Increased Levels of ICOS and ICOSL Are Associated to Pulmonary Arterial Hypertension in Patients Affected by Connective Tissue Diseases.

BACKGROUND: Pulmonary hypertension (PH) is a life-threatening complication of connective tissue diseases (CTD); in this study, we aimed at investigating the potential role of inducible co-stimulator (ICOS) and its ligand (ICOS-L) as biomarkers of PH in CTD. MATERIALS AND METHODS: We recruited 109 patients: 84 CTD patients, 13 patients with CTD complicated by pulmonary arterial hypertension (PAH), and 12 subjects with PAH alone. All recruited patients underwent a complete clinical and instrumental assessment along with quantitative measurement of serum ICOS and ICOS-L. RESULTS: Independently of the underlying cause, patients with PAH were older and had a lower glomerular filtration rate. Interestingly, patients with both CTD-related and CTD-unrelated PAH had higher ICOS and ICOS-L serum concentrations than CTD patients (0.0001 for both). When compared to CTD patients, those affected by CTD-PAH showed higher ICOS (440 (240-600) vs. 170 (105-275) pg/mL, p = 0.0001) and ICOS-L serum concentrations (6000 (4300-7000) vs. 2450 (1500-4100) pg/mL; p = 0.0001). In a logistic regression, ICOS and ICOS-L were associated with a diagnosis of PAH, independently from age, gender, and renal function. The corresponding receiver operating characteristic (ROC) curves demonstrated a good diagnostic performance for both ICOS and ICOS-L. CONCLUSIONS: ICOS and ICOS-L are increased in patients with PAH, irrespectively from the underlying cause, and represent promising candidate biomarkers for the diagnostic screening for PAH among CTDs patients.

Long-term sequelae are highly prevalent one year after hospitalization for severe COVID-19.

Many coronavirus disease 2019 (Covid-19) survivors show symptoms months after acute illness. The aim of this work is to describe the clinical evolution of Covid-19, one year after discharge. We performed a prospective cohort study on 238 patients previously hospitalized for Covid-19 pneumonia in 2020 who already underwent clinical follow-up 4 months post-Covid-19. 200 consented to participate to a 12-months clinical assessment, including: pulmonary function tests with diffusing lung capacity for carbon monoxide (DLCO); post-traumatic stress (PTS) symptoms evaluation by the Impact of Event Scale (IES); motor function evaluation (by Short Physical Performance Battery and 2 min walking test); chest Computed Tomography (CT). After 366 [363-369] days, 79 patients (39.5%) reported at least one symptom. A DLCO < 80% was observed in 96 patients (49.0%). Severe DLCO impairment (< 60%) was reported in 20 patients (10.2%), related to extent of CT scan abnormalities. Some degree of motor impairment was observed in 25.8% of subjects. 37/200 patients (18.5%) showed moderate-to-severe PTS symptoms. In the time elapsed from 4 to 12 months after hospital discharge, motor function improves, while respiratory function does not, being accompanied by evidence of lung structural damage. Symptoms remain highly prevalent one year after acute illness.

Simple Parameters from Complete Blood Count Predict In-Hospital Mortality in COVID-19.

INTRODUCTION: The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions. MATERIALS AND METHODS: In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients (F 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded. RESULTS: At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) (χ 2 10.4; p < 0.001), neutrophil-to-lymphocyte (NL) ratio (χ 2 7.6; p = 0.006), and platelet count (χ 2 5.39; p = 0.02), along with age (χ 2 87.6; p < 0.001) and gender (χ 2 17.3; p < 0.001), accurately predicted in-hospital mortality. Hemoglobin levels were not associated with mortality. We also identified the best cut-off for mortality prediction: a NL ratio > 4.68 was characterized by an odds ratio for in-hospital mortality (OR) = 3.40 (2.40-4.82), while the OR for a RDW > 13.7% was 4.09 (2.87-5.83); a platelet count > 166,000/µL was, conversely, protective (OR: 0.45 (0.32-0.63)). CONCLUSION: Our findings arise the opportunity of stratifying COVID-19 severity according to simple lab parameters, which may drive clinical decisions about monitoring and treatment.

Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients.

Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk.