Evaluation of novel disinfection methods for the remediation of heavily contaminated thermostatic mixing valves and water systems with Pseudomonas aeruginosa biofilm: considerations for new and existing healthcare water systems

Background: Pseudomonas aeruginosa is a leading cause of nosocomial Gram-negative bacteraemia. Water systems are a well-documented source of P. aeruginosa and established biofilms are difficult to remove. Aim: To evaluate the efficacy of regular flushing, peracetic acid disinfection, in-tap thermal disinfection, and in-line thermal disinfection to eradicate P. aeruginosa biofilm in a colonized tap model. Methods: A simulated tap system was constructed and inoculated with a reference and an environmental strain of P. aeruginosa to form biofilm. Water samples were collected from the taps and P. aeruginosa levels enumerated following disinfection methods. To simulate regular flushing, taps were flushed for 5 min, five times per day with water tested daily. Peracetic acid (4000 ppm) was manually injected into the system and flushed through the system with a pump. Thermal flushing at 60 degrees C was performed in-line and with an in-tap bypass valve. Tests were conducted with cross-linked polyethylene (PEX) piping and repeated with copper piping. Findings: Regular flushing and peracetic acid applied with a pump did not reduce P. aeruginosa levels. A limited reduction was observed when manually injecting peracetic acid. In-tap thermal flushing eradicated P. aeruginosa in copper piping but not PEX. In-line thermal flushing was the most effective at reducing P. aeruginosa levels; however, it did not eradicate the biofilm. Conclusion: In-line thermal flushing was the most effective method to remove P. aeruginosa biofilm. Results vary significantly with the strain of bacteria and the composition of the plumbing. Several methods used in combination may be necessary to remove established biofilm. (c) 2024 The Healthcare Infection Society. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.