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

International journal of statistics in medical research Pub Date : 2023-11-24 DOI:10.6000/1929-6029.2023.12.26

T. F. 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

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引用次数: 0

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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用于预测不同组别细菌耐药性的新算法

当预期细菌耐药率较高时，选择抗菌药物是一个常见的难题。在对不同抗菌药物进行检测的不等组别分离物中观察到的耐药性值可能会产生误导。这会影响推荐一种抗生素而非另一种抗生素的决定。我们在分析回顾性数据时考虑到了不等样本组的统计因素。我们收集了伊拉克巴格达 Al Alwyia 儿科教学医院疑似伤寒患儿的数据。研究时间为 2021 年 9 月至 2022 年 9 月。研究人员开发了一种新型算法，用于比较不同抗菌药物对数量不等的伤寒沙门氏菌（S. Typhi）分离物的药物敏感性。根据提出的算法，预测的耐药性值比观察到的值更有效。该算法有望帮助医院抗生素政策委员会为伤寒沙门氏菌和其他细菌分离物推荐合适的抗菌药物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International journal of statistics in medical research

CiteScore

0.40

自引率

0.00%

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