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Objectives: The study aimed to design and develop a monitoring system to assess the possible implications of the COVID-19 infection and the measures taken to limit its spreading on adherence to chronic therapies. Methods: Within the HEALTH-DB project and in collaboration with a pool of Local Healthcare Entities, a monitoring system called "fail-to-refill" was designed to evaluate the lack of adherence to chronic therapies in Italian clinical practice settings. Based on the date and dosage coverage of last prescription, all patients that should have refilled in the last month a prescription for chronic therapies are identified, and it is verified if they had the refill. The analysis was centred on two classes of chronic treatments, belonging to different distribution systems: lipid-lowering agents (distributed in community pharmacy) and biological therapies for chronic autoimmune conditions (dispensed by National Health System hospitals for outpatients use, ie direct distribution). The monthly analysis covered the no-COVID-19 period (01/2017-02/2020) and the COVID-19 period (03/2020-12/2020). Results: During the COVID-19 period, in May 2020, an increase (42%) of the fail-to-refill rate for lipid-lowering agents distributed in community pharmacies was observed, compared to the rate during the no-COVID-19 period (34% -35% during 2017-2019), while negligible changes were observed in the following months. Regarding the direct distribution, the fail-to-refill rate of biologics was higher during the COVID-19 period, 34% (May), 35% (June), and 37% (July) versus 26-30% (May 2017-2019), 28-29% (June 2017-2021), and 24-28% (July 2017-2019) of the no-COVID-19 period. Conclusions: During the COVID-19 pandemic, an increasing trend of failed refill to chronic therapies has been observed, especially among biologics, probably due to their dispensing system and the difficulty of accessing hospitals. The "fail-to-refill" monitoring system could support the Health Authorities to identify patients who do not correctly refill their prescriptions, thus optimizing the medication adherence and reducing negative clinical outcomes related to it.
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Objectives: To estimate the prognostic factors underlying severity of Sars-Cov-2 infection using a machine learning approach. Methods: The analysis is based on administrative databases of Italian Entities. Patients who were hospitalized with COVID-19 diagnosis (ICD-9 078.89) after 1st January 2020 were included into the dataset together with 13 relevant features representing age, sex and clinical history of each patient. Each record was labelled as 0 (hospitalized patients) or 1 (patients in intensive care or deceased). KerasTuner was used to define the architecture of the Neural Network achieving good accuracy score. To identify prognostic factors underlying severity of Sars Cov-2 infection, feature’s importance was evaluated starting from a Random Forest Classifier. Results: The preliminary dataset built contains 10.448 records from 9.346 hospitalized patients. The selected neural network is made of 13 input nodes, each one representing a feature, 1024 nodes in the hidden layer, processing information that comes from the input layer, and 2 nodes in the output layer, each one representing a label to define patient’s condition. The neural network obtained was able to achieve 64% of accuracy on the testing set. The condition of approximately 2 out of 3 patients was correctly predicted just by analysing their features. The feature’s importance computed from the Random Forest Classifier indicated that patient’s age is the primary prognostic factor underlying severity of Sars Cov-2 infection. The combination of the other features slightly improved model’s performance. Conclusions: The preliminary analysis shows that age is a prognostic factor of fundamental importance in defining the severity of Sars Cov-2 infection. The model obtained could be used to predict disease progression in patients most at risk by analysing their information in the databases. The model will be further improved through a process of feature selection to increase its accuracy and to allow the identification of other prognostic factors.
