Clinical and Antimicrobial Resistance Patterns of Acinetobacter Species: Implications For Future AI-Enabled Hospital Surveillance

Abstract

Background: Acinetobacter species has surfaced as a significant hospital pathogen and are getting significant importance due to its increasing medicine resistance. They cause outbreaks in intesive care units and health care units. This growing resistance burden highlights the need for improved surveillance approaches in hospital settings. The increasing availability of hospital microbiology and antimicrobial susceptibility data supports future AI-enabled surveillance, with machine-learning and predictive analytics helping identify emerging resistance trends and high-risk multidrug-resistant patterns.

Material and method: A cross-sectional study was conducted to determine the frequency and antibiotic resistnace pattern of Acinetobacter spp isolated from different clinical samples collected from cases admitted in various wards and intensive care units of the hospital over a period of 2 year (t November 2020 to December 2022).

Results: Out of 3715 samples, out of these, 120 (3.23%) Acinetobacter spp were isolated. These had been isolated from urine, pus, blood and from and various other body fluids. 59.68% was from general ward and 40.32% was from ICU. Samples received in Microbiology laboratory during the study period. Maximum resistance was seen to Ceftazidime 77.50%, followed by Levofloxacin 76.67%. Out of 120 Acinetobacter isolates 60.83% were multidrug resistance.

Conclusions: Acinetobacter isolates showed substantial multidrug resistance, highlighting the need for judicious antibiotic use, strict hand hygiene, and effective infection-control practices. These resistance patterns may also support future AI-enabled surveillance, machine-learning and data-mining approaches for early warning and antimicrobial stewardship.