ABSTRACT
The SARS-CoV-2 infections are still imposing a great public health challenge despite the recent developments in vaccines and therapy. Searching for diagnostic and prognostic methods that are fast, low-cost and accurate is essential for disease control and patient recovery. The MALDI-TOF mass spectrometry technique is rapid, low cost and accurate when compared to other MS methods, thus its use is already reported in the literature for various applications, including microorganism identification, diagnosis and prognosis of diseases. Here we developed a prognostic method for COVID-19 using the proteomic profile of saliva samples submitted to MALDI-TOF and machine learning algorithms to train models for COVID-19 severity assessment. We achieved an accuracy of 88.5%, specificity of 85% and sensitivity of 91.5% for classification between mild/moderate and severe conditions. Then, we tested the model performance in an independent dataset, we achieved an accuracy, sensitivity and specificity of 67.18, 52.17 and 75.60% respectively. Saliva is already reported to have high inter-sample variation; however, our results demonstrates that this approach has the potential to be a prognostic method for COVID-19. Additionally, the technology used is already available in several clinics, facilitating the implementation of the method. Further investigation using a bigger dataset is necessary to consolidate the technique.
ABSTRACT
SARS-CoV-2 quickly spread in the worldwide population by contact with oral and respiratory secretions of infected individuals, imposing social restrictions to control the infection. Massive testing is essential to breaking the chain of COVID-19 transmission. The aim of this study was to compare the performance of at-home self-collected samples - saliva and combined nasal-oropharyngeal swabs (NOP) - for SARS-CoV-2 detection in a telemedicine platform for COVID-19 surveillance. We analyzed 201 patients who met the criteria of suspected COVID-19. NOP sampling were combined (nostrils and oropharynx) and saliva collected using a cotton pad device. Detection of SARS-COV-2 was performed by using the Altona RealStar(R) SARS-CoV-2 RT-PCR Kit 1.0. According to our data, there was an overall significant agreement ({kappa} coefficient value of 0.58) between the performances of saliva and NOP. Assuming that positive results in either sample represent true infections, 70 patients positive for SARS-CoV-2 were identified, with 52/70 being positive in NOP and 55/70 in saliva. This corresponds to sensitivities of 74.2% (95% CI; 63.7% to 83.1%) for NOP and 78.6% (95% CI; 67.6% to 86.6%) for saliva. We also found a strong correlation ({beta}-coefficients < 1) between the cycle threshold values in saliva and NOP. Ageusia was the only symptom associated with patients SARS-CoV-2 positive only in NOP (p=0.028). In conclusion, our data show the feasibility of using at-home self-collected samples (especially saliva), as an adequate alternative for SARS-CoV-2 detection. This new approach of testing can be useful to develop strategies for COVID-19 surveillance and for guiding public health decisions.
