Clinical Validation Study of Naevia Medical, a Clinical Decision Support System, in Heart Valve Disease
Conditions: Heart Valve Disease
Sponsor: Dilemma Solutions S.L.

STUDY PROTOCOL 
 
Oﬃcial Title  
Clinical ValidaƟon Study of Naevia Medical, a Clinical Decision Support System, in Heart 
Valve Disease 
 
Brief Title  
ValidaƟon of Naevia Medical in Valvulopathies 
 
Date 03/03/2025 
NCT Number NCT06392464 
Unique Protocol Id 2024/104 
 
Name of the Sponsor 
Dilemma SoluƟons S.L. 
 
Brief Summary 
The study aims to validate naevia medical, a knowledge-based clinical decision support 
system (CDSS), for clinical beneﬁt and safety in cases of cardiac valvulopathies. Using a series 
of retrospecƟve clinical cases of heart valve disease, the research will evaluate the number 
of appropriate and inappropriate recommendaƟons during baseline measurement 
(convenƟonal management) and aŌer CDSS acƟvaƟon. 
 
 

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00. CLINICAL EVALUATION PLAN 
CEP 
PRODUCT: “NAEVIA MEDICAL” 
 
 
 
Prepared by: 
Aitor Castro Casas 
____________________________ 
PRRC 
Regulatory 
Responsible 
/ 
Quality Responsible 
Reviewed by: 
Lorenzo Monserrat  
____________________________ 
Clinical specialist 
Approved by: 
Jesús Pérez Dieguez 
____________________________ 
Director 
 
 
 
 
 
 
 
2025/03/03 
2025/03/03 
2025/03/03 
 
 
 
Reason for the last update 
Ver. 1 – Fist Clinical Evaluation Plan according to the Regulation (EU) 2017/745 on medical 
devices. 
 
 
 

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TABLE OF CONTENTS 
 
1. 
Purpose / Objective of Clinical Evaluation Plan .......................................................... 4 
2. 
Scope of the Clinical Evaluation Plan .......................................................................... 4 
3. 
Product description ................................................................................................... 5 
3.1. 
Special characteristics of the device that may pose safety concerns ............................... 7 
4. 
General safety and performance requirements that require support from relevant 
clinical data ...................................................................................................................... 7 
5. 
Specification of the intended purpose of the device .................................................. 11 
6. 
Intended target groups and user, indications, and contraindications ........................ 11 
7. 
Description of intended clinical benefits to patients. Clinical outcome parameters. .. 11 
7.1 
Direct clinical benefit to provide healthcare professionals with recommendations and 
suggestions for knowledge related to diagnosis, prognosis and/or treatment .......................... 12 
8. 
Alternative options for treatment, diagnostic and/or management ......................... 14 
9. 
Clinical Evaluation Strategy – Relevant clinical data and information about the state of 
the art – Sources and strategies to locate data. Methods to be used to assess data. ........ 14 
9.1 
Outline of the clinical evaluation strategy following Article 61 MDR ............................ 14 
9.2 
Sources and strategy to collect data ............................................................................ 16 
9.3 
Preliminary systematic review .................................................................................... 19 
9.4 
Bibliographic evaluation of Clinical Decision Support Systems¡Error! 
Marcador 
no 
definido. 
A. 
Analysis of published systematic reviews ................................ ¡Error! Marcador no definido. 
B. 
Results of the analysis and publications of note ...................... ¡Error! Marcador no definido. 
9.4.1 
Knowledge domain............................................................................¡Error! Marcador no definido. 
9.4.2 
Scope of use ......................................................................................¡Error! Marcador no definido. 
9.4.3 
Level of integration ...........................................................................¡Error! Marcador no definido. 
9.4.4 
Featured publications .......................................................................¡Error! Marcador no definido. 
10. Methods for assessment of clinical safety ................................................................ 21 
10.1. 
Residual risks ......................................................................................................... 22 
10.2. 
Foreseeable side-effects ......................................................................................... 22 
11. Basis to determine the acceptability of the benefit-risk ratio .................................... 22 
12. Benefit-risk issues relating to the specific components – use of pharmaceuticals, non-
viable animal, or human tissue. ...................................................................................... 30 

