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1.
Dis Markers ; 2021: 8863053, 2021.
Article in English | MEDLINE | ID: mdl-34055104

ABSTRACT

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.


Subject(s)
Blood Cell Count , COVID-19/blood , COVID-19/mortality , Clinical Decision Rules , Hospital Mortality , Severity of Illness Index , Adult , Aged , Aged, 80 and over , COVID-19/diagnosis , Female , Humans , Italy/epidemiology , Male , Middle Aged , Multivariate Analysis , Prognosis , Retrospective Studies
2.
Sci Rep ; 10(1): 20731, 2020 11 26.
Article in English | MEDLINE | ID: mdl-33244144

ABSTRACT

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.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Pandemics , SARS-CoV-2/genetics , Age Factors , Aged , Aged, 80 and over , COVID-19/virology , Comorbidity , Female , Humans , Italy/epidemiology , Length of Stay , Male , Middle Aged , Retrospective Studies , Reverse Transcriptase Polymerase Chain Reaction , Risk Factors , Sex Factors , Smoking , Survival Rate
4.
J Clin Med ; 9(1)2020 Jan 19.
Article in English | MEDLINE | ID: mdl-31963812

ABSTRACT

BACKGROUND: Acute myeloid leukemia is a heterogeneous hematological disease, characterized by karyotypic and molecular alterations. Mutations in IDH2 have a role in diagnosis and as a minimal residue disease marker. Often the variant allele frequency during follow up is less than 20%, which represents the limit of detection of Sanger sequencing. Therefore, the development of sensitive methodologies to identify IDH2 mutations might help to monitor patients' response to therapy. We compared three different methods to identify and monitor IDH2 mutations in patients' specimens. METHODS: Performances of PNA-PCR clamping, droplet digital PCR and Sanger for IDH2 status identification were evaluated and compared in 96 DNA patients' specimens. RESULTS: In contrast with Sanger sequencing, our results highlighted the concordance between PNA clamping and digital PCR. Furthermore, PNA-PCR clamping was able to detect more mutated DNA with respect to Sanger sequencing that showed several false negatives independently from the allelic frequency. CONCLUSIONS: We found that PNA-PCR clamping and digital PCR identified IDH2 mutations in DNA samples with comparable results in a percentage significantly higher compared to Sanger sequencing. PNA-PCR clamping can be used even in laboratories not equipped for sophisticated analyses, decreasing cost and time for IDH2 characterization.

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