Validation of the Device Feature Approach for Reusable Medical Device Cleaning Evaluations

Abstract

The identification of worst-case device (or device set) features has been a well-established validation approach in many areas (e.g., terminal sterilization) for determining process effectiveness and requirements, including for reusable medical devices. A device feature approach for cleaning validations has many advantages, representing a more conservative approach compared with the alternative compendial method of testing the entirety of the device. By focusing on the device feature(s), the most challenging validation variables can be isolated to and studied at the most difficult-to-clean feature(s). The device feature approach can be used to develop a design feature database that can be used to design and validate device cleanliness. It can also be used to commensurately develop a quantitative cleaning classification system that will augment and innovate the effectiveness of the Spaulding classification for microbial risk reduction. The current study investigated this validation approach to verify the efficacy of device cleaning procedures and mitigate patient risk. This feature categorization approach will help to close the existing patient safety gap at the important interface between device manufacturers and healthcare facilities for the effective and reliable processing of reusable medical devices. A total of 56,000 flushes of the device features were conducted, highlighting the rigor associated with the validation. Generating information from design features as a critical control point for cleaning and microbiological quality will inform future digital transformation of the medical device industry and healthcare delivery, including automation.

This study sought to validate a device feature approach to be used in cleaning validations for reusable medical devices.

Reusable medical devices are required to be cleaned, disinfected, and/or sterilized between patient exposure and can include those used directly on a patient during surgery or items that have minimal patient contact (e.g., blood pressure cuffs) or contact surfaces indirectly (e.g., monitor or piece of equipment).¹ Medical devices that are not properly cleaned have demonstrated an increased challenge for disinfection/sterilization and can lead to transmission of infectious disease.²

To comply with international standards, manufacturers of medical devices for which processing is required prior to patient use must provide validated processing instructions to the customer. These instructions for use (IFUs) are used to process medical devices in a healthcare facility so they are safe and functional for subsequent patient use.³ Manufacturers validate cleaning IFUs by developing a test system that challenges each step of the cleaning process with worst-case conditions. The process steps for the cleaning IFUs, which must be defined, may include initial treatment at point of use (pretreatment), preparation before cleaning (e.g., disassembly), manual and/or automated cleaning, rinsing, drying, and visual inspection.¹

During the cleaning validation, the following test conditions are selected to mitigate human factors that may affect the efficacy of the cleaning process within a healthcare setting³:

• Device conditioning (i.e., simulated use): Repetition of processing prior to validation to place the device in a used state and account for soil accumulation.³

• Soil formulation: The formulation of the of the test soil (i.e., substitute for a contaminate found on a device after clinical use³) should be representative of the clinical soil expected during use and validated.⁴,⁵

• Soil volume: The soil volume must be sufficient to adequately challenge the cleaning of the device as it would be used in a clinical setting and be representative of the clinically relevant analyte concentrations.³

• Soiling location: The most challenging areas of the device to clean, including areas of test soil and fluid ingress accumulation.⁶ Identifying these areas is of critical importance.

• Soil application: The method of applying the test soil should be representative of the most challenging (worst-case) clinical use.⁷

• Device articulation: Physical manipulations (e.g., actuations, flexures) of the device during soiling, thereby allowing for soil migration.³

• Soil conditioning/drying: Conditions during use (e.g., heating) that can make the test soil more difficult to remove should be mimicked, and the length of time and environmental conditions related to the drying of test soil on the device should represent the most appropriate challenge.⁸

In addition to the preparation of the test samples, the cleaning parameters should also be selected to simulate the most challenging cleaning conditions. If the cleaning IFUs provide a range of processing parameters, the validation should be completed at practical worst-case parameters.³ Examples include:

• Detergent preparation: If the IFU states to dilute the detergent to obtain a concentration range, then the weakest detergent concentration should be used.

• Flushing: If the device is to be flushed for a specific time or until visibly clean, then the indicator of visual cleanliness should be specified in the validation, including use of a timing element.

• Soaking: If a device is intended to be submersed for a specified time range (e.g., soak for 5–10 min), then the time selected should be the most rigorous (e.g., 5-min soak selected for validation).

• Volume: If volume of fluid is specified (e.g., flush lumen with 60 mL prepared detergent), then a volume slightly below the volume specified should be used in the validation (e.g., 59 mL detergent used in the validation procedure).

