MALDI-TOF MS and Machine Learning Explanations for the Detection of SARS-CoV-2 Infection in Human Plasma: Fingerprints as a Strategy for Risk Assessment

Abstract

The COVID-19 pandemic proved to be a major public health challenge that had an enormously disruptive effect on the operational management of hospitals. It became especially important to find both diagnostic and prognostic methods for risk severity evaluation. Here, a MALDI-TOF MS method for the profiling of plasma samples combined with machine learning (ML) and its explanations was developed to identify SARS-CoV-2 infection while also allowing for the classification of patients by the severity of the disease. A prospective study of the most important m/z values that can be used as biomarkers using the SHAP state of the art ML explicability technique was also studied. The fingerprint data-analysis strategy is concerned with pattern expression in serum samples, providing information about SARS-CoV-2. The trained model is found to have a significant power of discrimination between controls and COVID-19 patients, controls and patients in the ICU, and controls and patients who had been in the ICU, and so, a spectral signature can be identified to separate and identify these cases. Moreover, there were differences in the spectral signatures between patients who were in the ICU and those who were not admitted to the ICU or had left the ICU. In conclusion, MALDI-TOF MS and advanced ML algorithms demonstrated remarkable discriminatory power between controls and those diagnosed with COVID-19/ICU/Post-ICU conditions. Also, it provides a valuable tool for stratifying patients based on their severity symptoms. Finally, a set of potential biomarkers that play a crucial role in the discrimination were identified