A Novel Algorithm for Predicting Antimicrobial Resistance in Unequal Groups of Bacterial Isolates - Raham, Tareef Fadhil; Ali Al. Zubaidi, Haider Hussain; Olewi, Abbas Oweid; Abddul-Fatah Al-Aboosi, Aya Ahmed; Attia, Nassera; Jaleel, Senaa; Ali Ghalib Al-Naqeeb, Abdulkhaleq Abduljabbar (International Journal of Statistics in Medical Research, 2023)
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.