4C mortality score and COVID-19 mortality risk score: an analysis in four different age groups of an Italian population.

To evaluate the prognostic stratification ability of 4C Mortality Score and COVID-19 Mortality Risk Score in different age groups. Retrospective study, including all patients, presented to the Emergency Department of the University Hospital Careggi, between February, 2020 and May, 2021, and admitted for SARS-CoV2. Patients were divided into four subgroups based on the quartiles of age distribution: patients < 57 years (G1, n = 546), 57-71 years (G2, n = 508), 72-81 years (G3, n = 552), and > 82 years (G4, n = 578). We calculated the 4C Mortality Score and COVID-19 Mortality Risk Score. The end-point was in-hospital mortality. In the whole population (age 68 ± 16 years), the mortality rate was 19% (n = 424), and increased with increasing age (G1: 4%, G2: 11%, G3: 22%, and G4: 39%, p < 0.001). Both scores were higher among non-survivors than survivors in all subgroups (4C-MS, G1: 6 [3-7] vs 3 [2-5]; G2: 10 [7-11] vs 7 [5-8]; G3: 11 [10-14] vs 10 [8-11]; G4: 13 [12-15] vs 11 [10-13], all p < 0.001; COVID-19 MRS, G1: 8 [7-9] vs 9 [9-11], G2: 10 [8-11] vs 11 [10-12]; G3: 11 [10-12] vs 12 [11-13]; G4: 11 [10-13] vs 13 [12-14], all p < 0.01). The ability of both scores to identify patients at higher risk of in-hospital mortality, was similar in different age groups (4C-MS: G1 0.77, G2 0.76, G3 0.68, G4 0.72; COVID-19 MRS: G1 0.67, G2 0.69, G3 0.69, G4 0.72, all p for comparisons between subgroups = NS). Both scores confirmed their good performance in predicting in-hospital mortality in all age groups, despite their different mortality rate.

Clinical characteristics of patients hospitalized for COVID-19: comparison between different age groups.

BACKGROUND: To test whether known prognosticators of COVID-19 maintained their stratification ability across age groups. METHODS: We performed a retrospective study. We included all patients (n = 2225), who presented to the Emergency Department of the Careggi University Hospital for COVID-19 in the period February 2020-May 2021, and were admitted to the hospital. The following parameters were analyzed as dichotomized: 1) SpO2/FiO2 ≤ or > 214; 2) creatinine < or ≥ 1.1 mg/dL; 3) Lactic dehydrogenase (LDH) < or ≥ 250 U/mL; 4) C Reactive Protein (CRP) < or ≥ 60 mg/100 mL. We divided the study population in four subgroups, based on the quartiles of distribution of age (G1 18-57 years, G2 57-71 years, G3 72-81 years, G4 > 82). The primary end-point was in-hospital mortality. RESULTS: By the univariate analysis, the aforementioned dichotomized variables demonstrated a significant association with in-hospital mortality in all subgroups. We introduced them in a multivariate model: in G1 SpO2/FiO2 ≤ 214 (Relative Risk, RR 15.66; 95%CI 3.98-61,74), in G2 creatinine ≥ 1.1 mg/L (RR 2.87, 95%CI 1.30-6.32) and LDH ≥ 250 UI/L (RR 8.71, 95%CI 1,15-65,70), in G3 creatinine ≥ 1.1 mg/L (RR 1.98, 95%CI 1,17-3.36) and CRP ≥ 60 ng/L (RR 2.14, 95%CI 1.23-3.71), in G4 SpO2/FiO2 ≤ 214 (RR 5.15, 95%CI 2.35-11.29), creatinine ≥ 1.1 mg/L (RR 1.75, 95%CI 1.09-2.80) and CRP ≥ 60 ng/L (RR 1.82, 95%CI 1.11-2.98) were independently associated with an increased in-hospital mortality. CONCLUSIONS: A mild to moderate respiratory failure showed an independent association with an increased mortality rate only in youngest and oldest patients, while kidney disease maintained a prognostic role regardless of age.

COVID-19: A complex disease with a unique metabolic signature.

Plasma of COVID-19 patients contains a strong metabolomic/lipoproteomic signature, revealed by the NMR analysis of a cohort of >500 patients sampled during various waves of COVID-19 infection, corresponding to the spread of different variants, and having different vaccination status. This composite signature highlights common traits of the SARS-CoV-2 infection. The most dysregulated molecules display concentration trends that scale with disease severity and might serve as prognostic markers for fatal events. Metabolomics evidence is then used as input data for a sex-specific multi-organ metabolic model. This reconstruction provides a comprehensive view of the impact of COVID-19 on the entire human metabolism. The human (male and female) metabolic network is strongly impacted by the disease to an extent dictated by its severity. A marked metabolic reprogramming at the level of many organs indicates an increase in the generic energetic demand of the organism following infection. Sex-specific modulation of immune response is also suggested.

Delirium and IL-6 added to clinical scores improves their performance: a prospective analysis of CALL, PREDI-CO, MRS score applied to a population of patients admitted to internal medicine ward.

