Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Investigating the Unfavored Factors That Interfere MALDI-TOF Based AI in Predicting Antibiotic Resistance

Version 1 : Received: 3 January 2022 / Approved: 10 January 2022 / Online: 10 January 2022 (19:01:57 CET)

How to cite: Wang, H.; Liu, Y.; Tseng, Y.; Chung, C.; Lin, T.; Yu, J.; Huang, Y.; Lu, J. Investigating the Unfavored Factors That Interfere MALDI-TOF Based AI in Predicting Antibiotic Resistance. Preprints 2022, 2022010131 (doi: 10.20944/preprints202201.0131.v1). Wang, H.; Liu, Y.; Tseng, Y.; Chung, C.; Lin, T.; Yu, J.; Huang, Y.; Lu, J. Investigating the Unfavored Factors That Interfere MALDI-TOF Based AI in Predicting Antibiotic Resistance. Preprints 2022, 2022010131 (doi: 10.20944/preprints202201.0131.v1).

Abstract

Combining Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility test (AST) of S. aureus. Based on the AI predictive probability, the cases with probabilities between low and high cut-offs are defined as “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. A total 479 S. aureus isolates were collected, analyzed by MALDI-TOF, and AST prediction, standard AST were obtained in a tertiary medical center. The predictions were categorized into the correct prediction group, wrong prediction group, and grey zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For MRSA, larger cefoxitin zone size was found in the wrong prediction group. MLST of the MRSA isolates in the grey zone group revealed that uncommon strain types composed 80%. Amid MSSA isolates in the grey zone group, the majority (60%) was composed of over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity would contribute to suboptimal predictive performance.

Keywords

methicillin-resistant Staphylococcus aureus; Matrix-Assisted Laser Desorption/Ionization Time-of-Flight; antibiotic susceptibility test; artificial intelligence

Subject

MEDICINE & PHARMACOLOGY, General Medical Research

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