Evaluation of the Factors Associated with Reinfections towards SARS-CoV-2 Using a Case Control Design.

OBJECTIVE: The risk of reinfection with SARS-CoV-2 has been rapidly increased with the circulation of concerns about variants. So, the aim of our study was to evaluate the factors that increase the risk of this reinfection in healthcare workers compared to those who have never been positive and those who have had only one positivity. METHODS: A case-control study was carried out at the Teaching Hospital Policlinico Umberto I in Rome, Sapienza University of Rome, in the period between 6 March 2020 and 3 June 2022. Cases are healthcare workers who have developed a reinfection with the SARS-CoV-2 virus, while controls were either healthcare workers who tested positive once or those who have never tested positive for SARS-CoV-2. RESULTS: 134 cases and 267 controls were recruited. Female gender is associated with a higher odds of developing reinfection (OR: 2.42; 95% CI: 1.38-4.25). Moreover, moderate or high alcohol consumption is associated with higher odds of reinfection (OR: 1.49; 95% CI: 1.19-1.87). Diabetes is also associated with higher odds of reinfection (OR: 3.45; 95% CI: 1.41-8.46). Finally, subjects with increased red blood cell counts have higher odds of reinfection (OR: 1.69; 95% CI: 1.21-2.25). CONCLUSION: From the prevention point of view, these findings indicate that particular attention should be paid to subjects with diabetes mellitus, women and alcoholic drinkers. These results could also suggest that contact tracing represents a fundamental approach model against the SARS-CoV-2 pandemic, together with the health information of participants.

Risk of Seven-Day Worsening and Death: A New Clinically Derived COVID-19 Score.

This monocentric, retrospective, two-stage observational study aimed to recognize the risk factors for a poor outcome in patients hospitalized with SARS-CoV-2 infection, and to develop and validate a risk score that identifies subjects at risk of worsening, death, or both. The data of patients with SARS-CoV-2 infection during the first wave of the pandemic were collected and analyzed as a derivation cohort. Variables with predictive properties were used to construct a prognostic score, which was tried out on a validation cohort enrolled during the second wave. The derivation cohort included 494 patients; the median age was 62 and the overall fatality rate was 22.3%. In a multivariable analysis, age, oxygen saturation, neutrophil-to-lymphocyte ratio, C-reactive protein and lactate dehydrogenase were independent predictors of death and composed the score. A cutoff value of 3 demonstrated a sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) of 93.5%, 68.5%, 47.4% and 97.2% for death, and 84.9%, 84.5%, 79.6% and 87.9% for worsening, respectively. The validation cohort included 415 subjects. The score application showed a Se, Sp, PPV and NPV of 93.4%, 61.6%, 29.5% and 98.1% for death, and 81%, 76.3%, 72.1% and 84.1% for worsening, respectively. We propose a new clinical, easy and reliable score to predict the outcome in hospitalized SARS-CoV-2 patients.

Monocyte absolute count as a preliminary tool to distinguish between SARS-CoV-2 and influenza A/B infections in patients requiring hospitalization.

Since the most frequent symptoms of novel coronavirus 2019 disease (COVID-19) are common in influenza A/B (FLU), predictive models to distinguish between COVID-19 and FLU using standardized non-specific laboratory indicators are needed. The aim of our study was to evaluate whether a recently dynamic nomogram, established in the Chinese population and based on age, lymphocyte percentage and monocyte absolute count, might apply to a different context. We collected data from 299 patients (243 with COVID-19 and 56 with FLU) at Policlinico Umberto I, Sapienza University of Rome. The nomogram included age, lymphocyte percentage and monocyte absolute count to differentiate COVID-19 from FLU. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated for all associations. Multivariate logistic regression models were used to adjust for potential confounding. A p-value of less than 0.05 was considered statistically significant. Patients with COVID-19 had higher age, lymphocyte percentage and monocyte absolute count than patients with FLU. Although univariate analysis confirmed that age, lymphocyte percentage and monocyte absolute count were associated with COVID-19, only at multivariate analysis was monocyte count statistically significant as a predictive factor of COVID-19. Using receiver operating characteristic (ROC) curves, we found that a monocyte count >0.35x1000/mL showed an AUC of 0.680 (sensitivity 0.992, specificity 0.368). A dynamic nomogram including age, lymphocyte percentage and monocyte absolute count cannot be applied to our context, probably due to differences in demographic characteristics between Italian and Chinese populations. However, our data showed that monocyte absolute count is highly predictive of COVID-19, suggesting its potential role above all in settings where prompt PCR nasopharyngeal testing is lacking.