This study aimed to evaluate the effectiveness of various scoring systems in predicting in-hospital mortality for COVID-19 patients admitted to the internal medicine ward. We conducted a prospective collection of clinical data from patients admitted to the Internal Medicine Unit at Santa Maria Nuova Hospital in Florence, Italy, with confirmed pneumonia caused by SARS-CoV-2. We calculated three scoring systems: the CALL score, the PREDI-CO score, and the COVID-19 in-hospital Mortality Risk Score (COVID-19 MRS). The primary endpoint was in-hospital mortality. : A total of 681 patients were enrolled in the study, with a mean age of 68.8 ± 16.1 years, and 54.8% of them were male. Non-survivors had significantly higher scores in all prognostic systems compared to survivors (MRS: 13 [12- 15] vs. 10 [8-12]; CALL: 12 [10-12] vs. 9 [7-11]; PREDI-CO: 4 [3-6] vs. 2 [1-4]; all p<0.001). The receiver operating characteristic (ROC) analysis yielded the following area under the curve (AUC) values: MRS 0.85, CALL 0.78, PREDI-CO 0.77. The addition of Delirium and IL6 to the scoring systems improved their discriminative ability, resulting in AUC values of 0.92 for MRS, 0.87 for CALL, and 0.84 for PREDI-CO. The mortality rate increased significantly across increasing quartiles (p<0.001). In conclusion the COVID-19 in-hospital Mortality Risk Score (MRS) demonstrated reasonable prognostic stratification for patients admitted to the internal medicine ward with SARS-CoV-2-induced pneumonia. The inclusion of Delirium and IL6 as additional prognostic indicators in the scoring systems enhanced their predictive performance, specifically in determining in-hospital mortality among COVID-19 patients.

Profiling metabolites and lipoproteins in COMETA, an Italian cohort of COVID-19 patients.

Metabolomics and lipidomics have been used in several studies to define the biochemical alterations induced by COVID-19 in comparison with healthy controls. Those studies highlighted the presence of a strong signature, attributable to both metabolites and lipoproteins/lipids. Here, 1H NMR spectra were acquired on EDTA-plasma from three groups of subjects: i) hospitalized COVID-19 positive patients (≤21 days from the first positive nasopharyngeal swab); ii) hospitalized COVID-19 positive patients (>21 days from the first positive nasopharyngeal swab); iii) subjects after 2-6 months from SARS-CoV-2 eradication. A Random Forest model built using the EDTA-plasma spectra of COVID-19 patients ≤21 days and Post COVID-19 subjects, provided a high discrimination accuracy (93.6%), indicating both the presence of a strong fingerprint of the acute infection and the substantial metabolic healing of Post COVID-19 subjects. The differences originate from significant alterations in the concentrations of 16 metabolites and 74 lipoprotein components. The model was then used to predict the spectra of COVID-19>21 days subjects. In this group, the metabolite levels are closer to those of the Post COVID-19 subjects than to those of the COVID-19≤21 days; the opposite occurs for the lipoproteins. Within the acute phase patients, characteristic trends in metabolite levels are observed as a function of the disease severity. The metabolites found altered in COVID-19≤21 days patients with respect to Post COVID-19 individuals overlap with acute infection biomarkers identified previously in comparison with healthy subjects. Along the trajectory towards healing, the metabolome reverts back to the "healthy" state faster than the lipoproteome.

Point-of-care ultrasound (PoCUS) in the early diagnosis of novel coronavirus 2019 disease (COVID-19) in a first-level emergency department during a SARS-CoV-2 outbreak in Italy: a real-life analysis.

In December 2019, the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) spread worldwide, challenging emergency departments (EDs) with the need of rapid diagnosis for appropriate allocation in dedicated setting. Many authors highlighted the role of lung ultrasound (LUS) in management of the novel coronavirus disease 2019 (COVID-19). The study aims to analyze the performance of LUS in the early identification of COVID-19 patients in ED during a SARS-CoV-2 outbreak. We prospectively collected consecutive adult patients admitted to a first-level ED in Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation Florence with history or symptoms suggestive for COVID-19 that underwent LUS during the ED management. LUS findings were categorized in 6 discrete main etiological patterns. "A", "Cardiogenic B" and "Typical C" patterns were referred as non-COVID-19-suggestive, while "Atypical" B or C patterns, "Multiple Consolidations" pattern and "ARDS" pattern were referred as COVID-19-suggestive. The primary outcome was the diagnosis of SARS-CoV-2 infection. From 12 March to 12 May 2020, 360 patients were enrolled. COVID-19 suggestive LUS findings were significantly associated with final COVID-19 diagnosis (86% in COVID-19 vs 29% in non-COVID-19, p < 0.001). The presence in ED of at least one in positive swab OR a COVID-19-suggestive LUS showed a sensitivity of 97% and a negative predictive value (NPV) of 98%. In patients with known SARS-CoV-2 exposition in the last 14 days, a COVID-19-suggestive pattern at LUS had a positive predictive value (PPV) of 97% for COVID-19 diagnosis. Point-of-care ultrasound (PoCUS) is a valuable tool for diagnostic stratification during COVID-19 outbreaks. LUS can help physicians in identifying false-negative RT-PCR, improving its diagnostic sensitivity in ED.

Clinical features, prognostic factors and outcome in a series of 29 extra-skeletal Ewing Sarcoma. Adequate margins and surgery-radiotherapy association improve overall survival.

OBJECTIVE: Authors review a series of 29 extra-skeletal Ewing Sarcoma (EES). METHODS: They analyzed characteristics, prognostic factors and outcome of EES. RESULTS: Authors report 60% Overall Survival (OS) and 56% of Event Free Survival (EFS) at 5 years. Better 5 years EFS was found in patients with localized disease (68.8%) compared to metastatic EES (33.3%) (p = 0.042). Radiotherapy + surgery offered the best local treatment (p=0.017). Volume (p = 0.032), Surgical margins (p = 0.01), metastatic disease (p = 0.0013) were a significant prognostic factor for OS at 5-yrs. CONCLUSION: Adequate margins and surgery+radiotherapy improve Overall Survival.