Long-term prognostic impact of subclinical myocardial dysfunction in patients recovered from COVID-19.

BACKGROUND: Cardiovascular sequelae may occur in patients recovered from coronavirus disease 2019 (COVID-19). Recent studies have detected a considerable incidence of subclinical myocardial dysfunction-assessed with speckle-tracking echocardiography-and of long-COVID symptoms in these patients. This study aimed to define the long-term prognostic role of subclinical myocardial dysfunction and long-COVID condition in patients recovered from COVID-19 pneumonia. METHODS: We prospectively followed up 110 patients hospitalized at our institution due to COVID-19 pneumonia in April 2020 and then recovered from SARS-CoV-2 infection. A 7-month clinical and echocardiographic evaluation was performed, followed by a 21-month clinical follow-up. The primary outcome was major adverse cardiovascular events (MACE), a composite of myocardial infarction, stroke, heart failure hospitalization, and all-cause mortality. RESULTS: A subclinical myocardial dysfunction-defined as an impairment of left ventricular global longitudinal strain (≥-18%)-was identified at a 7-month follow-up in 37 patients (34%), was associated with an increased risk of long-term MACE with a good discriminative power (area under the curve: .73) and resulted in a strong independent predictor of extended MACE in multivariate regression analyses. Long-COVID condition was not associated with a worse long-term prognosis, instead. CONCLUSIONS: In patients recovered from COVID-19 pneumonia, a subclinical myocardial dysfunction is present in one-third of the whole population at 7-month follow-up and is associated with a higher risk of MACE at long-term follow-up. Speckle-tracking echocardiography is a promising tool to optimize the risk-stratification in patients recovered from COVID-19 pneumonia, while the definition of a long-COVID condition has no prognostic relevance.

Subclinical myocardial dysfunction in patients recovered from COVID-19.

BACKGROUND: Myocardial injury (MI) can be detected during the acute phase of Coronavirus disease 19 (COVID-19) and is associated with a dismal prognosis. Recent imaging studies described the persistence of cardiac abnormalities after the recovery. The aim of the study was to investigate the spectrum of cardiac abnormalities at mid-term follow-up in patients recovered from COVID-19 using clinical assessment, laboratory tests, and imaging evaluation with comprehensive echocardiography. METHODS: This is an observational, cross-sectional study assessing an unselected cohort of consecutive patients recovered from COVID-19. MI was defined by elevated plasma levels of high sensitive troponin T (hsTnT). At the follow-up, a complete examination including echocardiography was performed. RESULTS: The 123 patients included were divided into two groups according to the presence of MI during hospitalization: group A (without MI) and group B (with MI). After a median of 85 days, group B patients were more frequently symptomatic for dyspnea and had significantly higher values of hsTnT and N-Terminal prohormone of Brain Natriuretic Peptide (NT-proBNP), compared to Group A. No differences between the two groups in left nor right ventricle dimension and ejection fraction were found. However, in group B a significant reduction of mean left ventricle global longitudinal strain was observed (-15.7±.7 vs -18.1± .3 in group A, p < 0.001), together with higher frequency of impaired diastolic function and higher values of pulmonary pressure. CONCLUSIONS: In patients recovered from COVID-19, echocardiography with speckle-tracking analysis may be an useful imaging tool to identify subclinical myocardial dysfunction and potentially guide management strategies.

In-hospital prognostic role of coronary atherosclerotic burden in COVID-19 patients.

AIMS: Currently, there are few available data regarding a possible role for subclinical atherosclerosis as a risk factor for mortality in Coronavirus Disease 19 (COVID-19) patients. We used coronary artery calcium (CAC) score derived from chest computed tomography (CT) scan to assess the in-hospital prognostic role of CAC in patients affected by COVID-19 pneumonia. METHODS: Electronic medical records of patients with confirmed diagnosis of COVID-19 were retrospectively reviewed. Patients with known coronary artery disease (CAD) were excluded. A CAC score was calculated for each patient and was used to categorize them into one of four groups: 0, 1-299, 300-999 and at least 1000. The primary endpoint was in-hospital mortality for any cause. RESULTS: The final population consisted of 282 patients. Fifty-seven patients (20%) died over a follow-up time of 40 days. The presence of CAC was detected in 144 patients (51%). Higher CAC score values were observed in nonsurvivors [median: 87, interquartile range (IQR): 0.0-836] compared with survivors (median: 0, IQR: 0.0-136). The mortality rate in patients with a CAC score of at least 1000 was significantly higher than in patients without coronary calcifications (50 vs. 11%) and CAC score 1-299 (50 vs. 23%), P < 0.05. After adjusting for clinical variables, the presence of any CAC categories was not an independent predictor of mortality; however, a trend for increased risk of mortality was observed in patients with CAC of at least 1000. CONCLUSION: The correlation between CAC score and COVID-19 is fascinating and under-explored. However, in multivariable analysis, the CAC score did not show an additional value over more robust clinical variables in predicting in-hospital mortality. Only patients with the highest atherosclerotic burden (CAC ≥1000) could represent a high-risk population, similarly to patients with known CAD.

