Chenyao Lin,1 Hui Zhang,2 Qiaoping Wu,1 Xuedan Qiu,1 Qingcao Li,1 Guangliang Wu3 

1Department of Clinical Laboratory, The Affiliated LiHuiLi Hospital of Ningbo University, Ningbo, People’s Republic of China; 2Department of Clinical Laboratory, Ninghai County Chengguan Hospital, Ningbo, People’s Republic of China; 3Department of Clinical Pharmacy, The Affiliated LiHuiLi Hospital of Ningbo University, Ningbo, People’s Republic of China

Correspondence: Qingcao Li, Department of Clinical Laboratory, The Affiliated LiHuiLi Hospital of Ningbo University, 57 Xingning Road, Ningbo, Zhejiang, 315000, People’s Republic of China, Tel +86 139-8931-6035, Email lqc_lab@163.com Guangliang Wu, Department of Clinical Laboratory, The Affiliated LiHuiLi Hospital of Ningbo University, 57 Xingning Road, Ningbo, Zhejiang, 315000, People’s Republic of China, Tel +86 137-7709-1293, Email Lwuguang0313@163.com

Background: The emergence of multidrug-resistant bacteria limits antibiotic efficacy, making accurate antimicrobial susceptibility testing (AST) essential for clinical decisions. Broth microdilution (BMD) is the gold standard but is impractical for routine application. Current automated AST systems improve efficiency but face accuracy or operational challenges, highlighting the need for reliable and user-friendly solutions.
Objective: This study aims to evaluate the performance of a novel automated AST system (AutoMic-i600) based on the BMD method for AST of common clinical bacteria.
Methods: A total of 229 clinical isolates (150 Gram-negative and 79 Gram-positive) were prospectively collected from microbiology laboratory between June 2023 and August 2023. We reported the comparison of the AutoMic-i600 and Vitek 2 systems for routine antibiotics, and also validated the detection performance of AutoMic-i600 for novel antibiotics, based on the BMD method.
Results: The overall essential agreement (EA) and categorical agreement (CA) between AutoMic-i600 and BMD were 93.2% and 93.5% for Gram-negative bacteria and 98.5% and 97.8% for Gram-positive bacteria, respectively. The overall EA and CA between Vitek 2 and BMD were 92.6% and 93.5% for Gram-negative bacteria and 97.9% and 97.4% for Gram-positive bacteria. Importantly, for drug-resistant bacteria, AutoMic-i600 demonstrated a higher overall agreement than Vitek 2 (EA: 98.1% vs 94.8%, CA: 97.5% vs 92.0%), especially in Gram-negative bacteria (EA: 97.7% vs 93.5%, CA: 97.7% vs 89.3%). The VME rate for Gram-negative bacteria using AutoMic-i600 was significantly lower than that of Vitek 2 (1.0% vs 2.9%). Novel antibiotics detected by AutoMic-i600 exhibited EA and CA rates exceeding 90.0%.
Conclusion: Based on these findings, we recommend that the AutoMic-i600 system could be a new option for routine AST testing in a clinical setting. Particularly for drug-resistant bacteria and novel antibiotics, detection with AutoMic-i600 may be more reliable, which could further contribute to the prevention and treatment of drug-resistant bacteria.

Keywords: antibiotic susceptibility testing, drug-resistant bacteria, AutoMic-i600 system, automate