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1.
Sci Rep ; 11(1): 21136, 2021 10 27.
Article in English | MEDLINE | ID: covidwho-1493228

ABSTRACT

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.


Subject(s)
COVID-19/mortality , Machine Learning , Pandemics , SARS-CoV-2 , Aged , Aged, 80 and over , Blood Cell Count , Blood Chemical Analysis , COVID-19/blood , Cohort Studies , Female , Hospital Mortality , Humans , Male , Middle Aged , Models, Statistical , Multivariate Analysis , Oxygen/blood , Pandemics/statistics & numerical data , ROC Curve , Risk Factors , Rome/epidemiology
2.
Int J Environ Res Public Health ; 18(21)2021 Oct 22.
Article in English | MEDLINE | ID: covidwho-1480754

ABSTRACT

Health workers, especially those in patient-facing roles, had a significantly increased risk of COVID-19 infection, having serious outcomes, and risking spreading the virus to patients and staff. Vaccination campaign planning suggests allocating initial supplies of BNT162b2 vaccine to health workers given the importance of early protection to safeguard the continuity of care to patients. The aim of the study is to assess the effectiveness and safety of BNT162b2 vaccine among the health workers of Fondazione Policlinico Universitario Agostino Gemelli IRCCS (FPG). The retrospective cohort study was conducted among health staff working at the FPG. Vaccination data were collected from hospital records. The primary end points were vaccine effectiveness and safety. A total of 6649 health workers were included, of whom 5162 received injections. There were 14 cases of COVID-19 with onset at least 14 days after the second dose among vaccinated health workers and 45 cases among unvaccinated ones. BNT162b2 was 91.5% effective against COVID-19 (95% credible interval, 84.7% to 95.3%). The safety profile of BNT162b2 vaccine consisted of short-term, non-serious events. The promotion and boost of the COVID-19 vaccination campaign represents a key public health measure useful to curb the spread of the pandemic especially in vulnerable contexts, such as hospitals, where health workers carry out a paramount role for the entire community, and requires further protection with a possible booster dose in view of autumn-winter 2021.


Subject(s)
COVID-19 , Vaccines , COVID-19 Vaccines , Humans , Immunization Programs , Retrospective Studies , SARS-CoV-2
3.
Vaccines (Basel) ; 9(3)2021 Mar 18.
Article in English | MEDLINE | ID: covidwho-1158403

ABSTRACT

Seasonal flu vaccination is one of the most important strategies for preventing influenza. The attitude towards flu vaccination in light of the COVID-19 pandemic has so far been studied in the literature mostly with the help of surveys and questionnaires. Whether a person chooses to be vaccinated or not during the COVID-19 pandemic, however, speaks louder than any declaration of intention. In our teaching hospital, we registered a statistically significant increase in flu vaccination coverage across all professional categories between the 2019/2020 and the 2020/2021 campaign (24.19% vs. 54.56%, p < 0.0001). A linear regression model, based on data from four previous campaigns, predicted for the 2020/2021 campaign a total flu vaccination coverage of 30.35%. A coverage of 54.46% was, instead, observed, with a statistically significant difference from the predicted value (p < 0.0001). The COVID-19 pandemic can, therefore, be considered as an incentive that significantly and dramatically increased adherence to flu vaccination among our healthcare workers.

4.
Int J Environ Res Public Health ; 18(5)2021 03 06.
Article in English | MEDLINE | ID: covidwho-1154372

ABSTRACT

Healthcare workers are at the forefront against COVID-19, worldwide. Since Fondazione Policlinico Universitario A. Gemelli (FPG) IRCCS was enlisted as a COVID-19 hospital, the healthcare workers deployed to COVID-19 wards were separated from those with limited/no exposure, whereas the administrative staff were designated to work from home. Between 4 June and 3 July 2020, an investigation was conducted to evaluate the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin (IgG) antibodies among the employees of the FPG using point-of-care (POC) and venous blood tests. Sensitivity, specificity, and predictive values were determined with reverse-transcription polymerase chain reaction on nasal/oropharyngeal swabs as the diagnostic gold standard. The participants enrolled amounted to 4777. Seroprevalence was 3.66% using the POC test and 1.19% using the venous blood test, with a significant difference (p < 0.05). The POC test sensitivity and specificity were, respectively, 63.64% (95% confidence interval (CI): 62.20% to 65.04%) and 96.64% (95% CI: 96.05% to 97.13%), while those of the venous blood test were, respectively, 78.79% (95% CI: 77.58% to 79.94%) and 99.36% (95% CI: 99.07% to 99.55%). Among the low-risk populations, the POC test's predictive values were 58.33% (positive) and 98.23% (negative), whereas those of the venous blood test were 92.86% (positive) and 98.53% (negative). According to our study, these serological tests cannot be a valid alternative to diagnose COVID-19 infection in progress.


Subject(s)
COVID-19 , Antibodies, Viral , Health Personnel , Hospitals , Humans , Rome , SARS-CoV-2 , Seroepidemiologic Studies , Serologic Tests
5.
J Clin Nurs ; 30(13-14): 1826-1837, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1059445

ABSTRACT

AIMS: To identify the main diagnostic features of SARS-CoV-2-positive patients at the time of hospitalisation and their prevalence. BACKGROUND: Since the COVID-19 outbreak in China in December of 2019, several studies attempted to identify the epidemiological, viral and clinical characteristics of SARS-CoV-2. Given the rapid widespread transmission of the COVID-19 disease worldwide, a more comprehensive and up-to-date understanding of its features is needed to better inform nurses, clinicians and public health policy makers. METHODS: A rapid review and meta-analysis were carried out to identify the main diagnostic features of SARS-CoV-2-positive patients at the time of hospitalisation. All case series, cross-sectional, case-control and cohort studies published from 01/01/2020 till 30/06/2020 in English and Chinese that stated all or at least two of the outcomes of interest (clinical features, laboratory and radiological findings) were included. We performed a random-effects model meta-analysis to calculate pooled prevalence and 95% confidence intervals. Conduction of the review adheres to the PRISMA checklist. RESULTS: 21 studies involving 8837 patients were included in the quantitative synthesis. Fever, cough and fatigue were the most common clinical features, while the most relevant laboratory abnormalities at the time of hospitalisation were lymphopenia, elevated C-reactive protein and lactate dehydrogenase. CT images showed a bilateral lung involvement, with ground glass infiltrates and patchy shadows on most patients. CONCLUSION: This review provides an up-to-date synthesis of main diagnostic features of SARS-CoV-2-positive patients at the time of hospitalisation. RELEVANCE TO CLINICAL PRACTICE: Our findings could provide guidance for nurses and clinicians to early identification of positive patients at the time of the hospitalisation through a complete definition of main clinical features, laboratory and CT findings.


Subject(s)
COVID-19/diagnosis , COVID-19/pathology , Cough , Fatigue , Fever , Humans
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