Impaired Visual Inhibition in Amnestic Mild Cognitive Impairment.

Objective.The pathophysiology of amnestic mild cognitive impairment (aMCI) and Alzheimer disease (AD) is still a matter of debate. Visual system might be precociously altered, especially for its cholinergic connections. We thus studied patients with aMCI compared to AD with paired-pulse flash-visual evoked potentials (paired-F-VEPs), a putative marker of cholinergic function. Methods. We enrolled 12 adult patients with aMCI and 12 with AD. 14 normal age- and sex-matched subjects acted as controls (HS). Stimuli were single flashes, with interspersed random flash pairs at critical interstimulus intervals (ISIs, 16.5 to 125 ms) with closed eyes. The "single" (unconditioned) F-VEP was split into a "main complex" (50 to 200 ms after the flash) and a "late response" (200 to 400 ms). As for paired stimulation, the "test" F-VEP emerged from electronic subtraction of the "single" F-VEP from the "paired"-F-VEP. Results. In the single F-VEP, P2 latency was prolonged in patients (aMCI and AD) compared to HS (p < .05). As to the paired F-VEPs, in aMCI the "late response" normal inhibition was abolished at ISIs 50-62.5 ms (p ≤ .016), compared to AD and controls. No changes were detected for the "main complex". Conclusions. Paired-F-VEPs demonstrate a defective neural inhibition in the visual system of patients with aMCI at critical intervals. It may represent a compensatory mechanism against neuronal loss, the failure of which may be involved in AD development. Paired-F-VEPs may warrant inclusion in future preclinical/clinical studies, to evaluate its potential role in the pathophysiology and management of aMCI.

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.