Background: Airborne microorganisms in operating rooms (ORs) pose significant risks for surgical site infections (SSIs), leading to prolonged hospital stays and increased healthcare costs. Traditional microbial detection methods often fail to provide timely results, limiting prompt intervention. Rapid detection technologies have emerged as potential solutions for immediate airborne pathogen monitoring and improved infection control. Objective: This study aimed to evaluate the clinical efficacy and practical utility of rapid airborne microbial detection technology in OR settings, specifically investigating its effects on response times, postoperative infection rates, staff workload, and fatigue. Methods: A total of 84 patients scheduled for elective hemorrhoidectomy at a tertiary hospital were randomized into experimental (n = 42) and control groups (n = 42) using computer-generated block randomization with allocation concealment via sealed opaque envelopes. The experimental group employed the AirSamplR-2000 Bioaerosol Sensor (Model XR-200, AirTech Innovations, USA), providing real-time microbial alerts, while the control group utilized conventional air sampling with delayed microbial culture results. Baseline and postoperative fatigue levels were measured immediately before and after procedures using the Likert fatigue scale. Staff workload was assessed post-procedure with the NASA Task Load Index (NASA-TLX). Response times, postoperative infection rates, and subjective measures were statistically analyzed with independent t-tests and Chi-square tests, with significance defined as p < 0.05. Results: The experimental group exhibited significantly faster response times to microbial contamination alerts compared to the control group (3.1 ± 0.6 vs. 4.5 ± 0.9 seconds; p < 0.01). Despite improved response efficiency, postoperative infection rates were not significantly different between groups (7.1% vs. 11.9%; p > 0.05). Staff in the experimental group reported significantly lower workload (NASA-TLX: 52.3 ± 10.5 vs. 68.7 ± 9.2) and fatigue scores (Likert scale: 2.8 ± 0.7 vs. 4.2 ± 1.0; both p < 0.01) after procedures, adjusting for baseline fatigue. Conclusion: Rapid detection technology for airborne microorganisms significantly improved response efficiency and reduced occupational fatigue among healthcare staff, although it did not result in statistically significant reductions in postoperative infection rates. Given the operational advantages and enhanced staff well-being, broader
References
[1]
Koppolu, S., Mittal, G., Pascal, S., Variya Takodara, Y. and Singh, A.A. (2024) Factors Influencing Surgical Site Infections in a Tertiary Care Hospital: A Prospective Analysis. Cureus, 16, e72767. https://doi.org/10.7759/cureus.72767
[2]
Khodaparast, M., Sharley, D., Marshall, S. and Beddoe, T. (2024) Advances in Point-of-Care and Molecular Techniques to Detect Waterborne Pathogens. NPJ Clean Water, 7, Article No. 74. https://doi.org/10.1038/s41545-024-00368-9
[3]
Chiappa, F., Frascella, B., Vigezzi, G.P., Moro, M., Diamanti, L., Gentile, L., et al. (2021) The Efficacy of Ultraviolet Light-Emitting Technology against Coronaviruses: A Systematic Review. Journal of Hospital Infection, 114, 63-78. https://doi.org/10.1016/j.jhin.2021.05.005
[4]
Kohanski, M.A., Lo, L.J. and Waring, M.S. (2020) Review of Indoor Aerosol Generation, Transport, and Control in the Context of COVID‐19. International Forum of Allergy & Rhinology, 10, 1173-1179. https://doi.org/10.1002/alr.22661
[5]
Dhillon, R.S., Rowin, W.A., Humphries, R.S., Kevin, K., Ward, J.D., Phan, T.D., et al. (2020) Aerosolisation during Tracheal Intubation and Extubation in an Operating Theatre Setting. Anaesthesia, 76, 182-188. https://doi.org/10.1111/anae.15301
[6]
Tang, J.W., Li, Y., Eames, I., Chan, P.K.S. and Ridgway, G.L. (2006) Factors Involved in the Aerosol Transmission of Infection and Control of Ventilation in Healthcare Premises. Journal of Hospital Infection, 64, 100-114. https://doi.org/10.1016/j.jhin.2006.05.022
