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Objective: To evaluate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) anti-spike (S) IgG antibody production after vaccination with BNT162b2 and the protection from symptomatic breakthrough infections in health care workers. Methods: This prospective observational study (RENAISSANCE) had as a primary end point the evaluation of serologic response to BNT162b2 14 days after a second dose. SARS-CoV-2 anti-S IgG antibodies were evaluated with LIAISON SARS-CoV-2 TrimericS IgG assay (DiaSorin S.p.A.), which is able to detect the presence of both binding and neutralizing antibodies for trimeric spike glycoprotein. Participants were recruited from February 1, 2021, to February 22, 2021. Occurrence of vaccine breakthrough infections was assessed by reverse transcription–polymerase chain reaction on symptomatic and contact cases up to June 6, 2021. Results: Of 2569 staff evaluated, only 4 were nonresponders (0.16%; 95% CI, 0.04% to 0.41%). All 4 nonresponders were severely immunosuppressed and receiving treatment with mycophenolate mofetil or mycophenolic acid. At 14 days after the second dose, 67.5% (1733) of staff had anti-S IgG titers of 2000 BAU/mL or higher; 19.2% (494), between 1500 and 2000 BAU/mL; 9.8% (251), between 1000 and 1500 BAU/mL; and 3.4% (87), 1000 BAU/mL or lower. Women had a higher probability of having higher titers than men (64.5% [1044/1618] vs 58.3% [410/703]; P=.005). This was confirmed after adjustment for age group (odds ratio, 1.275; 95% CI, 1.062 to 1.531; P=.009). Four months after the end of the vaccination program, only 13 participants (0.26%) had experienced a breakthrough SARS-CoV-2 infection, including 1 nonresponder. This was the only participant requiring hospitalization for severe COVID-19. Conclusion: The vaccination campaign among health care workers at the ASST GOM Niguarda has resulted in a marked serologic response and reduction of incident COVID-19 cases. Yet, the lack of protection should not be overlooked in immunocompromised individuals.

SARS‐CoV2 infection is a systemic disease that may involve multiple organs, including the central nervous system (CNS). Aims of our study are to describe prevalence and clinical features of neurological manifestations, mortality and hospital discharge in subjects hospitalized with COVID‐19. All individuals admitted for to our hospital COVID‐19 were retrospectively included. Patients were classified according to the symptoms at hospital entry in (1) isolated respiratory, (2) combined respiratory and neurologic, (3) isolated neurologic and (4) stroke manifestations. Descriptive statistics and nonparametric tests to compare the groups were calculated. Kaplan Meier probability curves and multivariable Cox regression models for survival and hospital discharge were applied. The analysis included 901 patients: 42.6% showed a severe or critical disease with an overall mortality of 21.2%. At least one neurological symptom or disease was observed in 30.2% of subjects ranging from dysgeusia/anosmia (9.1%) to postinfective diseases (0.8%). Patients with respiratory symptoms experienced a more severe disease and a higher in‐hospital mortality compared to those who showed only neurologic symptoms. Kaplan Meier estimates displayed a statistically significant different survival among groups (p = 0.003): subjects with stroke had the worst. After adjusting for risk factors such as age, sex and comorbidity, individuals with isolated neurologic manifestations exhibited a better survival (aHR 0.398, 95% CI [0.206, 0.769], p = 0.006). Neurologic manifestations in COVID‐19 are common but heterogeneous and mortality in subjects with isolated neurologic manifestations seems lower than in those with respiratory symptoms., At least one neurologic symptom was observed in 30.2% of subjects. Neurologic manifestations in COVID‐19 are common but heterogeneous. Patients with neurologic symptoms exhibited a less severe pulmonary disease and a lower in‐hospital mortality (aHR 0.398, 95% CI 0.206‐0.769, p = 0.006) compared to those showing respiratory symptoms.

Background The COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes. Objective This study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization. Methods A predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed. Results The predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept –0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients. Conclusions A simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

BACKGROUND The COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes. OBJECTIVE This study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization. METHODS A predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed. RESULTS The predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept –0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients. CONCLUSIONS A simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

Background and aims To validate a set of indicators for monitoring the quality of care of patients with diabetes in ‘real-life’ practice through its relationship with measurable clinical outcomes and healthcare costs. Methods and results A population-based cohort study was carried out by including the 20,635 patients, residents in the Lombardy Region (Italy), who in the year 2012 were newly taken-in-care for diabetes. Adherence with clinical recommendations (i.e., controls for glycated haemoglobin, lipid profile, urine albumin excretion and serum creatinine) was recorded during the first year after the patient was taken-in-care, and categorized according whether he/she complied with none or almost none (0 or 1), just some (2) or all or almost all (3 or 4) the recommendations, respectively denoted as poor, intermediate and high adherence. Short- and long-term complications of diabetes, and healthcare cost incurred by the National Health Service, were assessed during follow-up. Compared with patients with poor adherence, those with intermediate and high adherence respectively showed (i) a delay in outcome occurrence of 13 days (95% CI, -2 to 27) and 23 days (9 to 38), and (ii) a lower healthcare cost of 54 € and 77 €. In average, a gain of 18 Euros and 15 Euros for each day free from diabetic complication by increasing adherence respectively from poor to intermediate and from poor to high were observed. Conclusion Close control of patients with diabetes through regular clinical examinations must be considered the cornerstone of national guidance, national audits, and quality improvement incentive schemes.

