Background : Most of the common risk factors for severe outcomes of COVID‐19 are correlated with poor oral health, tooth loss, and periodontitis. This has pointed to a possible relationship between oral and systemic health in COVID‐19 patients. Hence, this study aimed to assess the dental and periodontal status of hospitalized COVID‐19 patients and their associations with the incidence of adverse COVID‐19 outcomes. Methods : We included 128 hospital patients aged between 20 and 97 years and with diagnoses of COVID‐19 in this prospective observational study. Dental and periodontal status was assessed using in‐hospital clinical examinations, including the Decayed, Missing, and Filled Teeth index, periodontal status, and tooth loss patterns (Eichner index). Associations between oral health measures, the severity of COVID‐19 symptoms, and hospitalization endpoints were tested using chi‐square test and incidence rate ratio (IRR) estimation using a generalized linear model with log‐Poisson regression. The regression models used a block‐wise selection of predictors for oral health‐related variables, comorbidities, and patients’ ages. Results : Overall, poor oral health conditions were highly prevalent and associated with critical COVID‐19 symptoms, higher risk for admission in the intensive care unit (ICU), and death. Periodontitis was significantly associated with ICU admission [IRR = 1.44 (95%CI = 1.07–1.95); p = 0.017], critical symptoms [IRR = 2.56 (95%CI = 1.44–4.55); p = 0.001], and risk of death [IRR = 2.05 (95%CI = 1.12‐3.76); p = 0.020] when adjusted for age and comorbidities. The Eichner index (classes B and C) was associated with ICU admission. Conclusions : There was a positive association between deleterious oral health‐related conditions, especially periodontitis, and severe COVID‐19 outcomes in hospitalized COVID‐19 patients. This article is protected by copyright. All rights reserved

Viral genetic sequencing using real-time reverse transcriptionpolymerase chain reaction (qRT-PCR) is the most widely used technique for diagnosing coronavirus disease 2019 (COVID-19) due to its high sensitivity and specificity in cases of acute infection (Wang et al., 2020). However, qRT-PCR tests can be labor-intensive, requiring specialized equipment and a centralized laboratory, which increases the wait time for the results and the costs (Dinnes et al., 2021). In a systematic review, Mallet et al. (2020) demonstrated that the rate of qRT-PCR test false-negative results increases in nasopharyngeal samples collected 10 days after symptom onset. In this way, rapid immunochromatographic tests present a complementary diagnostic method, helping to identify infected patients (Dinnes et al., 2021; Zhao et al., 2020). Available rapid tests benefit from screening larger populations, with and without symptoms, in locations other than healthcare settings and would provide a faster diagnosis to allow early prevention of COVID-19 spread (Dinnes et al., 2021).

Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 μL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.

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