[7]
Harte, J.A. (2010) Standard and Transmission-Based Precautions: An Update for Dentistry. The Journal of the American Dental Association, 141, 572-581. https://doi.org/10.14219/jada.archive.2010.0232
[8]
Sajjad, B., Hussain, S., Rasool, K., Hassan, M. and Almomani, F. (2023) Comprehensive Insights into Advances in Ambient Bioaerosols Sampling, Analysis and Factors Influencing Bioaerosols Composition. Environmental Pollution, 336, Article ID: 122473. https://doi.org/10.1016/j.envpol.2023.122473
[9]
Essamlali, I., Nhaila, H. and El Khaili, M. (2024) Advances in Machine Learning and IoT for Water Quality Monitoring: A Comprehensive Review. Heliyon, 10, e27920. https://doi.org/10.1016/j.heliyon.2024.e27920
[10]
Nagaraj, S., Chandrasingh, S., Jose, S., Sofia, B., Sampath, S., Krishna, B., et al. (2022) Effectiveness of a Novel, Non-Intrusive, Continuous-Use Air Decontamination Technology to Reduce Microbial Contamination in Clinical Settings: A Multi-Centric Study. Journal of Hospital Infection, 123, 15-22. https://doi.org/10.1016/j.jhin.2022.02.002
[11]
Feng, X., Hu, P., Jin, T., Fang, J., Tang, F., Jiang, H., et al. (2024) On-Site Monitoring of Airborne Pathogens: Recent Advances in Bioaerosol Collection and Rapid Detection. Aerobiologia, 40, 303-341. https://doi.org/10.1007/s10453-024-09824-y
[12]
Branch-Elliman, W., Sundermann, A.J., Wiens, J. and Shenoy, E.S. (2023) The Future of Automated Infection Detection: Innovation to Transform Practice (Part III/III). Antimicrobial Stewardship & Healthcare Epidemiology, 3, e26. https://doi.org/10.1017/ash.2022.333
[13]
Zhao, L., Wang, J., Sun, X.X., Wang, J., Chen, Z., Xu, X., et al. (2021) Development and Evaluation of the Rapid and Sensitive RPA Assays for Specific Detection of Salmonella spp. in Food Samples. Frontiers in Cellular and Infection Microbiology, 11, Article ID: 631921. https://doi.org/10.3389/fcimb.2021.631921
[14]
Seidelman, J.L., Mantyh, C.R. and Anderson, D.J. (2023) Surgical Site Infection Prevention: A Review. JAMA, 329, 244-252. https://doi.org/10.1001/jama.2022.24075
[15]
Olatunji, A.O., et al. (2024) Revolutionizing Infectious Disease Management in Low-Resource Settings: The Impact of Rapid Diagnostic Technologies and Portable Devices. International Journal of Applied Research in Social Sciences, 6, 1417-1432. https://doi.org/10.51594/ijarss.v6i7.1332
[16]
Alowais, S.A., Alghamdi, S.S., Alsuhebany, N., Alqahtani, T., Alshaya, A.I., Almohareb, S.N., et al. (2023) Revolutionizing Healthcare: The Role of Artificial Intelligence in Clinical Practice. BMC Medical Education, 23, Article No. 689. https://doi.org/10.1186/s12909-023-04698-z
[17]
Tubbs-Cooley, H.L., Mara, C.A., Carle, A.C. and Gurses, A.P. (2018) The NASA Task Load Index as a Measure of Overall Workload among Neonatal, Paediatric and Adult Intensive Care Nurses. Intensive and Critical Care Nursing, 46, 64-69. https://doi.org/10.1016/j.iccn.2018.01.004
[18]
Chotinaiwattarakul, W., O'Brien, L.M., Fan, L. and Chervin, R.D. (2009) Fatigue, Tiredness, and Lack of Energy Improve with Treatment for Osa. Journal of Clinical Sleep Medicine, 5, 222-227. https://doi.org/10.5664/jcsm.27490
[19]
Sankurantripati, S. and Duchaine, F. (2024) Indoor Air Quality Control for Airborne Diseases: A Review on Portable UV Air Purifiers. Fluids, 9, Article No. 281. https://doi.org/10.3390/fluids9120281
[20]
Haque, M., McKimm, J., Sartelli, M., Dhingra, S., Labricciosa, F.M., Islam, S., et al. (2020) Strategies to Prevent Healthcare-Associated Infections: A Narrative Overview. Risk Management and Healthcare Policy, 13, 1765-1780. https://doi.org/10.2147/rmhp.s269315
[21]
Ahuja, S., Peiffer-Smadja, N., Peven, K., White, M., Leather, A.J.M., Singh, S., et al. (2021) Use of Feedback Data to Reduce Surgical Site Infections and Optimize Antibiotic Use in Surgery: A Systematic Scoping Review. Annals of Surgery, 275, e345-e352. https://doi.org/10.1097/sla.0000000000004909