Objective: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our maingoal was assessing the accuracy of artificial intelligence in forecasting the resultsof RT-PCR for SARS-COV-2, using basic information at hand in all emergencydepartments.Methods: This is a retrospective study carried out between February 22 and March 16 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2.Patients under 12 years old, with no leukocyte formula performed in the ED,were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission.Results: Among 199 patients subject to study (median [interquartile range] age 65 [46-78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity.Conclusion: Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed,on a larger-scale study, this approach could have important clinical and organizational implications.

Background Multimorbidity is a growing concern for healthcare systems, with many countries experiencing demographic transition to older population profiles. A simple multisource comorbidity score (MCS) has been recently developed and validated. A very large real-world investigation was conducted with the aim of measuring inequalities in the MCS distribution across Italy. Methods Beneficiaries of the Italian National Health Service aged 50–85 years who in 2018 were resident in one of the 10 participant regions formed the study population (15.7 million of the 24.9 million overall resident in Italy). MCS was assigned to each beneficiary by categorizing the individual sum of the comorbid values (i.e. the weights corresponding to the comorbid conditions of which the individual suffered) into one of the six categories denoting a progressive worsening comorbidity status. MCS distributions in women and men across geographic partitions were compared. Results Compared with beneficiaries from northern Italy, those from centre and south showed worse comorbidity profile for both women and men. MCS median age (i.e. the age above which half of the beneficiaries suffered at least one comorbidity) ranged from 60 (centre and south) to 68 years (north) in women and from 63 (centre and south) to 68 years (north) in men. The percentage of comorbid population was lower than 50% for northern population, whereas it was around 60% for central and southern ones. Conclusion MCS allowed of capturing geographic variability of multimorbidity prevalence, thus showing up its value for addressing health policy in order to guide national health planning.

Background: SARS-CoV2 infection is a systemic disease that may involve multiple organs, including the nervous system. Aims of our study are to describe prevalence and clinical features of neurological manifestations, mortality and hospital discharge in subjects hospitalized with COVID-19. Methods: All individuals admitted for COVID-19 were retrospectively included. Patients were classified according to the symptoms at hospital entry in 1) isolated respiratory, 2) combined respiratory and neurologic, 3) isolated neurologic and 4) stroke manifestations. Descriptive statistics and non-parametric tests to compare the groups were applied. Kaplan Meier probability curves and multivariable Cox regression models for survival and hospital discharge were applied. Results: The analysis included 901 patients, 42.6% showed a severe or critical disease with an overall mortality of 21.2%. At least one neurological symptom or disease was observed in 30.2% of subjects ranging from dysgeusia/anosmia (9.1%) to post-infective diseases (0.8%). Patients with respiratory symptoms experienced a more severe disease and a higher in-hospital mortality compared to those who showed only neurologic symptoms. Kaplan Meier estimates displayed a statistically significant different survival among groups (p=0.003): subjects with stroke had the worst. After adjusting for risk factors such as age, sex and comorbidity, individuals with isolated neurologic manifestations exhibited a better survival (aHR 0.398, 95% CI 0.206-0.769, p=0.006). Discussion: Neurologic manifestations in COVID-19 are common but heterogeneous. Subjects with isolated neurologic manifestations, experienced a lower mortality than those with respiratory symptoms, suggesting that neurologic disease may have a different course than when the virus involves respiratory system. Funding Statement: None. Declaration of Interests: The authors declare that they have no conflicts of interest for this work. Ethics Approval Statement: The local Ethics Committee approved the protocol under the special conditions indicated by the Italian 648/96 law. All subjects provided written informed consent.

