In the context of the introduction of the MDR (Medical Device Regulation), the Medical Device Coordination Group (MDCG) regularly publishes recommendations, which, based on the MEDDEV documents used to date, represent recommendations for action in dealing with the implementation of the requirements of the MDR.
In March this year, the "MDCG 2020-1: Guidance on Clinical Evaluation (MDR) / Performance Evaluation (IVDR) of Medical Device Software" was published. The aim of this recommendation is to assist in determining and evaluating the adequacy of clinical data to demonstrate the safety and performance of medical device software. This applies to both the clinical evaluation of medical devices and the performance evaluation of in-vitro diagnostics.
The guidance document distinguishes three types of medical device software, with corresponding consequences for the collection and determination of clinical data:
- Software with independent intended purpose and claimed clinical benefit àclinical evaluation and performance evaluation are then only aimed at this software
- Software with a intended purpose and claimed clinical benefit in relation to another medical device for a medical purposeà clinical evaluation and performance evaluation then target both the software and the medical device
- Software driving of influencing the use of another medical device (software without its own independent intended purpose or claimed clinical benefit). àClinical evaluation and performance evaluation then target the medical device including the software (but only as a component or accessory).
The background for the determination and evaluation of adequate clinical data within the framework of the MDCG 2020-1 Guidance Document are, on the one hand, Article 61 (1) of the MDR and Article 56 (1) of the IVDR.
Just as in the case of clinical evaluations for medical devices and performance evaluations of in vitro diagnostic medical devices themselves, the process for medical device software is an ongoing process that extends over the entire life cycle of the software. The present MDCG 2020-1 refers to the same basic principles for the preparation of clinical evaluations and performance evaluations of software classified as medical devices or in vitro diagnostic medical devices, which are presented and described in the corresponding guidelines and regulatory documents. These include, but are not limited to
- Establishment and maintenance of a clinical evaluation / performance evaluation plan
- Identification of all relevant data pertaining to clinical safety and performance, and the identification and evaluation of outstanding issues in relation to these issues
- Critical evaluation of the collected data with regard to their quality and their contribution to clinical evaluation or performance evaluation
- Analysis of the available data and their relevance to demonstrate conformity with the General Safety and Performance Requirements
When compiling the clinical data / evidence of medical device software, 3 key components should be considered:
- Scientific validity: Measure of the conformity of the data output of the medical device software based on the inputs and algorithms with regard to the desired clinical application situation, purpose, indication.
Evidence may be provided by comparing existing clinical data on performance with the state of the art or by generating new clinical data on performance if a CAP analysis has shown the need for it.
- Technical / analytical performance: accuracy, reliability and precision of the medical device software in generating the desired data output based on the input data.
Here, evidence should be drawn up to the effect to provide objectively measurable results that demonstrate the compliance of the specifications of the medical device software with the desired purpose and the user needs or expectations.
- Clinical performance: ability of medical device software to provide clinically relevant data output related to the desired purpose.
In this case, the provider must prove in accordance with MDCG 2020-1 that the medical device software has been tested for the intended purpose, target groups, conditions of use, usage environments and with all user groups. Ultimately, this is proof that the user can expect clinically relevant data output when using the software.
For the evaluation of the clinical performance, data on the medical device software itself as well as on equivalent software products can be used. The presentation and justification of equivalence should follow broadly the same approach as in clinical evaluations of medical devices, i.e. agreement on verifiable (technical) characteristics, intended purpose and clinical use scenarios.
The MDCG 2020-1 also provides guidance on how to assess extent and quality of the identified clinical evidence. The question regarding extent includes aspects such as: do the clinical data provide evidence of the intended purpose, indication, contraindications, target groups, or were the clinical risks and analytical or clinical performance mapped with the data? In terms of quality, questions should be answered such as: was the collection of the data in the source adequate and do the data correspond to current scientific knowledge?
If it is concluded that existing clinical data are not sufficient to demonstrate safety and performance, the question of clinical data collection within a clinical investigation inevitably arises. This can be the case for the fundamental modification of medical device software or completely new software products. Compared to classical medical devices, there are sometimes different requirements in the conception and design of the clinical investigation / study. For example, in the case of software products that measure the efficiency of a method of treatment, a prospective study design to demonstrate the safety and performance parameters should be planned as part of the clinical evaluation / performance evaluation.
The level of evidence of the clinical data required to demonstrate safety and performance and its adequacy is determined in accordance with the MDCG 2020-1 with Article 61 (1) of the MDR and Article 56 (1) of the IVDR according to the characteristics and the claim of the software. For medical device software for which it is not possible to demonstrate conformity with the General Safety and Performance Requirements on the basis of clinical data, the manufacturer must explain in his technical documentation why, in his opinion, conformity can only be achieved on the basis of non-clinical data. The data should be based on conclusions from the risk analysis, which should also include state-of-the-art and alternative diagnostic and therapeutic treatment methods. The presentation of the results and conclusions as well as the final risk-benefit assessment must still be reflected in a clinical evaluation report.
This evaluation report shall be updated at regular intervals. This includes data from post-market surveillance, i.e. feedback from the market, users, reports on complaints, but also the results of any post-marketing PMCF studies that may have been conducted. These requirements correspond to the requirements for clinical evaluations of medical devices / performance evaluations of in-vitro diagnostics; moreover, due to its connectivity, medical device software offers the possibility to a greater extent to collect so-called "real world performance" data and to evaluate them within the framework of post-market surveillance. This will make it much easier and more transparent to record, for example, malfunctions or systematic misuse of the software, which should also benefit the safety of the use of medical device software.
In summary, three cornerstones of this guideline are of crucial importance, all three focus on the generation of valid data; furthermore, the overriding characteristic of this guideline is to provide a practical and appropriate approach.
- Valid clinical context/scientific validity: Proof of conformity of the data output of the medical device software based on the inputs and algorithms with regard to the desired clinical application situation, intended purpose, indication. The proof can primarily be based on a comparison of existing clinical data on performance with the state-of-the-art, which can be justified, for example, in comparison with the guidelines of scientific medical associations
- Technical and analytical performance: Fulfilment of the basic and manufacturer intended criteria of performance of the medical device software, demonstrated by comparison of the input data with the output data in terms of the desired quality, reliability and precision.
- Clinical performance: Demonstration of the ability of medical device software to provide clinically relevant data output related to the desired purpose, the data output must be proven to provide a positive benefit to the health of the user. Depending on the concept of the medical device software, positive effects on, for example, public health are also conceivable.
The aim of the guideline is clear and many manufacturers are already generally aware of it due to the familiar clinical evaluation process. In order to score a goal evaluating the software in one go and with one shot, we are happy to support you in planning and creating your clinical evaluation / performance evaluation for software. Whether for training, for staying up or in the Champions League, seleon GmbH will be happy to assist you.
Please note that all details and listings do not claim to be complete, are without guarantee and are for information purposes only.