RESUMO
Clinical deterioration of COVID-19 patients is still a challenging event to predict in the emergency department (ED). The present study developed an artificial neural network using textual and tabular data from ED electronic medical reports. Predicted outcomes were 30-day mortality and ICU admission. Consecutive patients between February 20 and May 5, 2020, from Humanitas Research Hospital and San Raffaele Hospital, in the Milan area, were included. COVID-19 patients were 1296. Textual predictors were patient history, physical exam, and radiological reports. Tabular predictors were age, creatinine, C-reactive protein, hemoglobin, and platelet count. Tabular-textual model performance indices were compared to a model implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular model, with AUC 0.84 ± 0.02, F-1 score 0.56 ± 0.04 and an MCC 0.44 ± 0.04. Tabular model performance was: AUC 0.84 ± 0.02, F-1 score 0.55 ± 0.03 and MCC 0.43 ± 0.04. As for ICU admission, the combined model was not superior to the tabular one. The present data points to the effectiveness of a textual and tabular model for COVID-19 prognosis prediction. Also, it may support the ED physician in their decision-making process.
Assuntos
COVID-19RESUMO
Background The coronavirus disease 2019 (COVID-19) presents an urgent threat to global health. Identification of predictors of poor outcomes will assist medical staff in treatment and allocating limited healthcare resources. Aims The primary aim was to study the value of D-dimer as a predictive marker for in-hospital mortality. Methods This was a cohort study. The study population consisted of hospitalized patients (age >18 years), who were diagnosed with COVID-19 based on real-time PCR at 9 hospitals during the first COVID-19 wave in Lombardy, Italy (Feb-May 2020). The primary endpoint was in-hospital mortality. Information was obtained from patient records. Statistical analyses were performed using a Fine-Gray competing risk survival model. Model discrimination was assessed using Harrells C-index and model calibration was assessed using a calibration plot. Results Out of 1049 patients, 501 patients had evaluable data. Of these 501 patients, 96 died. The cumulative incidence of in-hospital mortality within 30 days was 20% (95CI: 16%-23%), and the majority of deaths occurred within the first 10 days. A prediction model containing D-dimer as the only predictor had a C-index of 0.66 (95%CI: 0.61-0.71). Overall calibration of the model was very poor. The addition of D-dimer to a model containing age, sex and co-morbidities as predictors did not lead to any meaningful improvement in either the C-index or the calibration plot. Conclusion The predictive value of D-dimer alone was moderate, and the addition of D-dimer to a simple model containing basic clinical characteristics did not lead to any improvement in model performance.
Assuntos
COVID-19RESUMO
Due to the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), deepening the host genetic contribution to severe COVID-19 may further improve our understanding about underlying disease mechanisms. Here, we describe an extended GWAS meta-analysis of 3,260 COVID-19 patients with respiratory failure and 12,483 population controls from Italy, Spain, Norway and Germany, as well as hypothesis-driven targeted analysis of the human leukocyte antigen (HLA) region and chromosome Y haplotypes. We include detailed stratified analyses based on age, sex and disease severity. In addition to already established risk loci, our data identify and replicate two genome-wide significant loci at 17q21.31 and 19q13.33 associated with severe COVID-19 with respiratory failure. These associations implicate a highly pleiotropic ~0.9-Mb 17q21.31 inversion polymorphism, which affects lung function and immune and blood cell counts, and the NAPSA gene, involved in lung surfactant protein production, in COVID-19 pathogenesis.
Assuntos
COVID-19 , Insuficiência RespiratóriaRESUMO
Leveraging the unique biological resource based upon the initial COVID-19 patients in Policlinico di Milano (Italy), our study provides the first metabolic profile associated with a fatal outcome. The identification of potential predictive biomarkers offers a vital opportunity to employ metabolomics in a clinical setting as diagnostic tool of disease prognosis upon hospital admission.
