Direct prediction of carbapenem-resistant, carbapenemase-producing, and colistin-resistant Klebsiella pneumoniae isolates from routine MALDI-TOF mass spectra using machine learning and outcome evaluation

The objective of this study was to develop a rapid prediction method for carbapenem-resistant Kleb-siella pneumoniae (CRKP) and colistin-resistant K. pneumoniae (ColRKP) based on routine MALDI-TOF mass spectrometry (MS) results in order to formulate a suitable and rapid treatment strategy. A total of 830 CRKP and 1462 carbapenem-susceptible K. pneumoniae (CSKP) isolates were collected; 54 ColRKP isolates and 1592 colistin-intermediate K. pneumoniae (ColIKP) isolates were also included. Routine MALDI-TOF MS, antimicrobial susceptibility testing, NG-Test CARBA 5, and resistance gene detection were followed by machine learning (ML). Using the ML model, the accuracy and area under the curve for differentiat-ing CRKP and CSKP were 0.8869 and 0.9551, respectively, and those for ColRKP and ColIKP were 0.8361 and 0.8447, respectively. The most important MS features of CRKP and ColRKP were m/z 4520-4529 and m/z 4170-4179, respectively. Of the CRKP isolates, MS m/z 4520-4529 was a potential biomarker for dis-tinguishing KPC from OXA, NDM, IMP, and VIM. Of the 34 patients who received preliminary CRKP ML prediction results (by texting), 24 (70.6%) were confirmed to have CRKP infection. The mortality rate was lower in patients who received antibiotic regimen adjustment based on the preliminary ML prediction (4/14, 28.6%). In conclusion, the proposed model can provide rapid results for differentiating CRKP and CSKP, as well as ColRKP and ColIKP. The combination of ML-based CRKP with preliminary reporting of results can help physicians alter the regimen approximately 24 h earlier, resulting in improved survival of patients with timely antibiotic intervention.(c) 2023 Elsevier Ltd and International Society of Antimicrobial Chemotherapy. All rights reserved.

Automated summary

The study aimed to develop a rapid prediction method for carbapenem-resistant Kleb-siella pneumoniae (CRKP) and colistin-resistant K. pneumoniae (ColRKP) based on routine MALDI-TOF mass spectrometry (MS) results to guide suitable and swift treatment strategies. By utilizing machine learning (ML), the accuracy and area under the curve of the ML model for distinguishing CRKP and CSKP were 0.8869 and 0.9551, respectively. Additionally, the ML model achieved an accuracy of 0.8361 and area under the curve of 0.8447 for differentiating ColRKP and ColIKP. The proposed model, combined with preliminary reporting of results, could potentially lead to improved patient survival through timely antibiotic intervention.