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Background: Right Ventricular (RV) dysfunction and pulmonary hypertension (PH) are two very likely acute and long term targets of COVID-19 pneumonia, with a potential prognostic implications. Purpose: To determine the COVID-19 pneumonia effects on the right ventricular to pulmonary circulation coupling through bedside echocardiography and extend its implications to prognostic assessment. Methods: Single-centre study including consecutive subjects hospitalized for COVID-19 pneumonia who underwent a clinical indicated echocardiogram between March 2020 and December 2020. Extensive analysis of cardiac function was performed offline by an operator blinded to clinical data, laboratory findings and CT scans. Results: 133 patients were enrolled (mean age 69±12 years, 57% men), 38% of whom already had cardiac disease in their medical history. Inhospital mortality was 26% (35 pts), during a mean hospital stay of 26±16 days. Non survivors had higher pulmonary artery systolic pressure (PASP) and worse RV function, assessed with both standard parameters (i.e. TAPSE) and with the novel speckle tracking analysis by RV-Global Longitudinal Strain (RV-GLS) and RV-Free Wall Longitudinal Strain (RV-FWLS). The combination of these two variables in TAPSE/PASP ratio allows assessment of RV to pulmonary circulation (Pc) coupling and was strongly associated with in-hospital death (HR 0.73, 95% CI 0.59-0.89, p=0.003) and patients with TAPSE/PASP<0.57 mm/mmHg had a more than 4-fold increased risk of in-hospital death (HR 4.8, 95% CI 1.7-13.1, p=0.003). In patients where speckle tracking analysis was feasible, we examined RVGLS/ PASP and RV-FWLS/PASP and found that it was associated with inhospital mortality. The best cut-offs for predicting in-hospital mortality was 0.51 for RV-GLS/PASP (94% sensitivity and 59% specificity) and 0.49 for RV-free wall LS (87% sensitivity and 70% specificity). At the multivariable analysis RV to Pc remained associated with in-hospital death after adjustments for age, PaO2/FiO2, LVEF, and severity of lung involvement at the CT. Conclusions: Either PH and RV dysfunction predict in-hospital mortality in patients with COVID-19 pneumonia. The assessment of RV to Pc coupling, however, better describes the adaptive RV response to increased PASP and gives additional prognostic information in a population with a relevant prevalence of comorbidities. (Figure Presented).
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Objective: To explore cognitive/behavioral features With in two months from hospital discharge in a cohort of patients with COVID-19. Background: Although the epidemiological and clinical features of COVID-19 patients are well characterized, the psychological impact of SARS-CoV-2 has been given little attention. Design/Methods: 49 patients with confirmed COVID-19 underwent neuropsychological assessment With in two months from hospital discharge. The presence of mood alterations and/or features of post-traumatic stress disorder (PTSD) was also investigated. The total sample was split based on age (yC-19, age<50: N=8;mC-19, age range 50-64: N=21;oC-19, age≥65: N=20) and the frequencies of cognitive and behavioral alterations were reported for the total sample and each sub-group. Correlations were tested between neuropsychological scores and the severity of respiratory symptoms at hospital admission. Results: 5 patients (1 yC-19, 4 mC-19) presented with depressive symptoms and 9 (3 yC-19, 4 mC-9, 2 oC-19) reported PTSD features. 45% of the total sample showed executive dysfunctions and difficulties in encoding new verbal material;30% showed visuospatial difficulties, and 25% long-term verbal and nonverbal memory problems. No patients showed language disturbances. Specifically, the yC-19 group showed the worst profile, with 60-75% patients showing executive dysfunctions and encoding difficulties, 50% pure visuospatial dysfunctions and 40% primary long-term memory problems. The mC-19 group showed primary executive (>40%) and visuospatial (25%) dysfunctions. 40% of the oC-19 group showed executive dysfunctions, 30% poor visuospatial abilities, and 25% long-term memory problems. The total sample showed a negative relationship between frontal executive performances and severity of acute-phase respiratory symptoms at hospital admission. Conclusions: With in two months from hospital discharge, cognitive/behavioral alterations are associated with COVID-19 infection, with more severe outcomes in the youngest group. Whether these alterations are linked with the infection itself or with its related consequences has to be determined, as well as whether they are reversible or part of a neurodegenerative process.
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The pandemic caused by the new SARS-CoV-2, named coronavirus disease 2019 (COVID-19) disease, has challenged the health-care systems and raised new diagnostic pathways and safety issues for cardiac imagers. Myocardial injury may complicate COVID-19 infection in more than a quarter of patients and due to the wide a range of possible insults, cardiac imaging plays a crucial diagnostic and prognostic role. There is still little evidence regarding the best-imaging pathway and the echocardiographic findings. Most of the data derive from the single centers experiences and case-reports;therefore, our review reflects the recommendations mainly based on expert opinion. Moreover, knowledge is constantly evolving. The health-care system and physicians are called to reorganize the diagnostic pathways to minimize the possibility of spreading the infection. Thus a rapid, bedside, ultrasound assessment of the heart, chest, and leg veins by point-of-care ultrasound seems to be the first-line tool of the fight against the SARS-CoV-2. A second Level of cardiac imaging is appropriate when the result may guide decision-making or may be life-saving. Dedicated scanners should be used and special pathways should be reserved for these patients. The current knowledge on cardiac imaging COVID-19 patients is reviewed.
