Advanced Imaging Supports the Mechanistic Role of Autoimmunity and Plaque Rupture in COVID-19 Heart Involvement.

The cardiovascular system is frequently affected by coronavirus disease-19 (COVID-19), particularly in hospitalized cases, and these manifestations are associated with a worse prognosis. Most commonly, heart involvement is represented by myocarditis, myocardial infarction, and pulmonary embolism, while arrhythmias, heart valve damage, and pericarditis are less frequent. While the clinical suspicion is necessary for a prompt disease recognition, imaging allows the early detection of cardiovascular complications in patients with COVID-19. The combination of cardiothoracic approaches has been proposed for advanced imaging techniques, i.e., CT scan and MRI, for a simultaneous evaluation of cardiovascular structures, pulmonary arteries, and lung parenchyma. Several mechanisms have been proposed to explain the cardiovascular injury, and among these, it is established that the host immune system is responsible for the aberrant response characterizing severe COVID-19 and inducing organ-specific injury. We illustrate novel evidence to support the hypothesis that molecular mimicry may be the immunological mechanism for myocarditis in COVID-19. The present article provides a comprehensive review of the available evidence of the immune mechanisms of the COVID-19 cardiovascular injury and the imaging tools to be used in the diagnostic workup. As some of these techniques cannot be implemented for general screening of all cases, we critically discuss the need to maximize the sustainability and the specificity of the proposed tests while illustrating the findings of some paradigmatic cases.

An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study.

Introduction: Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning. Material and methods: We conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation. Results: 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality. Conclusions: Machine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.

The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients.

BACKGROUND: Persistence of antibodies to SARS-CoV-2 viral infection may depend on several factors and may be related to the severity of disease or to the different symptoms. METHODS: We evaluated the antibody response to SARS-CoV-2 in personnel from 9 healthcare facilities and an international medical school and its association with individuals' characteristics and COVID-19 symptoms in an observational cohort study. We enrolled 4735 subjects (corresponding to 80% of all personnel) for three time points over a period of 8-10 months. For each participant, we determined the rate of antibody increase or decrease over time in relation to 93 features analyzed in univariate and multivariate analyses through a machine learning approach. RESULTS: Here we show in individuals positive for IgG (≥12 AU/mL) at the beginning of the study an increase [p = 0.0002] in antibody response in paucisymptomatic or symptomatic subjects, particularly with loss of taste or smell (anosmia/dysgeusia: OR 2.75, 95% CI 1.753 - 4.301), in a multivariate logistic regression analysis in the first three months. The antibody response persists for at least 8-10 months. CONCLUSIONS: SARS-CoV-2 infection induces a long lasting antibody response that increases in the first months, particularly in individuals with anosmia/dysgeusia. This may be linked to the lingering of SARS-CoV-2 in the olfactory bulb.

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence.

Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

Clinical Outcomes in the Second versus First Pandemic Wave in Italy: Impact of Hospital Changes and Reorganization

The region of Lombardy was the epicenter of the COVID-19 outbreak in Italy. Emergency Hospital 19 (EH19) was built in the Milan metropolitan area during the pandemic’s second wave as a facility of Humanitas Clinical and Research Center (HCRC). The present study aimed to assess whether the implementation of EH19 was effective in improving the quality of care of COVID-19 patients during the second wave compared with the first one. The demographics, mortality rate, and in-hospital length of stay (LOS) of two groups of patients were compared: the study group involved patients admitted at HCRC and managed in EH19 during the second pandemic wave, while the control group included patients managed exclusively at HCRC throughout the first wave. The study and control group included 903 (56.7%) and 690 (43.3%) patients, respectively. The study group was six years older on average and had more pre-existing comorbidities. EH19 was associated with a decrease in the intensive care unit admission rate (16.9% vs. 8.75%, p <0.001), and an equal decrease in invasive oxygen therapy (3.8% vs. 0.23%, p <0.001). Crude mortality was similar but overlap propensity score weighting revealed a trend toward a potential small decrease. The adjusted difference in LOS was not significant. The implementation of an additional COVID-19 hospital facility was effective in improving the overall quality of care of COVID-19 patients during the first wave of the pandemic when compared with the second. Further studies are necessary to validate the suggested approach.

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review.

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

The effect of COVID-19 epidemic on vital signs in hospitalized patients: a pre-post heat-map study from a large teaching hospital.