• Temperature: If a temperature is specified, the condition representing the most rigorous challenge would be selected (e.g., devices specified to be cleaned in a cleaning chemistry at a specific temperature [45°C ± 5°C]), then the most extreme condition should be selected (e.g., 40°C, as it is below the optimal temperature for enzyme performance).

By stacking the most challenging cleaning validation parameters, a more robust validation of the cleaning process can be developed and inform more reliable IFUs.

Device Feature Validation Approach

ANSI/AAMI/ISO 17664-1:2022 describes methods to classify devices for validation by using a risk-based approach (e.g., the Spaulding classification) or challenging the process based on the device design.¹ AAMI TIR12:2020 provides design considerations that may pose additional challenges during the cleaning process.⁹ Michels et al.⁶ expanded on the latter method by grouping devices into the following six groups: (1) instruments without joints (cavities/ lumens), (2) instruments with joints, (3) sliding-shaft instruments, (4) tubular instruments, (5) microsurgical instruments, and (6) complex instruments. This categorization was developed by analyzing the residual protein levels obtained from cleaning validations.

The concept of specifically targeting variables related to the most challenging portion of the medical device has been used for many years in validating sterilization parameters.¹⁰ The process challenge location is defined as a “site chosen within a load as the position at which the least microbiological inactivation is expected to be delivered,” and a process challenge device is defined as an “item providing a defined resistance to a cleaning, disinfection, or sterilization process and is used to assess performance of the process.”¹¹ The cleaning method for a reusable medical device can be validated using the actual medical device or surrogate devices that are well-designed comparators.¹,³

As demonstrated by Michels et al.,⁶ underreporting of the residual soil level on devices can occur if the validation method is not focused on the most difficult-to-clean area of the device, which also represents the greatest risk to the patient. By using the entirety of the device to evaluate cleanliness, the surface area of easy-to-clean areas may dilute the most challenging-to-clean features or process challenge location,⁶ thus underestimating appropriateness.

The design feature validation approach focuses exclusively on the device feature(s) that poses a known challenge to cleaning, without including the surface area from other exposed parts of the actual device that are not considered a challenge for cleaning. This approach also minimizes the volume of extraction fluid required, thereby optimizing the limit of quantification. The results of the feature testing then can be directly applied during the evaluation of actual devices to validate by equivalency. If the features of the candidate device are equal to or less challenging than the validated features, the candidate device can be considered validated, hence delivering a well-designed comparator.

Residual analyte concentrations on soiled devices with multiple features are expected to be equal to or less than concentrations for individual challenging features. This hypothesis is due to the increased surface area from the multiple features and the area of other, nonchallenging surfaces. Theoretically, the device feature approach can be used to isolate the most difficult-to-clean feature while considering patient risk to evaluate the entirety of the device. Although this approach has been widely accepted in similar processes for reusable medical devices, literature is lacking on mathematical approaches for cleaning validations.

Material and Methods

To investigate the device feature approach, a dead-end (i.e., blind) lumen was selected as the most challenging-to-clean feature (as demonstrated in the literature¹²,¹³). The test coupon design is shown in Figure 1. To be removed from the feature, a dead-end lumen requires a backflow of the eluent flush after it reaches the dead end. This requires competing pressure gradients in the lumen and can limit sheer force of the liquid over the surface, resulting in ineffective soil removal.¹⁴ The longer the lumen and the smaller the diameter, the more challenging this feature becomes to clean. As the diameter narrows, the competing flow of the liquid increases. The length of the lumen will require more force for the liquid to reach the dead end with enough flow velocity for the liquid to exit the lumen.

Figure 1.

Single-feature test coupon with a dead-end lumen.

The null hypothesis of this experiment was that the protein-per-surface-area relationship for a single feature would be statistically similar to that of a device with multiple features. To challenge this hypothesis, two types of coupons were used:

1. Single feature: 300 series stainless steel block (6 mm × 6 mm × 50 mm) with a 2-mm diameter hole drilled in the top center (Figure 1).

2. Multiple features: 300 series stainless steel block (30 mm × 30 mm × 50 mm) with 25 holes (2 mm each) drilled in the top (Figure 2).

Figure 2.

Multiple-feature test coupon with individual dead-end lumens.

To challenge the flow velocity of the lumen, each coupon type had three challenge lumen lengths (depth) of 20 mm, 30 mm, and 40 mm, for a total of six coupon types to be tested.

