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.

The object of this study was to evaluate the sensitivity and positive predictive value (PPV) of International Classification of Diseases, 9th revision (ICD-9) codes 430-438 in the Sistema Informativo Sanitario Regionale (SISR), an Italian health care automated database. We compared the SISR with a manual search of all cases of transient ischaemic attack (TIA) and stroke discharged from the Novara Hospital, NW Italy. Results were as follows: SISR list: 1017 patients; manual list 1005. Linked: 896; false negatives: 109; false positives: 121. Sensitivity of codes 430-438: 77% at the primary position only and 89% at either the primary or secondary position; PPV: 93% and 88%. Sensitivity and PPV for specific codes vs. each subcategory (sensitivity at the primary position only/any position; PPV at the primary position only/any position): for 430, subarachnoid haemorrhage (33/35%; 46/43%); for 431, cerebral haemorrhage (57/59%; 77/75%); for 434, cerebral infarction (35/37%; 90/87%); for 436, stroke of unknown type (29/29%; 19/16%); and for 435, TIA (75/82%; 80/78%). The SISR database has a high PPV; sensitivity is high for TIA, but low for specific stroke ICD codes.

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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.