Assessing the temporal and cause-effect relationship between myocarditis and mRNA COVID-19 vaccines. A retrospective observational study.

OBJECTIVE: In 2021, the US Centers for Disease Control and Prevention reported increased cases of myocarditis and pericarditis in the United States after mRNA COVID-19 vaccines. Our study aims to estimate the incidence of myocarditis in Apulia (Southern Italy) and the cause-effect relationship between COVID-19 mRNA vaccines and the risk of myocarditis. METHODS: The Apulian regional archive of hospital discharge forms was used to define the cases of myocarditis in Apulia, considering data from 2017 to 2022. The overall vaccination status of patients was assessed via data collected from the Regional Immunization Database. The history of SARS-CoV-2 infection was extracted from the Italian Institute of Health platform. RESULTS: Since 2017, 5687 cases of myocarditis have been recorded in Apulian subjects; the overall incidence described a decreasing trend, with a slight increase in 0-40 years-old subjects. From 2021 to 2022, 2,930,276 doses of COVID-19 mRNA vaccines were administered; a diagnosis of myocarditis after the second dose of the mRNA vaccine was reported for 894 (0.03%) of Apulian inhabitants, with an incidence rate of 17.9 × 1,000,000 persons-month. The multivariate analysis, adjusted for age, sex, underlying medical conditions, and diagnosis of COVID-19, showed that mRNA vaccination is a protective factor for myocarditis even in younger subjects (aOR = 0.4; 95% CI = 0.3-0.5). CONCLUSION: A temporal association between an exposure and an outcome is not equivalent to a causal association. Our study underlines how an approach that considers the other potential causes of myocarditis (primarily COVID-19) and a causality assessment must be prioritized in the study of the topic.

Impact of COVID-19 pandemic on emergency and elective surgery. A retrospective observational analysis in Apulia, southern Italy.

Introduction: In Italy, at the beginning of the COVID19 pandemic, only emergency and life-saving elective surgical procedures were allowed with obvious limitations in terms of numbers of operable cases. The aim of our study is to evaluate the performance of surgical activities by Apulian healthcare facilities (Southern Italy) under the pandemic emergency pressure. Methods: The surgical procedures in study were identified via the Apulian regional archive of hospital discharge forms. We used the ICD9 codes in order to define the elective and urgency surgeries in analysis, and we extended our search to all procedures performed from 2019 to 2021. Results: The number of all procedures decreased from 2019 to 2020; the reduction was higher for elective surgery (-43.7%) than urgency surgery (-15.5%). In 2021, an increase compared to 2020 was recorded for all procedures; nevertheless, elective surgeries registered a further slightly decrease compared to 2019 (-12.4%), while a slightly increase was observed for urgency surgeries (+3.5%). No particular variation was observed considering sex and age at surgery of the patients, and days of hospitalization from 2019 to 2021. Conclusions: The impact of COVID19 on Apulian regional health system has been extremely shocked and has required the implementation of strategies aimed at containing the infection and guaranteeing health services as far as possible. A new paradigm of hospital care for SARS-COV-2 patients in the post-emergency phase in Italy is needed, in order to optimize the resources available and to guarantee high standards of quality and efficiency for citizens.

Impact of COVID-19 pandemic on flu vaccine uptake in healthcare workers in Europe: a systematic review and meta-analysis.

INTRODUCTION: Influenza immunization policies in Europe primarily target at-risk and vulnerable subjects. Healthcare workers (HCWs) are a key focus of vaccination campaigns. Our systematic review and meta-analysis aim to evaluate the role of the COVID-19 pandemic on influenza vaccine uptake among HCWs since the 2020/21 influenza season. AREAS COVERED: Fourteen studies were included in the meta-analysis and systematic review, selected from scientific articles available in MEDLINE/PubMed, ISI Web of Knowledge, and Scopus databases between 1 January 2021 and 15 January 2023 The analysis revealed a significant relationship between influenza vaccine uptake and COVID-19 related determinants, such as willingness to receive COVID-19 vaccination, fear of COVID-19, and differentiating between influenza and COVID-19 symptoms (OR = 5.70; 95%CI = 2.08-15.60). Several studies reported higher vaccination coverages in the 2020/21 season compared to previous seasons, with VC values ranging from + 17% to + 38% compared to the 2019/20 season. The included studies identified a shift in HCWs' attitudes toward influenza vaccination, attributed to increased awareness due to the COVID-19 pandemic. EXPERT OPINION: Vaccine hesitancy is common among HCWs in Europe, necessitating mutual strategies across all European countries. So far, mandatory vaccination policies have shown the most potential in achieving high and sustainable influenza vaccination rates among HCWs.

