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Using machine learning techniques to predict antimicrobial resistance in stone disease patients

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Abstract

Purpose

Artificial intelligence is part of our daily life and machine learning techniques offer possibilities unknown until now in medicine. This study aims to offer an evaluation of the performance of machine learning (ML) techniques, for predicting bacterial resistance in a urology department.

Methods

Data were retrieved from laboratory information system (LIS) concerning 239 patients with urolithiasis hospitalized in the urology department of a tertiary hospital over a 1-year period (2019): age, gender, Gram stain (positive, negative), bacterial species, sample type, antibiotics and antimicrobial susceptibility. In our experiments, we compared several classifiers following a tenfold cross-validation approach on 2 different versions of our dataset; the first contained only information of Gram stain, while the second had knowledge of bacterial species.

Results

The best results in the balanced dataset containing Gram stain, achieve a weighted average receiver operator curve (ROC) area of 0.768 and F-measure of 0.708, using a multinomial logistic regression model with a ridge estimator. The corresponding results of the balanced dataset, that contained bacterial species, achieve a weighted average ROC area of 0.874 and F-measure of 0.783, with a bagging classifier.

Conclusions

Artificial intelligence technology can be used for making predictions on antibiotic resistance patterns when knowing Gram staining with an accuracy of 77% and nearly 87% when identifying specific microorganisms. This knowledge can aid urologists prescribing the appropriate antibiotic 24–48 h before test results are known.

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Abbreviations

ML:

Machine learning

LIS:

Laboratory information system

ROC:

Receiver operator curve

UTI:

Urinary tract infections

UCA-UTI:

Urinary calculi associated urinary tract infections

AMR:

Multidrug antimicrobial resistance

AI:

Artificial intelligence

HIS:

Hospital information system

CLSI:

Guidelines of laboratory standards institute

MIC:

Minimum inhibitory concentration

MMC:

Matthews correlation coefficient

PRC:

Precision–recall plot

AUC-PR:

Precision–recall curve

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Funding

This research received no external funding.

Author information

Author notes

  1. Lazaros Tzelves, Lazaros Lazarou and Georgios Feretzakis contributed equally as first authors.

Authors and Affiliations

  1. 2nd Department of Urology, Sismanogleio General Hospital, National and Kapodistrian University of Athens, Sismanogleiou 37, Marousi, 15126, Athens, Greece

    Lazaros Tzelves, Lazaros Lazarou, Panagiotis Mourmouris, Marinos Berdempes, Ioannis Manolitsis, Iraklis Mitsogiannis, Andreas Skolarikos & Ioannis Varkarakis

  2. School of Science and Technology, Hellenic Open University, Patras, Greece

    Georgios Feretzakis & Dimitris Kalles

  3. Department of Quality Control, Research and Continuing Education, Sismanogleio General Hospital, Marousi, Greece

    Georgios Feretzakis

  4. Information Technologies Department, Sismanogleio General Hospital, Marousi, Greece

    Georgios Feretzakis & Evangelos Loupelis

  5. Department of Urology, New York Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA

    Spyridon Basourakos

Authors
  1. Lazaros Tzelves
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  2. Lazaros Lazarou
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  3. Georgios Feretzakis
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  4. Dimitris Kalles
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  5. Panagiotis Mourmouris
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  6. Evangelos Loupelis
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  7. Spyridon Basourakos
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  8. Marinos Berdempes
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  9. Ioannis Manolitsis
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  10. Iraklis Mitsogiannis
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  11. Andreas Skolarikos
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  12. Ioannis Varkarakis
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Contributions

Conceptualization: LT, GF and IV, Methodology: GF, LL, Software EL, SB, Validation: DK, PM, Formal analysis: LL, LT, Investigation: MB, IM, IM, Resources: GF, Data curation: LL, MB, LT, Writing original draft preparation: GF, LT, LL, Writing-review and editing: IV, Visualization: DK, Supervision: AS, IV, Project administration: GF. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Ioannis Manolitsis.

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Conflict of interest

The authors declare no conflict of interest.

Ethical approval

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the institutional review board of Sismanogleio general hospital (Ref. No. 15178/2020).

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Tzelves, L., Lazarou, L., Feretzakis, G. et al. Using machine learning techniques to predict antimicrobial resistance in stone disease patients. World J Urol 40, 1731–1736 (2022). https://doi.org/10.1007/s00345-022-04043-x

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  • DOI: https://doi.org/10.1007/s00345-022-04043-x

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