Disentangling the Association of Hydroxychloroquine Treatment with Mortality in Covid-19 Hospitalized Patients through Hierarchical Clustering.

The efficacy of hydroxychloroquine (HCQ) in treating SARS-CoV-2 infection is harshly debated, with observational and experimental studies reporting contrasting results. To clarify the role of HCQ in Covid-19 patients, we carried out a retrospective observational study of 4,396 unselected patients hospitalized for Covid-19 in Italy (February-May 2020). Patients' characteristics were collected at entry, including age, sex, obesity, smoking status, blood parameters, history of diabetes, cancer, cardiovascular and chronic pulmonary diseases, and medications in use. These were used to identify subtypes of patients with similar characteristics through hierarchical clustering based on Gower distance. Using multivariable Cox regressions, these clusters were then tested for association with mortality and modification of effect by treatment with HCQ. We identified two clusters, one of 3,913 younger patients with lower circulating inflammation levels and better renal function, and one of 483 generally older and more comorbid subjects, more prevalently men and smokers. The latter group was at increased death risk adjusted by HCQ (HR[CI95%] = 3.80[3.08-4.67]), while HCQ showed an independent inverse association (0.51[0.43-0.61]), as well as a significant influence of cluster∗HCQ interaction (p < 0.001). This was driven by a differential association of HCQ with mortality between the high (0.89[0.65-1.22]) and the low risk cluster (0.46[0.39-0.54]). These effects survived adjustments for additional medications in use and were concordant with associations with disease severity and outcome. These findings suggest a particularly beneficial effect of HCQ within low risk Covid-19 patients and may contribute to clarifying the current controversy on HCQ efficacy in Covid-19 treatment.

Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study.

BACKGROUND AND AIMS: There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death. METHODS AND RESULTS: Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a geographical gradient, Northern Italian regions featuring more than twofold higher death rates as compared to Central/Southern areas (15.6% vs 6.4%, respectively). Machine learning analysis revealed that the most important features in death classification were impaired renal function, elevated C reactive protein and advanced age. These findings were confirmed by multivariable Cox survival analysis (hazard ratio (HR): 8.2; 95% confidence interval (CI) 4.6-14.7 for age ≥85 vs 18-44 y); HR = 4.7; 2.9-7.7 for estimated glomerular filtration rate levels <15 vs ≥ 90 mL/min/1.73 m2; HR = 2.3; 1.5-3.6 for C-reactive protein levels ≥10 vs ≤ 3 mg/L). No relation was found with obesity, tobacco use, cardiovascular disease and related-comorbidities. The associations between these variables and mortality were substantially homogenous across all sub-groups analyses. CONCLUSIONS: Impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death in a large cohort of unselected patients with COVID-19, admitted to 30 different clinical centres all over Italy.

Early detection of elevated cardiac biomarkers to optimise risk stratification in patients with COVID-19.

OBJECTIVE: Risk stratification is crucial to optimise treatment strategies in patients with COVID-19. We aimed to evaluate the impact on mortality of an early assessment of cardiac biomarkers in patients with COVID-19. METHODS: Humanitas Clinical and Research Hospital (Rozzano-Milan, Lombardy, Italy) is a tertiary centre that has been converted to the management of COVID-19. Patients with confirmed COVID-19 were entered in a dedicated database for cohort observational analyses. Outcomes were stratified according to elevated levels (ie, above the upper level of normal) of high-sensitivity cardiac troponin I (hs-TnI), B-type natriuretic peptide (BNP) or both measured within 24 hours after hospital admission. The primary outcome was all-cause mortality. RESULTS: A total of 397 consecutive patients with COVID-19 were included up to 1 April 2020. At the time of hospital admission, 208 patients (52.4%) had normal values for cardiac biomarkers, 90 (22.7%) had elevated both hs-TnI and BNP, 59 (14.9%) had elevated only BNP and 40 (10.1%) had elevated only hs-TnI. The rate of mortality was higher in patients with elevated hs-TnI (22.5%, OR 4.35, 95% CI 1.72 to 11.04), BNP (33.9%, OR 7.37, 95% CI 3.53 to 16.75) or both (55.6%, OR 18.75, 95% CI 9.32 to 37.71) as compared with those without elevated cardiac biomarkers (6.25%). A multivariate analysis identified concomitant elevation of both hs-TnI and BNP as a strong independent predictor of all-cause mortality (OR 3.24, 95% CI 1.06 to 9.93). CONCLUSIONS: An early detection of elevated hs-TnI and BNP predicts mortality in patients with COVID-19. Cardiac biomarkers should be systematically assessed in patients with COVID-19 at the time of hospital admission in order to optimise risk stratification.