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13. Required level of clinical evidence ............................................................................ 30 
14. Clinical development plan (CDP) .............................................................................. 31 
15. References .............................................................................................................. 38 
 
 
 

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1. Purpose / Objective of Clinical Evaluation Plan 
As required under Article 61 and Annex XIV, Part A (1)(a) of Regulation (EU) 2017/745 
(hereafter MDR), this Clinical Evaluation Plan (CEP) is to establish, specify and justify critical 
aspects related to the device under evaluation (DUE) before proceeding with subsequent steps 
to locate, appraise and analyse available clinical data and prior to establishing the Clinical 
Evaluation Report (CER).  
In particular this CEP provides the basis and justification for the level of clinical evidence 
required for the DUE.  
The level of clinical evidence is determined in view of the characteristics of the device and its 
intended purpose and is fundamental to enable the acceptability of the benefit-risk ratio to be 
established.  
Methods to enable identification and evaluation of available clinical data relevant to the DUE 
and its intended use are specified, in order to enable a conclusion to be reached concerning 
the sufficiency of the available clinical data establishing the safety and performance of the 
device. 
 
2. Scope of the Clinical Evaluation Plan 
This Clinical Evaluation Plan is related to the Clinical Evaluation of the following device (family): 
NAEVIA MEDICAL 
 
Figure 1. NAEVIA MEDICAL picture 
 
REFERENCE 
PRODUCT NAME / SHORT DESCRIPTION 
UDI-DI (GTIN14) 
NAEVIA 
MEDICAL 
NAEVIA MEDICAL 
(01)08437026776015 
 
 

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3. Product description  
NAEVIA MEDICAL is software whose ultimate purpose is to provide support to physicians in 
the process of applying medical practice and scientific clinical knowledge. It is based on the 
clinical decision support systems (CDSS) paradigm. 
Naevia Medical does not use artificial intelligence technologies such as machine learning or 
generative systems. Instead, it is based on an expert system composed of clinical rules made 
and validated by healthcare professionals, derived from reliable sources of knowledge and 
subjected to a rigorous peer-review process. 
The solution is implemented on a robust .NET-based platform, fully leveraging the capabilities 
of the Visual Studio IDE to create an integrated, high-productivity development environment. 
The programming languages used during its development were C#, JavaScript, HTML and 
CSS, allowing the development of both back-end components and interactive and responsive 
user interfaces. This architecture promotes modularity and code reusability, facilitating the 
scalable and maintainable integration of new modules and functionalities. Furthermore, 
standardized coding practices and design patterns are adopted to enable early error detection 
through automated testing and static code analysis, ensuring quality and efficiency at every 
stage of the development lifecycle. Overall, this technical approach not only maximizes 
development efficiency but also guarantees the software’s adaptability and evolution in 
response to future technological demands. 
The system is designed with a strong emphasis on compatibility, ensuring seamless integration 
with future upgrades, multiple Software of Unknown Provenance (SOUP) components, and 
various device versions. To achieve this, the architecture follows a modular and loosely 
coupled approach, allowing individual components to be updated or replaced without 
impacting overall system functionality. The implementation adheres to industry best practices 
to facilitate interoperability across different software versions and hardware platforms. 
Additionally, rigorous version control and continuous integration practices are employed to 
guarantee stability and reliability as the system evolves. This approach minimizes disruptions, 
enhances maintainability, and ensures long-term adaptability to technological advancements.  
A fundamental aspect to maintain the security of critical areas in applications is to perform a 
correct validation of input and output data. In naevia's platform, exhaustive validations of input 
and output data are implemented and must be complemented by the relevant functional or 
business logic validations: 
Validations are always performed on the server side. Client-side validations can also 
be useful, but are sufficient.   
Fully cover data validation using validation schemes or standard mechanisms that 
ensure data entry by means of: sanitizations, data type, format, lengths, values, 
whitelists, blacklists, etc.   
Ensure that structured data is strongly typed and validated according to a defined 
scheme, including allowed characters, length and pattern.  
Verify that unstructured data is sanitized to impose generic security measures, such as 
allowed characters and length, and avoid potentially harmful characters.   