The Lombardy SARS-CoV-2 outbreak in February 2020 represented the beginning of COVID-19 epidemic in Italy. Hospitals were flooded by thousands of patients with bilateral pneumonia and severe respiratory, and vital sign derangements compared to the standard hospital population. We propose a new visual analysis technique using heat maps to describe the impact of COVID-19 epidemic on vital sign anomalies in hospitalized patients. We conducted an electronic health record study, including all confirmed COVID-19 patients hospitalized from February 21st, 2020 to April 21st, 2020 as cases, and all non-COVID-19 patients hospitalized in the same wards from January 1st, 2018 to December 31st, 2018. All data on temperature, peripheral oxygen saturation, respiratory rate, arterial blood pressure, and heart rate were retrieved. Derangement of vital signs was defined according to predefined thresholds. 470 COVID-19 patients and 9241 controls were included. Cases were older than controls, with a median age of 79 vs 76 years in non survivors (p = < 0.002). Gender was not associated with mortality. Overall mortality in COVID-19 hospitalized patients was 18%, ranging from 1.4% in patients below 65 years to about 30% in patients over 65 years. Heat maps analysis demonstrated that COVID-19 patients had an increased frequency in episodes of compromised respiratory rate, acute desaturation, and fever. COVID-19 epidemic profoundly affected the incidence of severe derangements in vital signs in a large academic hospital. We validated heat maps as a method to analyze the clinical stability of hospitalized patients. This method may help to improve resource allocation according to patient characteristics.

Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.

The Use of Antiviral Agents against SARS-CoV-2: Ineffective or Time and Age Dependent Result? A Retrospective, Observational Study among COVID-19 Older Adults.

BACKGROUND: Our aim was to investigate the impact of therapeutics with antiviral activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on mortality of older adults affected by coronavirus disease 2019 (COVID-19), taking into consideration the time interval from symptoms onset to drugs administration. METHODS: Data from 143 COVID-19 patients over 65 years of age admitted to the Humanitas Clinical and Research Center Emergency Department (Milan, Italy) and treated with Lopinavir/ritonavir (LPV/r) or Darunavir/cobicistat (DVR/c) associated to Hydroxychloroquine (HCQ) were retrospectively analyzed. Statistical analysis was performed by using a logistic regression model and survival analysis to assess the role of different predictors of in-hospital mortality, including an early (<6 days from symptoms onset) vs. late treatment onset, signs and symptoms at COVID-19 presentation, type of antiviral treatment (LPV/r or DVR/c) and patients' age (65-80 vs. >80 years old). RESULTS: Multivariate analysis showed that an older age (OR: 2.54) and dyspnea as presenting symptom (OR: 2.01) were associated with higher mortality rate, whereas cough as presenting symptom (OR: 0.53) and a timely drug administration (OR: 0.44) were associated with lower mortality. Survival analysis demonstrated that the timing of drug administration had an impact on mortality in 65-80 years-old patients (p = 0.02), whereas no difference was seen in those >80 years-old. This impact was more evident in patients with dyspnea as primary symptom of COVID-19, in whom mortality decreased from 57.1% to 38.3% due to timely drug administration (OR: 0.5; p = 0.04). CONCLUSIONS: There was a significant association between the use of a combined antiviral regimen and HCQ and lower mortality, when timely-administered, in COVID-19 patients aged 65-80 years. Our findings support timely treatment onset as a key component in the treatment of COVID-19.

The role of anti-hypertensive treatment, comorbidities and early introduction of LMWH in the setting of COVID-19: A retrospective, observational study in Northern Italy.

BACKGROUND: There is a great deal of debate about the role of cardiovascular comorbidities and the chronic use of antihypertensive agents (such as ACE-I and ARBs) on mortality on COVID-19 patients. Of note, ACE2 is responsible for the host cell entry of the virus. METHODS: We extracted data on 575 consecutive patients with laboratory-confirmed SARS-CoV-2 infection admitted to the Emergency Department (ED) of Humanitas Center, between February 21 and April 14, 2020. The aim of the study was to evaluate the role of chronic treatment with ACE-I or ARBs and other clinical predictors on in-hospital mortality in a cohort of COVID-19 patients. RESULTS: Multivariate analysis showed that a chronic intake of ACE-I was associated with a trend in reduction of mortality (OR: 0.53; 95% CI: 0.27-1.03; p = 0.06), differently from a chronic intake of ARB (OR: 1.1; 95% CI: 0.5-2.8; p=0.8). Increased age (ORs ranging from 3.4 to 25.2 and to 39.5 for 60-70, 70-80 and >80 years vs <60) and cardiovascular comorbidities (OR: 1.90; 95% CI: 1.1-3.3; p = 0.02) were confirmed as important risk factors for COVID-19 mortality. Timely treatment with low-molecular-weight heparin (LMWH) in ED was found to be protective (OR: 0.36; 95% CI: 0.21-0.62; p < 0.0001). CONCLUSIONS: This study can contribute to understand the reasons behind the high mortality rate of patients in Lombardy, a region which accounts for >50% of total Italian deaths. Based on our findings, we support that daily intake of antihypertensive medications in the setting of COVID-19 should not be discontinued and that a timely LMWH administration in ED has shown to decrease in-hospital mortality.