As specified in ANSI/AAMI ST98:2022,³ the surface area of the device was used to normalize the analyte concentration by applying a constant to evaluate the cleaning efficacy of the device against the established acceptance criteria (reported in μg/cm²). The single-feature-only surface area was calculated using the feature alone. For the multiple-feature coupons, the 25 features were added for a total feature surface area. The surface area for the whole device was calculated using the surface area for the entire exterior stainless-steel block and that for the feature (Table 1).

Table 1.

Surface area by coupon type.

In preparation for soiling, the devices were rinsed under running critical water¹⁵ for 1 minute while brushing the lumen with a 2.2 mm × 12 inch lumen brush (Key Surgical, Eden Prairie, MN). Devices then were immersed in a 10 mL/L concentration of alkaline cleaning agent (NeoDisher; Dr. Weigert, Hamburg, Germany), and each lumen was flushed with the cleaning agent solution using a 16.5-G needle and 3-mL syringe. Following a 60-minute soak, the lumens were again flushed with 10 mL of the detergent solution and sonicated for 15 minutes at 45 kHz in a fresh batch of the alkaline cleaning agent. Then, they were rinsed under running critical water¹⁵ and each lumen was flushed two times. The lumens were dried using medical-grade compressed air and inspected for cleanliness using a borescope.

Modified coagulated blood soil has been previously described⁵ and was identified as the most difficult-to-remove soil due to the complexity of water-soluble and -insoluble protein complexes, resulting from fibrin in the coagulated blood.⁴ The soil was prepared by mixing 100 mL fresh egg yolk (Eggland’s Best, Malvern, PA) with 100 mL sheep blood (Rockland, Royersford, PA; with 0.1 mL heparin), and 2 g dehydrated hog mucin (Sigma-Aldrich, St. Louis, MO) in a blender. The soil was stored at 4°C to 8°C and brought to room temperature prior to coagulation. The soil was poured into a bowl and mixed well with 0.05 mL of 100% powdered protamine sulphate (Thermo Scientific, Waltham, MA) for each 5 mL of blood. The soil was applied immediately and typically would coagulate in 10 to 15 minutes. The test soil was applied within 10 minutes of preparation (i.e., before coagulation) with a pipette. When depositing the test soil, the pipette tip was inserted as far as it would go into the lumen. The coupon was gently tapped on the counter to promote the migration of the test soil to the dead end of the lumen. The devices then were dried under the most challenging conditions (72 hours at 22°C/50% relative humidity).⁸

The amount of test soil used in a cleaning efficacy should be an appropriate challenge but also must be representative of soil levels following clinical use. That is, the challenge protein concentration should be equivalent to clinical protein concentration levels and quantifiable via a validated protein residual test method. For surgical devices, protein analyte levels of approximately 244 μg/cm² are representative of clinical use.¹⁶ The approximate protein concentration of modified coagulated blood soil was 108,747 μg/mL, as measured using the micro-BCA protein assay (Thermo Scientific). The minimum soil volume for the device was calculated using the following equation, resulting in the volumes shown in Table 2.

$$\text{Soil\hspace{0.17em}volume}\text{per}\text{lumen}(\text{ml})=\frac{\text{Protein\hspace{0.17em}analyte\hspace{0.17em}level}\left(\frac{\mathrm{\mu g}}{{\mathrm{cm}}^2}\right)\times \text{Lumen\hspace{0.17em}surface\hspace{0.17em}area}\left({\mathrm{cm}}^2\right)}{\text{Soil\hspace{0.17em}protein\hspace{0.17em}concentration}\left(\frac{\mathrm{\mu g}}{\mathrm{mL}}\right)}$$

Table 2.

Minimum soil volume, rounded to the nearest microliter.

The cleaning procedure began with a prerinse, where each lumen was flushed with 10 mL water and bushed five times using a 2.2 mm × 12 inch lumen brush with a twisting motion. The devices then were immersed in a 4-mL/L concentration-neutral pH cleaning agent solution (Valsure Neutral; STERIS, St. Louis, MO) at less than 40°C for 5 minutes. While immersed, a 2.2 mm × 12 inch lumen brush was used to clean all traces of test soil from the lumen and exterior surface using a twisting motion five times for a minimum of 1 minute. To rinse, the devices were immersed in critical water (<40°C) for a minimum of 1 minute. An ultrasonic bath was prepared with the neutral pH cleaning agent at a concentration of 4 mL/L. The lumens were flushed with the cleaning agent solution using a 50-mL syringe before being sonicated for 5 minutes at 40 kHz. The devices then were immersed in critical water (<40°C) for a minimum of 1 minute while the lumens were flushed with 50 mL water. The lumens in the devices were dried by flushing the lumen with air using a 16.5-G needle until no droplets exited the lumen, and the outside of the device was dried using a lint-free cloth.

