Swab test in biological fluids as predictor of COVID-19 transmission risk during surgery: a prospective cross-sectional study from an Italian COVID center.

BACKGROUND: The contamination of body fluids by Severe Acute Respiratory Syndrome Coronavirus 2 during surgery is current matter of debate in the scientific literature concerning CoronaVIrus Disease 2019. Surgical guidelines were published during the first wave of the COVID-19 pandemic and recommended to avoid laparoscopic surgery as much as possible, in fear that the chimney effect of high flow intraperitoneal gas escape during, and after, the procedure would increase the risk of viral transmission. AIM: The aim of this study was to evaluate the possibility of SARS-CoV-2 transmission during surgery by searching for viral RNA in serial samplings of biological liquids. METHODS: This is a single center prospective cross-sectional study. We used a real-time reverse transcriptase (RT) polymerase chain reaction (PCR) test to perform swab tests for the qualitative detection of nucleic acid from SARS-CoV-2 in abdominal fluids, during emergency surgery and on the first post-operative day. In the case of thoracic surgery, we performed a swab test of pleural fluids during chest drainage placement as well as on the first post-operative day. RESULTS: A total of 20 samples were obtained: 5 from pleural fluids, 13 from peritoneal fluids and two from biliary fluid. All 20 swabs performed from biological fluids resulted negative for SARS-CoV-2 RNA detection. CONCLUSION: To date, there is no scientific evidence of possible contagion by laparoscopic aerosolization of SARS-CoV-2, neither is certain whether the virus is effectively present in biological fluids.

COVID-19: identifying the main outcome predictors. A retrospective cohort study in Northern Italy.

OBJECTIVE: The need for efficient drugs and early treatment of patients with SARS-CoV-2 infection developing COVID-19 symptoms is of primary importance in daily clinical practice and it is certainly among the most difficult medical challenges in the current century. Recognizing those patients who will need stronger clinical efforts could effectively help doctors anticipate the eventual need for intensification of care (IoC) and choose the best treatment in order to avoid worse outcomes. PATIENTS AND METHODS: We enrolled 501 patients, consecutively admitted to our two COVID hospitals, and collected their clinical, anamnestic and laboratory data on admission. The aim of this retrospective study was to identify those data that are strictly associated with COVID-19 outcomes (IoC and in-hospital death) and that could somehow be intended as predictors of these outcomes. This allowed us to provide a "sketch" of the patient who undergoes, more often than others, an intensification of care and/or in-hospital death. RESULTS: Males were found to have a double risk of needing an IoC (OR=2.11) and a significant role was played by both the PaO2/FiO2 ratio on admission (OR=0.99) and serum LDH (OR=1.01). The main predictors of in-hospital death were age (OR=1.08) and the PaO2/FiO2 ratio on admission (OR=0.99). CONCLUSIONS: Male patients with high serum LDH on admission are those who undergo more often an intensification of care among COVID-19 inpatients. Both age and respiratory performances on admission modify the prognosis within the hospitalization period.

Early Prediction of COVID-19 Outcome: Contrasting Clinical Scores and Computational Intelligence Methods

Triaging incoming patients is critical for an optimal allocation of hospital resources, especially during a pandemic, when these tend to be quickly depleted. A typical approach for predicting patients’ outcomes relies on clinical scores such as the Charlson Comorbidity Index (CCI). CCI-based triaging is a reliable approach for estimating the mortality risk in the general patients’ population. However, this score is not optimized for predicting mortality in specific populations such as the one represented by COVID inpatients, often the most represented population in the emergency department cohorts during the current pandemic. Motivated by this, this chapter describes the development of a new COVID-19-specific clinical score: The General Assessment of SARS-CoV-2 patients Score (GASS). The score builds on the clinical experience gained during the first phase of the pandemic, and it is based on both clinical and laboratory data. It was aimed at predicting the 30-day mortality outcome of hospitalized COVID-19 patients and showed markedly better accuracy than the CCI. Furthermore, this chapter introduces an additional predictive model based on a classical Computational Intelligence method. Specifically, it describes the development and validation of a feedforward artificial Neural Network (NN) that automatically maps patients’ clinical and laboratory data to a 30-day mortality-risk score. Critically, the NN-based method was shown to be more accurate at predicting 30-day mortality of COVID-19 patients than both the CCI and GASS scores. However, the intrinsic black-box nature of the NN-based method makes it hard to reach an intuitive understanding of the internal computations underlying its decision process. This might affect its general acceptance among clinicians, and lead them to prefer using the GASS score. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

SARS-CoV-2 infection and H1N1 vaccination: does a relationship between the two factors really exist? A retrospective analysis of a territorial cohort in Ferrara, Italy.

OBJECTIVE: SARS-CoV-2 has been compared with other strains of coronaviruses, SARS-CoV and MERS-CoV, and with the flu viruses: all of them manifest themselves with respiratory symptoms and, although their genetic patterns are similar, the spread of SARS-CoV-2 infection has quickly reached global dimensions, demonstrating that SARS-CoV-2 is a virus with greater spreading capacity, albeit less lethal. Compared with influenza viruses, coronaviruses have a longer incubation period and the patients with coronaviruses' syndromes develop more severe diseases requiring frequent hospitalizations and intensive care admissions. The aim was to explore the relationships between seasonal influenza vaccination and coronavirus infection and to understand whether this hypothetic role by the flu vaccines modifies SARS-CoV-2 infection's outcomes. PATIENTS AND METHODS: In this retrospective, multicenter study, we enrolled 952 patients diagnosed with SARS-CoV-2 infection; 448 were admitted to our two main hospitals in Ferrara territory, while the remaining 504 were isolated at home. We compared the group of patients who had been vaccinated for influenza in the previous 12 months to that of unvaccinated patients. RESULTS: Significant differences were found for both the need for hospitalization and 30-day mortality between vaccinated and unvaccinated patients. We found age to be the only independent risk factor for a worse 30-day prognosis, while gender, influenza vaccinations and age itself were independent risk factors for undergoing hospitalization. CONCLUSIONS: In our groups of patients, we found a relationship between seasonal influenza vaccinations and SARS-CoV-2 infection. Age seems to be the main risk factor for short-term mortality in COVID-19 inpatients, while the influenza vaccination is, together with gender and age itself, a determining factor in predicting the need for hospitalization.