JMIR Publications

ABSTRACT

Background:

The rising prevalence and swift spread of multidrug-resistant gram-negative bacteria (MDR-GNBs), especially Klebsiella pneumonia (KP), present a critical global health threat highlighted by World Health Organization (WHO), with mortality rates soaring approximately 50% with inappropriate antimicrobial treatment.

Objective:

This study advances a novel strategy to develop an Artificial Intelligence-Clinical Decision Support System (AI-CDSS) that combines machine learning with Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS), aiming to significantly improve the accuracy and speed of diagnosing antibiotic resistance, directly addressing the grave health risks posed by the widespread dissemination of pandrug-resistant GNB across numerous countries.

Methods:

A comprehensive dataset comprising 165,299 bacterial specimens and 11,996 KP isolates was meticulously analyzed using MALDI-TOF MS technology. Advanced machine learning (ML) algorithms were harnessed to sculpt predictive models that ascertain resistance to quintessential antibiotics, notably levofloxacin and ciprofloxacin, by utilizing the amassed spectral data.

Results:

Our ML models revealed remarkable proficiency in forecasting antibiotic resistance, with the Random Forest classifier emerging as particularly effective in predicting resistance to both levofloxacin and ciprofloxacin, achieving the highest AUC of 0.95. Performance metrics across different models, including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score , were detailed, underlining the potential of these algorithms in aiding the development of precision treatment strategies.

Conclusions:

This investigation highlights the synergy between MALDI-TOF MS and ML as a beacon of hope against the escalating threat of antibiotic resistance. The advent of AI-CDSS heralds a new era in clinical diagnostics, promising a future in which rapid and accurate resistance prediction becomes a cornerstone in combating infectious diseases. Through this innovative approach, we mastered the challenge posed by KP and other MDR pathogens, marking a significant milestone in our journey towards global health security.