To account for the water-soluble and -insoluble protein present in the test soil postdrying, an additive extraction was validated. This was performed by first extracting with high-purity water (<50 ppb total organic carbon), followed by a second extraction of 2% alkaline sodium dodecyl sulfate (SDS) at a pH 10. The alkaline SDS solution is an aggressive extraction eluent and was validated to remove the residual protein remaining on the device.¹⁷ The first and second extraction then were added together to deliver the total residual protein concentration. The validated extraction efficiencies are shown in Table 3 using a sample size of 30 coupons.

Table 3.

Protein residual results. Abbreviation used: CI, confidence interval; SD, standard deviation.

The extraction volume used within a cleaning efficacy is a critical test variable. Too little eluent can result in incomplete removal of the residual protein, whereas too much will dilute the analyte, causing detectability issues.¹⁸ To account for this situation, the limit of quantification established for the protein residual method (2.5 μg/mL) was divided into the analyte method acceptance criteria (6.4 μg/cm²) to establish the maximum extraction ratio.³ The ratio then was multiplied by the surface area to calculate the maximum extraction volume for each lumen.

$$\text{Device maximum extraction\hspace{0.17em}volume}\left(\mathrm{mL}\right)=\frac{\text{Device\hspace{0.17em}surface area}\left({\mathrm{cm}}^2\right)\times \text{Method\hspace{0.17em}acceptance\hspace{0.17em}criteria}\left(\frac{\mathrm{\mu g}}{{\mathrm{cm}}^2}\right)}{\text{Method}\mathrm{LOQ}\left(\frac{\mathrm{\mu g}}{\mathrm{mL}}\right)}$$

The resulting extraction volumes for the devices are shown in Table 3.

Cleaned devices were extracted using the validated method of flushing three times; thus, the extraction volume was divided by three to deliver the flush volume per lumen per flush (Table 3). The device was inserted into the Whirl-pak extraction bag (Nasco, Madison, WI) with the lumen to the side of the bag. Using a 16.5-G needle and 3-mL syringe, the lumen(s) were flushed. The device was oriented so that the lumen opening was completely covered by the extraction fluid, then the bag was closed and sonicated for 15 minutes at 40 kHz. Following sonication, the devices were placed so that the lumen was oriented to the bag opening and an additional flush was completed. The bag was again sealed and sonicated for an additional 15 minutes at 40 kHz. After the second sonication, the device was again flushed with extraction fluid. The extraction fluid was measured for protein residuals using the standard addition micro-BCA protein assay¹⁸ using a Spectra- Max Plus 384 UV-VIS Spectrophotometer and the Pierce BCA Protein Assay Kit (Thermo Scientific). Testing was completed with a sample size of 30 coupons, and the results were calculated with Minitab 19 using a one-sample t test and a one-way analysis of variance.

Results

A negative sample control, negative device control, positive sample control, and positive device control were evaluated within the test system to verify whether the lumen was appropriately challenged and the test system would yield accurate results.³ The results for the controls for both water and SDS extraction eluents demonstrated that each validation test system was in a state of control and that the devices were appropriately challenged.

The calculation method using the device feature approach was compared with the compendial method, which uses the surface area of the entire device (Figure 3). The results for the total lumen concentration with standard deviation and 95% confidence interval for the mean (μ) are reported in Table 4.

Figure 3.

Protein residual concentration: design feature approach versus compendial method.

Table 4.

Maximum extraction volume per coupon and validated extraction efficiency.

The data from both coupon types at 20, 30, and 40 mm was normally distributed, as demonstrated by the probability density of results. Comparing the cleaning efficacy of the devices with a 20-, 30-, and 40-mm single lumen with the results from the devices with 25 lumens (20, 30, and 40 mm) showed that the associated surface areas were statistically similar (P = 0.534 for the 20-mm devices, P = 0.925 for the 30-mm devices, and P = 0.079 for the 40 mm devices). All means, except for 40-mm devices with multiple features, were statistically similar (P = 0.368).

