INFECTION PREVENTION & CONTROL
or sterilisation process. All records associated with the sterilisation process itself must be retained for reference purposes, including steriliser planned preventive maintenance (PPM) and breakdown records, physical parameter monitoring records, biological and chemical indicator results, as well as protocols and their validation. Regulatory agencies and/or notified bodies would expect to scrutinise all of these results, along with the records of any ancillary processes such as cleaning and microbiological monitoring of associated controlled environmental areas, equipment maintenance and calibration, personnel training and qualification, and control over the packaging, labelling, wrapping, handling and storage of sterile items.
Independent monitoring
When parametric release is used, due to the increased reliance on this parametric data, it is usual to have process monitoring sensors that are independent of the control function. These independent sensors may be placed in the same location as those that control the process, or may be placed in additional locations that have been determined as being representative of the actual loads being processed, through process validation studies, although with wrapped goods being processed, locating these sensors within loads is not practical.
The independent sensors will typically have limiting values (process tolerances) that were determined during validation – operational qualification and performance qualification, although particularly the latter,
where a knowledge of the relationship of the load parameters to these sensor outputs will be generated. Depending upon which sterilisation processes are used, an understanding of one variable, such as pressure, can be derived from a different variable, such as sterilising agent concentration or temperature. Biological indicators and chemical indicators are often used to supplement the parametric data that is generated. Typically, all sensor results, together with biological and chemical indicator results, should be in specification for the process to be considered in a state of control and that the load can be released. One area that requires some discussion is what is explicitly meant by the term ‘independent’; as noted above, there is generally little contention with the use of separate sensors for the control and monitoring functions, however how the separate signals are processed is where there needs to be more understanding. In an ‘extreme independence’ scenario, there will be completely separate analogue to digital converters and completely separate signal processing for each sensor, with the result available on a dedicated display. The opposite scenario could use common signal processing and a common display. Before a conclusion is made that the ‘extreme independence’ is better, it is important to note that there are also some significant disadvantages to this approach; these disadvantages are unfortunately of fundamental importance. If we consider the example of a steriliser that is fitted with the ‘extreme independence’ monitoring, there
would be a display showing the independent data; but as this data is by definition completely divorced from the control data, it is not possible to transfer and synchronise this data into a common system, hence it does not have salient information such as steriliser cycle number, operator information, load contents etc. But there is an even bigger drawback to this scenario; combined data processing has the benefit of being able to compare the control data with the monitoring data in real time, and generating an alarm if the two data values diverge by more than a given amount.
This ‘watchdog’ approach allows users to quickly identify any out of tolerance values based on alarms, rather than having to search through potentially pages of digital data. Clearly, both extremes of the above scenarios have significant disadvantages, however a happy medium can be generated by allowing some communication between the data streams but ensuring that separate data processing is used for them. Another interesting approach to data independence is how different process variables can be used to cross-check each data set; for example, steam sterilisers use steam under pressure to sterilise devices. This saturated steam has a defined and predictable relationship with the two process variables of pressure and temperature. If the pressure is increased, this will increase the temperature by an amount proportional to the pressure increase. And so, for a given temperature, the pressure is expected. If a large amount of air is trapped within the steriliser, it will alter the pressure- temperature relationship (according to Dalton’s Law) that can then be detected by the system. Although small amounts of air cannot be detected accurately by this method, the principle of controlling one process variable and monitoring it by a different process variable is better than controlling and monitoring the same process variable. In practical terms, the pressure of the steam can be set and controlled, yet it is the temperature variable that is monitored; this allows for the system to monitor the correlation between these process variables and provide alarms if the correlation exceeds pre-determined values.
Conclusion
Release of loads as disinfected or sterilised can be achieved with a high degree of safety by a holistic system of parametric release that includes independent monitoring of the key process variables. With the advent of very rapid-read biological indicators, loads can be released after biological indicator incubation in as little as 20 minutes. Use of biological monitoring does not preclude the use of independent monitoring of the process, and in fact adds to the assurance of the entire process. The current state of the art would embody these concepts, and ideally presents all of this salient information to the operator or process owner in an electronic system that will record and archive all of these inputs. CSJ
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