Surgical site infections, air-borne transmission and ventilation
Surgical-site infections (SSIs) are serious and contribute to higher rates of patient morbidity and mortality, increased hospitalization time, and patient dissatisfaction[
1]. Infections after hip- and knee-prosthetic surgery are devastating. Several measures must be taken to reduce the infection rate[
2].
It is well-known that operating room (OR) personnel are the main source of airborne bacteria as they disseminate infectious particles into their surrounding environment. A person releases about 10
4 skin scales per minute during walking, 10 percent of which carry bacteria[
3]. However, the count of discharged microorganisms varies widely, even as much as 12-fold, between individuals and sampling days[
4]. The size of the particles carrying microorganisms has been reported ranging from 4–60 μm[
5,
6]. Bacteria suspended in the OR air may contaminate the surgical wound, either by direct sedimentation from the air or indirectly by contaminated surgical instruments[
7].
Air contamination can be reduced with an efficient ventilation system to dilute and evacuate contaminants from the OR[
8], increasing the performance of staff clothing to prevent bacteria shedding to the air[
9], and restricting the number of people and their activity in the OR[
10,
11].
Laminar airflow (LAF) is the most efficient OR ventilation system[
8,
12]. However, indoor obstacles including medical lamps, surgical staff, and equipment can easily affect the unidirectional airflow pattern of a vertical LAF system[
13,
14]. Intended colony-forming unit (CFU) levels (<10 CFU/m
3) will not be present, and the desired SSI-rate decrease cannot be achieved. Installing a LAF ventilation system might also be difficult in existing ORs and is costly when constructing new ORs. A mobile laminar airflow (MLAF) unit could overcome both the problem with physical obstacles and costs.
A MLAF unit with conventional turbulent-mixing ventilation is a valuable complement to general ventilation in reducing bacterial load during operations in an OR[
15‐
19]. The authors conclude that the additional MLAF screen reduced the number of viable airborne bacteria and sedimenting, bacteria-carrying particles (BCPs) to the same level as ultra-clean LAF-ventilation.
Microbiological air-sampling methods
Currently, microbiological air sampling in ORs is performed either by passive air sampling (PAS) with settle plates or by active air sampling (AAS) with a slit sampler, impaction sampler, or filter sampler[
20]. PAS measures the settlement rate of viable particles on surfaces, while AAS provides information about the concentration of viable particles in the air. Both methods require access to a bacteriological laboratory and can only be used in existing ORs. Sadrizadeh et al. provides a more detailed explanation of the mathematical modeling of active and air-sampling methods[
10].
Computational fluid dynamics
It is important to control air distribution in enclosed spaces to create and maintain a comfortable, healthy atmosphere for occupants, especially in sensitive indoor environments such as ORs. Experimental studies provide direct evidence of airflow and particle-transport phenomena. However, the complexity of indoor airflow makes experimental investigation very difficult and expensive.
With recent advances in computer technology in various methods, computational fluid dynamics (CFD) has become an essential complementary tool to physical experiments. CFD is the science of predicting fluid or gas flow, which largely reduces the number of required physical experiments and provide great potential for improving prediction accuracy of air distribution in enclosed environments. This method was successfully applied in simulated OR environments[
10,
13,
21]. The data made it possible to clarify uncertainties at initial stages of design. Obtaining precise information can provide an important foundation on which to base design decisions. It can also overcome some measurement limitations and extend the range of research. Generally, the CFD technique has three main steps.
Pre-processing: This step consists of defining simulation domain and grid generation. The domain in which flow is to be analyzed requires modeling, generally with a CAD software package. For the complex geometries, some degree of simplification may be required to correct the geometry and make it valid as a CFD model. Portions of the flow-domain boundary coincide with the surfaces of the body geometry. The spatial geometric spaces the fluid occupies are modeled so as to provide input for grid generation. Mesh generation is essential in the CFD analysis process, which subdivides the domain into discrete cells, known as grid or mesh. The created mesh surrounds the object and then extends in all directions to get the physical properties of the surrounding fluid; in other words, the OR air in the present study. The mesh is very fine in areas with large gradients in the flow field and coarser in regions with relatively little change.
Boundary condition and solve: Numerical simulation generally requires input parameters consisted of the desired strategy. The boundary conditions are specified as the fluid properties and behavior at the boundaries of the problem, inlet temperature and velocity, and particle generation rate. The numerical solution is obtained by an iterative method, which achieves high accuracy using a large number of repetitions. As the simulation proceeds, the solution is monitored to determine if a converged solution has been obtained.
Post-processing: This stage involves extracting the desired flow properties (velocity, particle concentration, temperature) from the computed flow field. This is accomplished by means of contour and color plots, vector plots, and animation for dynamic result.
Result sensitivity should be examined to understand possible differences in the accuracy of results and with respect to initial flow conditions and experimental investigation.