Effectiveness of Streptococcus Pneumoniae Urinary Antigen Testing in Decreasing Mortality of COVID-19 Co-Infected Patients: A Clinical Investigation.

BACKGROUND AND OBJECTIVES: Streptococcus pneumoniae urinary antigen (u-Ag) testing has recently gained attention in the early diagnosis of severe and critical acute respiratory syndrome coronavirus-2/pneumococcal co-infection. The aim of this study is to assess the effectiveness of Streptococcus pneumoniae u-Ag testing in coronavirus disease 2019 (COVID-19) patients, in order to assess whether pneumococcal co-infection is associated with different mortality rate and hospital stay in these patients. MATERIALS AND METHODS: Charts, protocols, mortality, and hospitalization data of a consecutive series of COVID-19 patients admitted to a tertiary hospital in northern Italy during COVID-19 outbreak were retrospectively reviewed. All patients underwent Streptococcus pneumoniae u-Ag testing to detect an underlying pneumococcal co-infection. Covid19+/u-Ag+ and Covid19+/u-Ag- patients were compared in terms of overall survival and length of hospital stay using chi-square test and survival analysis. RESULTS: Out of 575 patients with documented pneumonia, 13% screened positive for the u-Ag test. All u-Ag+ patients underwent treatment with Ceftriaxone and Azithromycin or Levofloxacin. Lopinavir/Ritonavir or Darunavir/Cobicistat were added in 44 patients, and hydroxychloroquine and low-molecular-weight heparin (LMWH) in 47 and 33 patients, respectively. All u-Ag+ patients were hospitalized. Mortality was 15.4% and 25.9% in u-Ag+ and u-Ag- patients, respectively (p = 0.09). Survival analysis showed a better prognosis, albeit not significant, in u-Ag+ patients. Median hospital stay did not differ among groups (10 vs. 9 days, p = 0.71). CONCLUSIONS: The routine use of Streptococcus pneumoniae u-Ag testing helped to better target antibiotic therapy with a final trend of reduction in mortality of u-Ag+ COVID-19 patients having a concomitant pneumococcal infection. Randomized trials on larger cohorts are necessary in order to draw definitive conclusion.

Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation.

OBJECTIVE: Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March 2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support devices. In our Institution, most patients received chest CT at admission, only interpreted visually. Given the proven value of quantitative CT analysis (QCT) in the setting of ARDS, we tested QCT as an outcome predictor for COVID-19. METHODS: We performed a single-centre retrospective study on COVID-19 patients hospitalised from January 25, 2020, to April 28, 2020, who received CT at admission prompted by respiratory symptoms such as dyspnea or desaturation. QCT was performed using a semi-automated method (3D Slicer). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient's clinical data including oxygenation support throughout hospitalisation. RESULTS: Two hundred twenty-two patients (163 males, median age 66, IQR 54-6) were included; 75% received oxygenation support (20% intubation rate). Compromised lung volume was the most accurate outcome predictor (logistic regression, p < 0.001). %CL values in the 6-23% range increased risk of oxygenation support; values above 23% were at risk for intubation. %CL showed a negative correlation with PaO2/FiO2 ratio (p < 0.001) and was a risk factor for in-hospital mortality (p < 0.001). CONCLUSIONS: QCT provides new metrics of COVID-19. The compromised lung volume is accurate in predicting the need for oxygenation support and intubation and is a significant risk factor for in-hospital death. QCT may serve as a tool for the triaging process of COVID-19. KEY POINTS: • Quantitative computer-aided analysis of chest CT (QCT) provides new metrics of COVID-19. • The compromised lung volume measured in the - 500, 100 HU interval predicts oxygenation support and intubation and is a risk factor for in-hospital death. • Compromised lung values in the 6-23% range prompt oxygenation therapy; values above 23% increase the need for intubation.