A Novel Algorithm for Predicting Antimicrobial Resistance in Unequal Groups of Bacterial Isolates

Tareef Fadhil  Raham; Haider Hussain  Ali Al. Zubaidi; Abbas Oweid  Olewi; Aya Ahmed  Abddul-Fatah Al-Aboosi; Nassera  Attia; Senaa  Jaleel; Abdulkhaleq Abduljabbar  Ali Ghalib Al-Naqeeb

doi:10.6000/1929-6029.2023.12.26

Authors

Tareef Fadhil Raham Department of Pediatrics, Al-Alwyia Teaching Pediatric Hospital, Baghdad, Iraq https://orcid.org/0000-0001-5245-6753
Haider Hussain Ali Al. Zubaidi Department of Pediatrics, Al-Alwyia Teaching Pediatric Hospital, Baghdad, Iraq
Abbas Oweid Olewi Department of Pediatrics, AL-Kindy College of Medicine, University of Baghdad, Iraq
Aya Ahmed Abddul-Fatah Al-Aboosi Department of Laboratory, Al-Alwyia Teaching Pediatric Hospital, Baghdad, Iraq
Nassera Attia Department of Laboratory, Al-Alwyia Teaching Pediatric Hospital, Baghdad, Iraq
Senaa Jaleel Department of Laboratory, Al-Alwyia Teaching Pediatric Hospital, Baghdad, Iraq
Abdulkhaleq Abduljabbar Ali Ghalib Al-Naqeeb Medical & Health Technology College, Baghdad-Iraq

DOI:

https://doi.org/10.6000/1929-6029.2023.12.26

Keywords:

Unequal groups, ranking, salmonella drug resistant, predicted value, observed value

Abstract

Choosing antimicrobials is a common dilemma when the expected rate of bacterial resistance is high. The observed resistance values in unequal groups of isolates tested for different antimicrobials can be misleading. This can affect the decision to recommend one antibiotic over the other. We analyzed recalled data with the statistical consideration of unequal sample groups.

Data was collected concerning children suspected to have typhoid fever at Al Alwyia Pediatric Teaching Hospital in Baghdad, Iraq. The study period extended from September 2021 to September 2022. A novel algorithm was developed to compare the drug sensitivity among unequal numbers of Salmonella typhi (S. Typhi) isolates tested with different antibacterials.

According to the proposed algorithm, the predicted resistance values were more valid than the observed values. This proposed algorithm is expected to help the hospital antibiotic policy committee recommend the proper antibacterial agents for S. Typhi and further bacterial isolates.

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