Assuntos
COVID-19RESUMO
Introduction: During the recent outbreak of COVID-19 (Corona VIrus Diseases 2019), Lombardy was the Italian region most affected, with 87,000 patients and 15,876 deaths (until May 26). Since February 22, well before the Government declared the state of emergency, a huge reduction of emergency surgeries was seen in Lombardy Hospitals, with a generalized drop of attendances in the Emergency Departments (EDs).Study Objective: The aim of this study is to report the experience of the ED of a third-level hospital in downtown Milan, Lombardy (IRCCS Foundation Cà Granda Ospedale Maggiore Policlinico), and try to explain the causes of the observed phenomena.Methods: A retrospective, observational study was performed assessing the volume of emergency surgeries and of the attendances in the ED during the course of the pandemic, i.e. immediately before, during and after progressive community lockdown in response to the COVID-19 pandemic, comparing the same time periods in 2019.Results: Compared to the previous year, in 2020 a significant overall drop of emergency surgeries (60%, p<0.002) and of ED attendances (66%, p≅0) was observed. The drop was significant for medical ( 40%), surgical (74%), specialist fast track (92%), and psychiatric (60%) complaints, for domestic violence accesses (59%) and for patients who left the ED without being seen (LWBS) (76%). Conversely, deaths raised by 196%.Conclusion: During the COVID-19 outbreak the volume of urgent surgeries and the volume of patients accessing ED dropped. At the moment, it is not known if mortality of people who did not seek care increased during the pandemic. Further studies are needed to try to understand if such reductions during the COVID-19 pandemic will result in a rebound of patients left untreated, or in unwanted consequences on population health.
Assuntos
COVID-19 , Viroses , Transtornos MentaisRESUMO
Background. Respiratory failure is a key feature of severe Covid-19 and a critical driver of mortality, but for reasons poorly defined affects less than 10% of SARS-CoV-2 infected patients. Methods. We included 1,980 patients with Covid-19 respiratory failure at seven centers in the Italian and Spanish epicenters of the SARS-CoV-2 pandemic in Europe (Milan, Monza, Madrid, San Sebastian and Barcelona) for a genome-wide association analysis. After quality control and exclusion of population outliers, 835 patients and 1,255 population-derived controls from Italy, and 775 patients and 950 controls from Spain were included in the final analysis. In total we analyzed 8,582,968 single-nucleotide polymorphisms (SNPs) and conducted a meta-analysis of both case-control panels. Results. We detected cross-replicating associations with rs11385942 at chromosome 3p21.31 and rs657152 at 9q34, which were genome-wide significant (P<5x10-8) in the meta-analysis of both study panels, odds ratio [OR], 1.77; 95% confidence interval [CI], 1.48 to 2.11; P=1.14x10-10 and OR 1.32 (95% CI, 1.20 to 1.47; P=4.95x10-8), respectively. Among six genes at 3p21.31, SLC6A20 encodes a known interaction partner with angiotensin converting enzyme 2 (ACE2). The association signal at 9q34 was located at the ABO blood group locus and a blood-group-specific analysis showed higher risk for A-positive individuals (OR=1.45, 95% CI, 1.20 to 1.75, P=1.48x10-4) and a protective effect for blood group O (OR=0.65, 95% CI, 0.53 to 0.79, P=1.06x10-5). Conclusions. We herein report the first robust genetic susceptibility loci for the development of respiratory failure in Covid-19. Identified variants may help guide targeted exploration of severe Covid-19 pathophysiology.
Assuntos
Síndrome Respiratória Aguda Grave , COVID-19 , Insuficiência RespiratóriaRESUMO
Background Severe acute respiratory syndrome coronavirus 2 is a recently discovered pathogen responsible of coronavirus disease 2019 (COVID-19). The immunological changes associated with this infection are largely unknown. Methods We evaluated the peripheral blood mononuclear cells profile of 63 patients with COVID-19 at diagnosis and the presence of association with inflammatory biomarkers and 28-days mortality. Results Lymphocytopenia was present in 51 of 63 (80.9%) patients. This reduction was mirrored also on CD8+ lymphocytes (128 cells/uL), natural killer cells (67 cells/uL) and natural killer T cells (31 cells/uL). Monocytes were preserved in total number but displayed a subpopulation composed mainly of cells with a reduced expression of both CD14 and HLA-DR. A direct correlation was found between serum values of IL-6 and the frequency of Th2 lymphocytes (R=0.17; p=0.04) but not with the monocytes count (R=0.01; p=0.60). Patients who died in the 28 days from admission (N=10, 15.9%), when compared to those who did not, displayed lower mean values of CD3+ (p=0.028) and CD4+ cells (p=0.042) and higher mean percentages of CD8+/CD38+/HLA-DR+ lymphocytes (p=0.026). Conclusions The early phases of COVID-19 are characterized by lymphocytopenia, predominance of Th2 lymphocytes and less immunocompetent monocytes, which include atypical mononuclear cells.