Development of post-COVID-19 cardiovascular events: an analysis of clinical features and risk factors from a single hospital retrospective study.

Cardiovascular complications after a SARS-CoV-2 infection are a phenomenon of relevant scientific interest. The aim of this study was to analyze the onset of post-COVID-19 cardiovascular events in patients hospitalized in a tertiary care center. This is a retrospective study conducted on patients hospitalized over a period of three months. The patients were older than 18 years of age and had a diagnosis of COVID-19 infection confirmed from a nasopharyngeal swab sample. Anamnestic and clinical-laboratory data were collected. Cardiovascular events at 30 days were defined as follows: arrhythmias, myocardial infarction, myocarditis, and pulmonary embolism. Univariate analysis (Student's t-test or Mann-Whitney U test, as appropriate) and multivariate analysis (multinomial logistic regression) were applied to the data. A total of 394 patients were included; they were mostly males and had a median age of 65.5 years. Previous cardiovascular disease was present in 14.7% of patients. Oxygen therapy was required for 77.9%, and 53% received anticoagulant therapy. The overall 30-day mortality was 20.3%. A cardiovascular event developed in 15.7% of the subjects. These were mainly pulmonary embolism (9.4%), followed by arrhythmias (3.3%), myocardial infarction (2.3%), and myocarditis (0.8%). Patients who developed cardiovascular events upon univariate analysis were significantly older, with major comorbidities, a more compromised respiratory situation, and a higher mortality rate. Multivariate analysis revealed independent factors that were significantly associated with the development of cardiovascular events: hypertension, endotracheal intubation, and age older than 75 years. In patients with COVID-19, the development of a cardiovascular event occurs quite frequently and is mainly seen in elderly subjects with comorbidities (especially hypertension) in the presence of a severe respiratory picture.

Herpes Simplex Virus Re-Activation in Patients with SARS-CoV-2 Pneumonia: A Prospective, Observational Study.

BACKGROUND: Herpes simplex 1 co-infections in patients with COVID-19 are considered relatively uncommon; some reports on re-activations in patients in intensive-care units were published. The aim of the study was to analyze herpetic re-activations and their clinical manifestations in hospitalized COVID-19 patients, performing HSV-1 PCR on plasma twice a week. METHODS: we conducted a prospective, observational, single-center study involving 70 consecutive patients with severe/critical SARS-CoV-2 pneumonia tested for HSV-1 hospitalized at Azienda Ospedaliero-Universitaria of Modena. RESULTS: of these 70 patients, 21 (30.0%) showed detectable viremia and 13 (62%) had clinically relevant manifestations of HSV-1 infection corresponding to 15 events (4 pneumonia, 5 herpes labialis, 3 gingivostomatitis, one encephalitis and two hepatitis). HSV-1 positive patients were more frequently treated with steroids than HSV-1 negative patients (76.2% vs. 49.0%, p = 0.036) and more often underwent mechanical ventilation (IMV) (57.1% vs. 22.4%, p = 0.005). In the unadjusted logistic regression analysis, steroid treatment, IMV, and higher LDH were significantly associated with an increased risk of HSV1 re-activation (odds ratio 3.33, 4.61, and 16.9, respectively). The association with the use of steroids was even stronger after controlling for previous use of both tocilizumab and IMV (OR = 5.13, 95% CI:1.36-19.32, p = 0.016). The effect size was larger when restricting to participants who were treated with high doses of steroids while there was no evidence to support an association with the use of tocilizumab Conclusions: our study shows a high incidence of HSV-1 re-activation both virologically and clinically in patients with SARS-CoV-2 severe pneumonia, especially in those treated with steroids.

The impact of tocilizumab on respiratory support states transition and clinical outcomes in COVID-19 patients. A Markov model multi-state study.

BACKGROUND: The benefit of tocilizumab on mortality and time to recovery in people with severe COVID pneumonia may depend on appropriate timing. The objective was to estimate the impact of tocilizumab administration on switching respiratory support states, mortality and time to recovery. METHODS: In an observational study, a continuous-time Markov multi-state model was used to describe the sequence of respiratory support states including: no respiratory support (NRS), oxygen therapy (OT), non-invasive ventilation (NIV) or invasive mechanical ventilation (IMV), OT in recovery, NRS in recovery. RESULTS: Two hundred seventy-one consecutive adult patients were included in the analyses contributing to 695 transitions across states. The prevalence of patients in each respiratory support state was estimated with stack probability plots, comparing people treated with and without tocilizumab since the beginning of the OT state. A positive effect of tocilizumab on the probability of moving from the invasive and non-invasive mechanical NIV/IMV state to the OT in recovery state (HR = 2.6, 95% CI = 1.2-5.2) was observed. Furthermore, a reduced risk of death was observed in patients in NIV/IMV (HR = 0.3, 95% CI = 0.1-0.7) or in OT (HR = 0.1, 95% CI = 0.0-0.8) treated with tocilizumab. CONCLUSION: To conclude, we were able to show the positive impact of tocilizumab used in different disease stages depicted by respiratory support states. The use of the multi-state Markov model allowed to harmonize the heterogeneous mortality and recovery endpoints and summarize results with stack probability plots. This approach could inform randomized clinical trials regarding tocilizumab, support disease management and hospital decision making.

Incidence, risk factors and outcome of acute kidney injury (AKI) in patients with COVID-19.