Discussion

When comparing the residual protein concentrations between the device feature approach and the compendial method, it is evident that the concentration of protein is diluted when using the surface area of the entire device. Using the device feature approach, only the surface area from the difficult-to-clean feature was used to calculate the extraction volume and soil application amount in the test system. The surface area then was used to calculate the residual protein level (per cm²). The compendial method used the entirety of the device, including easy-to-clean features and surfaces of the device. As such, the entire surface area of the device was included in the calculations. This dilution of the analyte in the case of the entire device can provide misleading passing results when the most challenging device feature continues to harbor residual soil in a concentration above the acceptable level at that location.

To our knowledge, this constitutes the first published study demonstrating that the device feature approach is a more conservative method with less risk for determining analyte residuals than the compendial whole-device approach. These data also demonstrated that if a device has multiple features, the challenge to the most difficult-to-clean feature (or combination of features) can be representative of the cleaning performance of the entire device with multiple features. The device feature approach therefore is a conservative approach to validating a reusable medical device.³ This approach also provides a new insight into the practicalities of addressing a complex device feature for cleaning validation. It is envisaged that the effectiveness of such an approach will be further corroborated, such as by using advanced surface imaging or specific measurement of analytes or bioindicator(s).

Next Steps

As demonstrated by comparing the results for the whole device with those for the device feature approach (Figure 3 and Table 4), the device feature approach—as a method to assess cleaning efficacy of isolated device features—can allow new devices to be assessed and validated by equivalency. For example, a device that contains multiple lumens without any other challenging features can be validated by equivalency using the results from one lumen feature validation—if the lumen feature is more challenging than the lumens found on the actual device. As the number of lumens increase, so does the surface area, keeping the amount of analyte (e.g., protein per cm²) the same. Further, the amount of analyte (per cm²) likely will decrease given the addition of any smooth surface area that is not a challenge for cleaning.

Using the device feature approach will also allow for the creation of a design feature database to be used in new device assessments and as guidance to device designers for determining which new designs will need testing or unique cleaning steps before the final assessment. Such a database would facilitate designing a device for cleanability and cleaning validations. As new, more challenging features are used in device designs, they can be further validated and represent a new master challenge feature. Devices consisting of a combination of features, and creating a more challenging scenario compared each individual feature, will be treated as a compound feature and have their own validation. Similarly, complex design features that consist of many individual components and, as a result, are difficult to assess can be isolated and validated as an individual feature.

A design feature database such as the one noted here could also be used to assess previously validated devices to establish a new cleaning classification with associated patient risk.¹⁹ By providing information regarding the device feature cleaning efficacy, location within the device, and patient exposure, this database would provide a quantitative method to establish device cleaning requirements and/or acceptable analyte residual concentrations.

The design of device categories for cleaning validations will help in reducing the risks associated with cleaning steps in device processing and allow for minimization of the cleaning process. As medical devices become increasingly complex, it is necessary to develop a body of evidence and rigorous data to give designers, device users, and regulators the scientific support needed to ensure consistency in device processing. The current Spaulding classification system is not optimal in considering the complexity of device design and risks of inadequate cleaning; therefore, a subsequent classification may be established to ensure patient safety.¹⁹

Operationally, the practical impact of the device feature approach is to assess the proposed risk to patients. Using this approach, device features can be assessed as part of risk management, thus allowing developers to design medical device for cleanability in a manner that mitigates risk during processing at healthcare facilities.

The risk assessment performed by device manufacturers must include an analysis of human factors that can lead to the inability to properly clean medical devices. Devices that are too complex may require additional mitigation steps, such as intensive training or special equipment, to fully mitigate cleaning risk when complex features are present. In addition, understanding the risk of the most challenging device feature will facilitate communication with healthcare facilities as part of the shared responsibility of ensuring the appropriate microbiological quality of reusable medical devices during execution of manufacturers’ IFUs.

Conclusion

The device feature approach is a conservative method for validating the cleaning requirements of reusable medical devices and validates the use of reliable surrogate(s) for a whole device. This method can improve the reliability of device processing by helping to facilitate a design feature database for validation, IFU development, and a newly established quantitative cleaning classification system.