A. Fassina, M. Corradin, D. Zardo, R. Cappellesso, F. Corbetti and M. Fassan Role and accuracy of rapid on-site evaluation of CT-guided fine needle aspiration cytology of lung nodules Objective: To prospectively investigate the role of trans-thoracic fine needle aspiration cytology (FNA) and the value of rapid on-site evaluation (ROSE) in the clinical management of patients with pulmonary nodules/masses. Computed tomography (CT)-guided FNA is commonly employed for the diagnosis of lung lesions although its position in the diagnostic work-up is still a matter of debate. Methods: We reviewed 311 patients (211 males and 100 females, mean age 69.5 years) admitted to the University of Padova from 2004 to 2008, correlating the results of cytology with the available histological findings obtained from biopsies, surgery or autopsy. Results: Smears were adequate in 305 cases (98%) and inadequate in six (2%); a diagnosis of malignancy was achieved in 263 cases (86.2%); 39 cases (12.8%) were classified as non-malignant; and three cases (1%) were classified as suspect for malignancy. When correlated with histology, FNA with ROSE discriminated malignant versus non-malignant lesions (Cohen’s kappa 0.78), with three false negatives (sensitivity 96.3%, specificity 100%). Moreover, a satisfactory overall agreement of 71.4% was achieved in differentiating the cancer histological types. Pneumothorax occurred in 13 cases, haemoptysis in four, and chest pain in three. A single aspiration was sufficient in 79.6% of patients; two aspirations were needed in 17.4% and three in 3%. The low complication rate was related to the limited number of aspirations needed due to ROSE. Conclusions: FNA with ROSE is a safe and useful tool in the diagnostic work-up of lung cancer patients, with no contraindications to its use as the first diagnostic procedure for all patients with peripheral lung lesions. FNA with ROSE should be reconsidered in the guidelines for diagnosing and managing lung cancer.

The increasing number of Malignant Mesothelioma (MM) cases that arrive for expert examinations to court for compensation reasons in subjects exposed to asbestos, in many instances rely exclusively on cytological smears of pleural effusion. We evaluated the accuracy and reproducibility of cytological pleural effusions, based on morphological criteria alone. Nine pathologists and eight residents from seven institutions in north-east Italy blindly examined 45 smears of MM (17), metastases (14) and benign effusions (14), in two rounds. Diagnoses had been confirmed by immunohistochemical and clinical follow-up, and eventually at autopsy. Diagnostic accuracy, interobserver and intraobserver agreement in the distinction of benign vs malignant cases, and in the differentiation of primary from metastatic malignancies, were evaluated. The distinction of benign from malignant smears resulted rather satisfactory ( k =0.514), but markedly decreased in differentiation of MM from metastases (overall agreement: k =0.343), as well as when readings from residents were analyzed ( k =0.132). Cytology is a useful and reliable tool in the identification of malignancies, but when the distinction of primary from metastatic tumors is addressed morphological criteria alone are not sufficient for a definite diagnosis of MM and the use of cell blocks, immunohistochemistry (IHC) and molecular ancillary techniques are recommended.

Markers of skin wound vitality and the research methodology used for their determination are still matters of debate in forensic pathology. Cathepsin-D, a lysosomal enzyme, is the most expressed cathepsin in human skin, and although it seems to have the necessary requirements to be utilized as a vitality marker, past research has provided no definitive and clear response on its potential usefulness. Immunohistochemistry with monoclonal antibodies and image analysis has been employed to detect and quantify the expression of Cathepsin-D in human skin wounds. We analyzed skin fragments obtained from 20 living individuals (group A) and 20 persons deceased from natural causes (group B). For each case, five skin fragments were withdrawn at 0′, 5′, 10′, 30′, and 90′ after abdominal incision. Once the samples were formalin-fixed and paraffin-embedded, we analyzed the expression of Cathepsin-D through the quantification of the immunohistochemistry signal by image analysis. Immunoreactivity was displayed in Pixels of positive area measured by image analysis and converted in micrometer squares. The average levels of Cathepsin-D were higher in group B than in group A, except in three cases which showed a lower expression, with a statistically significant difference of Cathepsin-D expression between the two groups ( p

For pathologists, pneumologists, and occupational and environmental physicians it is relevant to know silica levels in lung tissue to better define limits of exposure. Environmental Scanning Electron Microscopy (ESEM) has been employed to detect silica particles and to compare silica levels in subjects with and without Lung Cancer (LC). We investigated 25 paraffin-embedded tissue samples of patients with LC adenocarcinoma, and 20 fresh samples of subjects without LC deceased for extra-pulmonary diseases. Silica levels were quantified considering the Number of Spots of silica particles (NS), and the Number of Positive Zones (NPZ) in which there was at least one spot. Levels of NS and NPZ were assessed with Poisson-type regression models, and in two samples of silica-exposed workers with LC the performance of models were evaluated. LC patients displayed higher silica levels, as compared to controls; smoking, age and gender had no significant effects on this relationship. Values of NS and NPZ for the exposed workers were in agreement with model estimates. The fitted model between NS and NPZ might be useful in evaluating new observations and in the development of threshold limit values of silica in biological tissues. ESEM is a rapid, simple and valid tool for the determination of silica levels in lung tissues.