BACKGROUND: Acute kidney injury (AKI) is a severe complication of coronavirus disease-2019 (COVID-19). This study aims to evaluate incidence, risk factors and case-fatality rate of AKI in patients with COVID-19. METHODS: We reviewed the health medical records of 307 consecutive patients with COVID-19 hospitalized at the University Hospital of Modena, Italy. RESULTS: AKI was diagnosed in 69 out of 307 (22.4%) COVID-19 patients. Stages 1, 2, or 3 AKI accounted for 57.9%, 24.6% and 17.3%, respectively. AKI patients had a mean age of 74.7 ± 9.9 years. These patients showed higher serum levels of the main markers of inflammation and higher rate of severe pneumonia than non-AKI patients. Kidney injury was associated with a higher rate of urinary abnormalities including proteinuria (0.44 ± 0.85 vs 0.18 ± 0.29 mg/mg; P = < 0.0001) and microscopic hematuria (P = 0.032) compared to non-AKI patients. Hemodialysis was performed in 7.2% of the subjects and 33.3% of the survivors did not recover kidney function after AKI. Risk factors for kidney injury were age, male sex, CKD and higher non-renal SOFA score. Patients with AKI had a mortality rate of 56.5%. Adjusted Cox regression analysis revealed that COVID-19-associated AKI was independently associated with in-hospital death (hazard ratio [HR] = 4.82; CI 95%, 1.36-17.08) compared to non-AKI patients. CONCLUSION: AKI was a common and harmful consequence of COVID-19. It manifested with urinary abnormalities (proteinuria, microscopic hematuria) and conferred an increased risk for death. Given the well-known short-term sequelae of AKI, prevention of kidney injury is imperative in this vulnerable cohort of patients.

Darunavir/Cobicistat Is Associated with Negative Outcomes in HIV-Negative Patients with Severe COVID-19 Pneumonia.

The aim of this study was to evaluate both positive outcomes, including reduction of respiratory support aid and duration of hospital stay, and negative ones, including mortality and a composite of invasive mechanical ventilation or death, in patients with coronavirus disease 2019 (COVID-19) pneumonia treated with or without oral darunavir/cobicistat (DRV/c, 800/150 mg/day) used in different treatment durations. The secondary objective was to evaluate the percentage of patients treated with DRV/c who were exposed to potentially severe drug-drug interactions (DDIs) and died during hospitalization. This observational retrospective study was conducted in consecutive patients with COVID-19 pneumonia admitted to a tertiary care hospital in Modena, Italy. Kaplan-Meier survival curves and Cox proportional hazards regression were used to compare patients receiving standard of care with or without DRV/c. Adjustment for key confounders was applied. Two hundred seventy-three patients (115 on DRV/c) were included, 75.8% males, mean age was 64.6 (±13.2) years. Clinical improvement was similar between the groups, depicted by respiratory aid switch (p > .05). The same was observed for duration of hospital stay [13.2 (±8.9) for DRV/c vs. 13.4 (±7.2) days for no-DRV/c, p = .9]. Patients on DRV/c had higher rates of mortality (25.2% vs. 10.1%, p < .0001. The rate of composite outcome of mechanical ventilation and death was higher in the DRV/c group (37.4% vs. 25.3%, p = .03). Multiple serious DDI associated with DRV/c were observed in the 19 patients who died. DRV/c should not be recommended as a treatment option for COVID-19 pneumonia outside clinical trials.

Acute myocarditis as the main clinical manifestation of SARS-CoV 2 infection.

We describe a case of acute myocarditis, which was reported as the main COVID-19 clinical manifestation, with a favorabile outcome. In addition to symptoms, laboratory tests (BNP and troponin), echocardiogram and cardiac MRI contributed to diagnosis. Regardless heart biopsy was not obtained, it is likely an immunological pathogenesis of this condition which pave the way to further therapeutic implications, since there are currently no standardized treatments.

Immunomodulation for the management of severe SARS-CoV2 infections. State of the art and review of the literature.

This Mini Review of the literature aimed to assess the role of tocilizumab for the treatment of severe coronavirus disease 2019 (COVID-19). Based on the available scientific evidence, it is not clear to date what is the best therapeutic strategy for the treatment of COVID-19. Since SARS-CoV-2 infection stimulates a vigorous proinflammatory response and may cause the so-called "cytokine storm", immunomodulator drugs have been investigated as potential treatment for severe COVID-19 pneumonia. Among immunomodulators, tocilizumab, a recombinant humanized monoclonal antibody directed against IL-6 receptor, seems to be promising. An increasing number of clinical trials are exploring the role of tocilizumab in COVID-19, focusing on outcomes like mortality, risk of intensive care unit admission and the need for mechanical ventilation. At the moment, there is no conclusive evidence that tocilizumab would be proper outright in all patients with COVID-19 pneumonia, but some studies suggest that its use may be beneficial in selected categories of patients.

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency.

AIMS: The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. METHODS: This was an observational prospective study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients' medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio <150 mmHg in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Shapley Additive exPlanations values were used to quantify the positive or negative impact of each variable included in each model on the predicted outcome. RESULTS: A total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth "boosted mixed model" included 20 variables was selected from the model 3, achieved the best predictive performance (AUC = 0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example. CONCLUSION: This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels.