Impact of Microbial Load on Operating Room Air Quality and Surgical Site Infections: A Systematic Review

This is the authors' abstract. We don't add key points for this paper.

Surgical site infections (SSIs) are one of the most common causes of hospital-acquired infections worldwide, with significant clinical and economic implications. The aim of this review was to summarize the latest body of evidence on associations between microbial air load and SSIs. The systematic review was conducted using the revised Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA, 2020) method. Pubmed and Scopus databases were searched for the period 2014–2024. English language articles were searched for their reports on the microbial burden of operating room air and its association with surgical site infections. The present review includes a total of 36 articles related to microbial air load as an aggravating factor to air quality in the operating room and its association with SSIs. A direct correlation between microbial air load and the occurrence of SSIs was established through sampling methods and genetic analysis. A lack of consensus on the effectiveness of laminar air flow (LAF) systems was underlined, while temperature-controlled air flow seemed a promising alternative. One study found that each additional person in the operating room increases the number of bacterial colonies by 4.93 CFU/m3 while another did not find significant changes in air quality. More than 20 air changes per hour (ACH) appeared to have better results in improving the quality of the air in the operation room. Airborne microbial contamination is multifactorial, and for some of those factors, a revision of the guidelines seems necessary. Artificial Intelligence (AI) and Next-Generation Sequencing methods show great promise for improving air quality in the future. This review calls for the implementation of international guidelines regarding air contamination limits in operating rooms and standardized air sampling methods, as well as further research for the efficacy assessment of air flow systems and emerging technologies based on AI in order to reduce the burden of SSIs and improve patient outcomes.