. The magnitude of cardiac damage did not influence overall in-hospital mortality but almost all patients with high-damage died within 15 days from admission. A secondlevel diagnostic test was performed seldomly in high-damage patients, suggesting that the unexpected high burden of the first COVID-19 wave negatively influenced the health system and our clinical daily practice.
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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.