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Ensures that all untrusted input is properly sanitized using a sanitization library.   
Avoid displaying sensitive information as a result of a validation error for any parameter 
received.   
It accepts only the expected data at each application entry point that comes from the 
user, from the end process of all input fields, forms, URLs, application cookies, etc. 
Any unexpected data should be rejected   
Verify that server-side input validation errors result in rejection of the request.   
Ensures that all database queries are protected using parameterized queries to prevent 
SQL injection.   
Checks that the application is not susceptible to command injection.   
Checks that the application does not contain mass parameter assignment 
vulnerabilities (AKA automatic variable binding). Ensure that all input data is validated, 
not just HTML form fields, but all input sources such as REST requests, query 
parameters, HTTP headers, cookies, batch files, RSS feeds, etc., using whitelists, 
lesser forms of validation such as greylists (which remove known bad strings) or 
blacklists (which reject bad input). 
 
The product is a Class IIa medical device according to Rule 11 of Annex VIII of the MDR.  
 
It belongs to the following device categories: 
GMDN CODE 
GMDN TERM 
DESCRIPTION 
61087 
Clinical management 
support software 
An application software program intended to facilitate 
clinical management decisions by receiving structured 
patient data from electronic health records and/or 
manually-entered information (e.g., demographics, 
diagnostic/laboratory results) and returning clinical 
care information (e.g., reports, pedigree diagrams, 
reminders, 
post-therapy 
prognosis/risk 
score, 
anesthesia/pharmaceutical dosage, and/or links to 
guidelines) to a healthcare professional responsible for 
patient care; it is not primarily intended to analyse or 
manage diagnostic/patient images. Otherwise known 
as clinical decision support (CDS) software, it may be 
a locally installed program, web-based, or mobile 
application. 
 
EMDN CODE 
CATEGORY 
CATEGORY DESCRIPTION 
EMDN CODE DESCRIPTION 
V92 
V 
Various devices 
Medical device software - not 
included in other classes 

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3.1. 
Special characteristics of the device that may pose safety concerns 
The DUE does not include medicinal, human, or animal components. It does not have any 
special characteristics that could pose new safety concerns. 
 
4. General safety and performance requirements that require support from 
relevant clinical data 
For the DUE, and following the Annex XIV (Part A, Section 1) of the MDR, the General Safety 
and Performance Requirements (GSPR) extracted from the Annex I that require support from 
relevant clinical data are:  
 
[GSPR #1] Devices shall achieve the performance intended by their manufacturer and shall 
be designed and manufactured in such a way that, during normal conditions of use, they are 
suitable for their intended purpose. They shall be safe and effective and shall not compromise 
the clinical condition or the safety of patients, or the safety and health of users or, where 
applicable, other persons, provided that any risks which may be associated with their use 
constitute acceptable risks when weighed against the benefits to the patient and are 
compatible with a high level of protection of health and safety, taking into account the generally 
acknowledged state of the art.  
 
Relevant clinical data are to be located concerning: 
- 
Device clinical performance. Increasing the number of appropriate diagnostic and 
therapeutic recommendations compared to conventional medical management. 
- 
Device clinical safety. The product aims to provide this support safely for the patient, 
without generating inappropriate recommendations under normal conditions of use. 
- 
Clinical conditions of the patient population and clinical outcomes following use of the 
device 
- 
Clinical benefits related to the use of the device. Increasing adherence to scientific 
recommendations 
- 
Clinical risks related to the use of the device. Generate recommendations and 
suggestions of knowledge that do not apply to the patient's data. 
- 
Acceptability of the benefit-risk profile → Compatibility with a high level of protection of 
health and safety  
 
The purpose of the product is to add value to healthcare professionals in the decision-making 
process by increasing appropriate recommendations based on scientific evidence and 
decreasing inappropriate recommendations. Its use aims to promote the practice of scientific 
medicine, with greater adherence to scientific recommendations that have been shown to 
improve patient outcomes. 