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The main focus of Coronavirus disease 2019 (COVID-19) infection is pulmonary complications through virus-related neurological manifestations, ranging from mild to severe, such as encephalitis, cerebral thrombosis, neurocognitive (dementia-like) syndrome, and delirium. The hospital screening procedures for quickly recognizing neurological manifestations of COVID-19 are often complicated by other coexisting symptoms and can be obscured by the deep sedation procedures required for critically ill patients. Here, we present two different case-reports of COVID-19 patients, describing neurological complications, diagnostic imaging such as olfactory bulb damage (a mild and unclear underestimated complication) and a severe and sudden thrombotic stroke complicated with hemorrhage with a low-level cytokine storm and respiratory symptom resolution. We discuss the possible mechanisms of virus entrance, together with the causes of COVID-19-related encephalitis, olfactory bulb damage, ischemic stroke, and intracranial hemorrhage.
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Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared as a pandemic by the World Health Organization (WHO) on 11 March 2020. Clinical presentation ranges from asymptomatic to acute respiratory distress syndrome (ARDS) that can lead to death. Patients with concomitant cardiac diseases have an extremely poor prognosis, and SARS-CoV-2 may cause direct acute and chronic damage to the cardiovascular system. Echocardiography may provide useful information, especially in critical care patients, because it can be performed quickly at the bedside. However, the recommendations relating to the use of echocardiography in the COVID-19 pandemic must be considered only as expert suggestions due to the lack of evidence-based scientific outcome data. To date, there is no means to predict the impact of the virus on patient outcome probably because the pathophysiology of COVID-19 remains unexplained. Purpose: To assess the prognostic utility of quantitative 2D-echocardiography, including strain, in patients with COVID-19 disease. Methods: COVID-19 patients admitted to the San Paolo University Hospital of Milan, that underwent a clinically indicated echocardiographic exam were included in the study. To limit contamination all measurements were performed offline. Quantitative measurements were obtained by an operator blinded to the clinical data. Results: Among the 49 patients, non-survivors (33%) had worse respiratory parameters, index of multiorgan failure and worse markers of lung involvement. Right Ventricular (RV) dysfunction (as assessed by conventional and 2-dimensional speckle tracking, fig. 1) was a common finding and a powerful independent predictor of mortality. At the ROC curve analyses, RV free-wall longitudinal strain (LS) showed an AUC 0.77 ± 0.08 in predicting death, p = 0.008, and global RV LS (RV-GLS) showed an AUC 0.79 ± 0.04, p = 0.004. This association remained significant after correction for age (OR= 1.16, 95%CI 1.01-1.34, p = 0.029 for RV free-wall LS and OR = 1.20, 95%CI 1.01-1.42, p = 0.033 for RV-GLS), for oxygen partial pressure at arterial gas analysis/fraction of inspired oxygen (OR= 1.28, 95%CI 1.04-1.57, p = 0.021 for RV free wall-LS and OR = 1.30, 95%CI 1.04-1.62, p = 0.020 for RV-GLS) and for the severity of pulmonary involvement measured by a computed tomography lung score (OR = 1.27, 95%CI 1.02-1.19, p = 0.034 for RV free-wall LS, and OR = 1.30, 95%CI 1.04-1.63, p = 0.022 for RV-GLS). Conclusions: In patients hospitalized with COVID-19, offline quantitative 2D-echocardiographic assessment of cardiac function is feasible. Parameters of RV function are frequently abnormal and have an independent prognostic value over markers of lung involvement. Early identification of RV dysfunction with speckle tracking might be useful not only to guide management acutely (i.e. fluid management, monitoring high-PEEP response in intubated patients) but also to tailor follow-up subsequently.