Burden of COVID-19 disease and vaccine coverages in Apulian splenectomized patients: A retrospective observational study.

Splenectomy/asplenia is a condition associated with immune-compromission and specific vaccines are recommended for these patients, including the anti-COVID-19 vaccine. Among the high-risk group for which vaccination was prioritized in Italy, the immunocompromised patients after therapies or treatments were included. The Apulian regional archive of hospital discharge forms was used to define the list of splenectomized Apulian inhabitants, considering data from 2015 through 2020. The overall vaccination status of asplenic patients was assessed via data collected from the Regional Immunization Database. The history of SARS-CoV-2 infection and the infectious disease outcomes were extracted from the Italian Institute of Health platform "Integrated surveillance of COVID-19 cases in Italy". 1219 Apulian splenectomized inhabitants were included; the incidence rate of SARS-CoV-2 infection was 15.0 per 100 persons-year with a proportion of re-infection equal to 6.4%; the proportion of hospitalization was 2.9%, with a case-fatality rate of 2.6%. The vaccine coverage (VC) for the anti-COVID-19 vaccine basal routine was 64.2%, for the first booster dose was 15.4%, and for the second booster dose was 0.6%. A multifactorial approach is needed to increase the vaccination uptake in this sub-group population and to increase the awareness of the asplenia-related risks to patients and health personnel.

Prediction of metabolic syndrome: A machine learning approach to help primary prevention.

AIMS: To describe the performance of machine learning (ML) applied to predict future metabolic syndrome (MS), and to estimate lifestyle changes effects in MS predictions. METHODS: We analyzed data from 17,182 adults attending a checkup program sequentially (37,999 visit pairs) over 17 years. Variables on sociodemographic attributes, clinical, laboratory, and lifestyle characteristics were used to develop ML models to predict MS [logistic regression, linear discriminant analysis, k-nearest neighbors, decision trees, Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting]. We have tested the effects of changes in lifestyle in MS prediction at individual levels. RESULTS: All models showed adequate calibration and good discrimination, but the LGBM showed better performance (Sensitivity = 87.8 %, Specificity = 70.2 %, AUC-ROC = 0.86). Causal inference analysis showed that increasing physical activity level and reducing BMI by at least 2 % had an effect of reducing the predicted probability of MS by 3.8 % (95 % CI = -4.8 %; -2.7 %). CONCLUSION: ML models based on data from a checkup program showed good performance to predict MS and allowed testing for effects of lifestyle changes in this prediction. External validation is recommended to verify models' ability to identify at-risk individuals, and potentially increase their engagement in preventive measures.

COVID-19 diagnosis prediction in emergency care patients: a machine learning approach

The coronavirus disease (COVID-19) pandemic has increased the necessity of immediate clinical decisions and effective usage of healthcare resources. Currently, the most validated diagnosis test for COVID-19 (RT-PCR) is in shortage in most developing countries, which may increase infection rates and delay important preventive measures. The objective of this study was to predict the risk of positive COVID-19 diagnosis with machine learning, using as predictors only results from emergency care admission exams. We collected data from 235 adult patients from the Hospital Israelita Albert Einstein in Sao Paulo, Brazil, from 17 to 30 of March, 2020, of which 102 (43%) received a positive diagnosis of COVID-19 from RT-PCR tests. Five machine learning algorithms (neural networks, random forests, gradient boosting trees, logistic regression and support vector machines) were trained on a random sample of 70% of the patients, and performance was tested on new unseen data (30%). The best predictive performance was obtained by the support vector machines algorithm (AUC: 0.85; Sensitivity: 0.68; Specificity: 0.85; Brier Score: 0.16). The three most important variables for the predictive performance of the algorithm were the number of lymphocytes, leukocytes and eosinophils, respectively. In conclusion, we found that targeted decisions for receiving COVID-19 tests using only routinely-collected data is a promising new area with the use of machine learning algorithms.