TY - JOUR
T1 - Detection of extended-spectrum β-lactamase-producing bacteria isolated directly from urine by infrared spectroscopy and machine learning
AU - Abu-Aqil, George
AU - Suleiman, Manal
AU - Sharaha, Uraib
AU - Nesher, Lior
AU - Lapidot, Itshak
AU - Salman, Ahmad
AU - Huleihel, Mahmoud
N1 - Funding Information:
This research was supported by the ISRAEL INNOVATION AUTHORITY (grant No. 71733). We thank the SUMC bacteriology laboratory for providing the samples used in this study.
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/7/5
Y1 - 2023/7/5
N2 - Resistant bacteria have become one of the leading health threats in the last decades. Extended-spectrum β-lactamase (ESBL) producing bacteria, including Escherichia (E.) coli and Klebsiella (K.) pneumoniae (the most frequent ones), are a significant class out of all resistant infecting bacteria. Due to the widespread and ongoing development of ESBL-producing (ESBL+) resistant bacteria, many routinely used antibiotics are no longer effective against them. However, an early and reliable ESBL+ bacteria detection method will improve the efficiency of treatment and limit their spread. In this work, we investigated the capability of infrared (IR) spectroscopy based machine learning tools [principal component analysis (PCA) and Random Forest (RF) classifier] for the rapid detection of ESBL+ bacteria isolated directly from patients’ urine. For that, we examined 1881 E. coli samples (416 ESBL+ and 1465 ESBL-) and 609 K. pneumoniae samples (237 ESBL+ and 372 ESBL-). All samples were isolated directly from the urine of midstream patients. This study revealed that within 40 min of receiving the patient urine it is possible to determine the infecting bacterium as E. coli or K. pneumoniae with 95% success rate while it was possible to determine the ESBL+ E. coli and ESBL+ K. pneumoniae with 83% and 78% accuracy rates, respectively.
AB - Resistant bacteria have become one of the leading health threats in the last decades. Extended-spectrum β-lactamase (ESBL) producing bacteria, including Escherichia (E.) coli and Klebsiella (K.) pneumoniae (the most frequent ones), are a significant class out of all resistant infecting bacteria. Due to the widespread and ongoing development of ESBL-producing (ESBL+) resistant bacteria, many routinely used antibiotics are no longer effective against them. However, an early and reliable ESBL+ bacteria detection method will improve the efficiency of treatment and limit their spread. In this work, we investigated the capability of infrared (IR) spectroscopy based machine learning tools [principal component analysis (PCA) and Random Forest (RF) classifier] for the rapid detection of ESBL+ bacteria isolated directly from patients’ urine. For that, we examined 1881 E. coli samples (416 ESBL+ and 1465 ESBL-) and 609 K. pneumoniae samples (237 ESBL+ and 372 ESBL-). All samples were isolated directly from the urine of midstream patients. This study revealed that within 40 min of receiving the patient urine it is possible to determine the infecting bacterium as E. coli or K. pneumoniae with 95% success rate while it was possible to determine the ESBL+ E. coli and ESBL+ K. pneumoniae with 83% and 78% accuracy rates, respectively.
KW - Escherichia (E.) coli
KW - Extended-spectrum β-lactamase (ESBL) producing bacteria
KW - Infrared (IR) spectroscopy
KW - Klebsiella (K.) pneumonia
KW - Machine learning algorithms
KW - Resistant bacteria to antibiotics
KW - Urine tract infection (UTI)
UR - http://www.scopus.com/inward/record.url?scp=85150240824&partnerID=8YFLogxK
U2 - 10.1016/j.saa.2023.122634
DO - 10.1016/j.saa.2023.122634
M3 - Article
C2 - 36944279
AN - SCOPUS:85150240824
SN - 1386-1425
VL - 295
JO - Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
JF - Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
M1 - 122634
ER -
