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MALDI-TOF MS and Machine Learning Explanations for the Detection of SARS-CoV‑2 Infection in Human Plasma:
Meritxell Deulofeu1, Esteban García-Cuesta2, Eladia María Peña-Méndez3
1Research Group of Clinical Anatomy, Embryology and Neuroscience (NEOMA), Department of Medical Sciences, University of Girona, Girona 17003, Catalonia, Spain.
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.
Insights
Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) combined with machine learning effectively identifies SARS-CoV-2 infection and stratifies patient severity. This approach aids in diagnosing COVID-19 and evaluating patient outcomes.
Area of Science:
- Clinical proteomics and metabolomics
- Infectious disease diagnostics
- Machine learning in healthcare
Background:
- The COVID-19 pandemic presented significant challenges to hospital operations and necessitated advanced diagnostic and prognostic tools.
- Accurate risk assessment and patient stratification are crucial for managing severe COVID-19 cases and optimizing treatment strategies.
Purpose of the Study:
- To develop and validate a MALDI-TOF MS-based method for identifying SARS-CoV-2 infection using plasma samples.
- To classify patients based on disease severity (COVID-19, ICU, Post-ICU) using machine learning and spectral data.
- To identify potential protein biomarkers indicative of SARS-CoV-2 infection and disease progression.
Main Methods:
- Plasma samples were analyzed using MALDI-TOF MS to generate spectral fingerprints.
- Machine learning algorithms, including SHAP for model interpretability, were employed to analyze spectral data.
- A prospective study design was used to evaluate the discriminatory power of identified spectral features and potential biomarkers.
Main Results:
- The developed MALDI-TOF MS and ML model demonstrated significant discriminatory power between healthy controls and COVID-19 patients, as well as ICU and post-ICU patients.
- Distinct spectral signatures were identified, enabling the separation and identification of different patient groups based on infection status and severity.
- Differences in spectral profiles were observed between patients requiring ICU admission and those who did not, highlighting prognostic potential.
Conclusions:
- MALDI-TOF MS coupled with advanced machine learning algorithms offers a powerful tool for diagnosing COVID-19 and stratifying patients by disease severity.
- The study successfully identified potential biomarkers through spectral analysis, contributing to a deeper understanding of SARS-CoV-2 pathophysiology.
- This integrated approach provides a valuable method for clinical decision-making and patient management during infectious disease outbreaks.
