Deployment of real-time particle detection monitoring system in operating theatres for airborne contamination assessments : a methodological evaluation.

AIMS

Surgical site infections (SSIs) are a major complication of orthopaedic implant surgeries, causing patient morbidity and reduced quality of life, and represent a substantial economic burden. Current methods for monitoring airborne contamination in operating theatres (OTs) are labour-intensive and delayed, limiting timely preventive actions. Advanced real-time monitoring technologies offer potential for improving infection control in surgical settings. This study evaluated real-time monitoring for airborne contamination; two scenarios were analyzed with the developed system: 1) the use of reusable non-disposable versus disposable surgical sheets; and 2) surgical team shift changes. SSI outcomes were also evaluated in relation to elevated particle levels.

METHODS

This study was conducted in four OTs at Sahlgrenska University Hospital, Sweden. Particle counters were employed in each OT for detection of airborne contamination for continuous surveillance. SSIs leading to reoperations were extracted from national registries and integrated into the analysis.

RESULTS

The use of reusable surgical sheets significantly reduced airborne particle concentrations across all sizes (0.5, 1, 5, and 10 µm; p = 0.022, p = 0.004, p = 0.009, and p = 0.015, respectively) compared with single-use sheets. Team shift changes were associated with increased airborne particle levels for 0.5, 1, and 5.0 µm (p = 0.001, p = 0.004, and p = 0.009, respectively). While smaller particle concentrations showed no consistent association with SSIs, larger particles (10 µm) were significantly elevated in SSI cases (p = 0.005 for maximum values and p = 0.009 for mean values).

CONCLUSION

Real-time monitoring systems proved effective in identifying factors influencing airborne contamination in OTs. Notably, non-disposable sheets outperformed disposable sheets in minimizing particulate dispersion, and surgical events with team shift changes showed an increase in maximum particle levels. Although the system shows promise for infection prevention and workflow optimization, its direct impact on SSI rates requires validation in larger cohorts. Future research should focus on integrating predictive algorithms and machine-learning to enhance clinical utility and drive improvements in surgical safety.