Episode I. Exploring Streptococcus pneumoniae capsular typing through MALDI-TOF mass spectrometry and machine-learning algorithms in Argentina: Identifying prevalent NON PCV13 serotypes alongside PCV13 serotypes.

Abstract Laboratory surveillance of Streptococcus pneumoniae serotypes is crucial for the successful implementation of vaccines to prevent invasive pneumococcal diseases. The reference method of serotyping is the Quellung reaction, which is labor-intensive and expensive. In the last few years, the introduction of MALDI-TOF MS into the microbiology laboratory has been revolutionary. In brief, this new technology compares protein profiles by generating spectra based on the m/z ratio. We evaluated the performance of MALDI-TOF MS for typing serotypes of S. pneumoniae isolates included in the PCV13 vaccine using a machine learning approach. We challenged our classification algorithms with a total of new 215 isolates of S. pneumoniae from Argentinian nationwide surveillance. Our work involved a thorough evaluation and comparison of multiple machine learning models. Through this evaluation, we achieved high accuracy values, indicating the effectiveness of the approach taken in solving the problem. Additionally, we attained high kappa index values, indicating that the predictions made by the models were highly accurate and reliable. We also carefully considered the strengths and limitations of each model. As such, our results are highly valuable and have important implications for future research and applications.