The development and validation of a novel and quantitative 'Kremer' cleaning classification for reusable medical devices

Abstract

For over half a century, Spaulding's microbial reduction categorization has been a guiding principle in the healthcare sector outlining necessary measures to safeguard patient safety for reusable medical devices. However, this classification system operates under an unreliable assumption that medical devices are initially clean before undergoing disinfection or sterilization procedures. This is supported by concerns regarding hospital-acquired infections (HAIs) originating from contaminated devices such as intricate endoscopes and robotic instruments. Despite medical device manufacturers validating their cleaning instructions, best-published literature highlights inconsistent adherence to effective device processing protocols within clinical settings leading to heightened risks to patient safety. Thus, the overarching aim of this novel study is to develop, test, and validate a new cleaning classification system (designated ‘Kremer’) for appropriate and effective standardized cleaning of reusable medical devices globally focusing on complex device features as a key challenge linked to patient risk.

Novel methods are developed and applied underpinning this simplified Kremer cleaning categorization system. An extensive suite of key device design features was evaluated (n = 23) for residual soils during cleaning validation to ensure their safety for human use. This risk-based approach evaluates the likelihood of residual soil remaining on or within different design features of a device after cleaning. For effective cleaning, the cleaning chemistry (comprising cleaning agent and water) must sufficiently access the soil, either through exposure (such as spraying or soaking) or force (like brushing, flushing, or sonication), to dissolve and remove it from the surface. The ‘device feature’ becomes a crucial variable influencing this relationship. Moreover, by focusing on the hardest to clean feature of the reusable medical device, the overall cleaning challenge can be established for the entire device. This more conservative approach for validation/verification of cleaning practices allows for the design of cleaning processes that are robust to quantify the risk of patient safety. By simplifying and streamlining classification criteria, users can swiftly assign items or concepts to specific categories, reducing complexity and the likelihood of errors. This simplicity expedites the categorization process, enhances clarity, and lessens cognitive load enabling users to make decisions based on their understanding of device's complexity. This cleaning classification proposes three risk categories: maximal, moderate, and minimal. Twenty-three of the most intricate device features were identified and rigorously tested in this study until they failed to clean effectively. Across the 150 experiments carried out (encompassing ca. 56,000 extractions/flushes for device feature validation and 2,695 individual analyte measurements for the 23 features experiment), each feature underwent evaluation concerning its impact on cleaning that considers geometry, material of construction, probability of soil drying, and fluid dynamics. Among these, the risk of soil drying emerged as the most crucial validation variable. Consequently, soil drying time and soil configuration were manipulated to adjust the cleaning challenge for the features. Manual cleaning, being the most variable method, served as the standardized cleaning approach. However, to explore the potential for excluding manual cleaning from the process, a semi-automated cleaning method was also tested to ascertain the feasibility of automation within the cleaning process. The results of cleaning validation for protein residuals were categorized into risk levels based on acceptance criteria outlined in ISO 15883-5.

The device feature categorization serves as the foundational element for risk assessment, but it is essential to also evaluate compound risks involving device geometry and material of construction. Compound risk occurs when multiple manageable risk factors converge or interact, creating a more complex level of risk. In the context of cleaning reusable medical devices, compound risk arises when various factors combine to make the cleaning process more challenging. This includes factors like complex device design, intricate components, and hard-to-reach areas. When these factors compound, they significantly increase the risk of incomplete cleaning, potentially endangering patient safety. As such, thirteen core topics were addressed using the risk assessment for medical devices outlined in ISO 14971 to quantify the compounding risks and sort reusable medical devices into the ‘Kremer’ cleaning classification for communicating device design risks across the entire device processing cycle. Medical device manufacturers can utilize this classification alongside Spaulding’s antimicrobial criteria to evaluate risks associated with the entire processing cycle for reusable medical devices. For the first time, this integration can enhance validation methods for cleaning, disinfection, and sterilization, improve device design, and ensure effective risk communication and mitigation at healthcare facilities. The well-established Spaulding Classification, focusing on disinfection, sterilization, and patient risk, serves as a convenient means to link manufacturers and healthcare facilities regarding device validation and processing requirements.

The benefits of the conclusions from this novel research extend widely. In addition to completing ten peer-reviewed publications to disseminate the acquired knowledge, it is anticipated that future application of this cleaning classification will yield several advantages. These include enhancing the economics of processing reusable medical devices, fostering trust in sustainability practices related to device reuse, diminishing the occurrence of hospital-acquired infections (HAIs), and guiding the development of future device processing methods, including automation and machine learning. For example, a proposal for a new draft work item (NWI) for industry titled "ISO-NP TS 17664-3, Processing of healthcare products – Information to be provided by the medical device manufacturer for the processing of medical devices – Part 3: Guidance on the designation of a reusable medical device" was accepted by the ISO/TC 198 committee as part of an initiative with Kremer's cleaning classification. This endorsement by an international assembly of experts highlights the practicality and relevance of the classification to the healthcare industry. The introduction of this ISO document is expected to promote the adoption of the cleaning classification in various global guidance and standard documents, establishing it as a valuable tool for risk reduction in healthcare.