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It is important to emphasize that naevia medical does NOT replace the physician nor does it 
make autonomous decisions regarding patient management; its function is limited to 
presenting a prioritized series of knowledge recommendations that the professional can review 
and choose to adopt or not. These recommendations are explainable by showing the facts that 
triggered the recommendation, reproducible, and supported by information referenced in 
bibliographic citations. In this regard, naevia medical does NOT use artificial intelligence 
algorithms based on case learning nor does it employ generative technologies. 
 
The product aims to provide this support safely for the patient, without undesirable side effects 
or adverse events under normal conditions of use. 
 
[GSPR #2] The requirement in [Annex I of the MDR] to reduce risks as far as possible means 
the reduction of risks as far as possible without adversely affecting the benefit-risk ratio. 
Relevant clinical data are to be located concerning: 
- 
Acceptability of the benefit-risk profile → Compatibility with a high level of protection of 
health and safety 
The risk-benefit relationship is favourably justified when the benefits in improving the quality of 
care, increasing appropriate recommendations, and decreasing inappropriate ones outweigh 
the risks associated with the use of CDSS. This requires careful modelling of knowledge, 
correct implementation, constant monitoring, and continuous updating. Acceptance of the 
system by clinical users is crucial and must be ensured through appropriate training, support, 
and participation in system improvement. 
 
Deterministic rules are widely used for problem-solving. The application of a set of clinical rules 
to the specific data of a patient enables the logical generation of recommendations and allows 
for the review of knowledge that applies to a particular case. However, it should be considered 
that there is intrinsic uncertainty in each decision-making problem, and the medical knowledge 
managed by a system has limitations. 
 
With the objective of reducing risks the knowledge modelling and rule-making system of naevia 
medical facilitates semantically well-formed rules with coherent logic. The peer review process 
aims to ensure that the variables and propositions used have the same meaning as the texts 
of the recommendations they intend to model. 
 
[GSPR #4] Risk control measures adopted by manufacturers for the design and manufacture 
of the devices shall conform to safety principles, taking account of the generally acknowledged 

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state of the art. To reduce risks, Manufacturers shall manage risks so that the residual risk 
associated with each hazard as well as the overall residual risk is judged acceptable. In 
selecting the most appropriate solutions, manufacturers shall, in the following order of priority: 
(a) eliminate or reduce risks as far as possible through safe design and manufacture; (b) where 
appropriate, take adequate protection measures, including alarms if necessary, in relation to 
risks 
that 
cannot 
be 
eliminated; 
and 
(c) 
provide 
information 
for 
safety 
(warnings/precautions/contra-indications) and, where appropriate, training to users. 
Manufacturers shall inform users of any residual risks.  
Relevant clinical data is to be located concerning: 
- 
Generally acknowledged state of the art as related to clinical, biological, and technical 
aspects of the DUE 
- 
Information for safety / training to users currently provided with similar devices on the 
market 
- 
Known and foreseeable residual risks currently related to similar devices on the market 
 
[GSPR #5] In eliminating or reducing risks related to use error, the manufacturer shall (a) 
reduce as far as possible the risks related to the ergonomic features of the device and the 
environment in which the device is intended to be used (designed for patient safety), and (b) 
give consideration to the technical knowledge, experience, education, training and use 
environment, where applicable, and the medical and physical conditions of intended users 
(design for lay, professional, disabled or other users).  
Relevant clinical data is to be located concerning: 
- 
Specific usability / use-error risks that are known or foreseeable 
- 
Information for safety / training to users currently provided with similar devices on the 
market 
 
Naevia medical is designed to be used by physicians after a short training period aimed at 
eliminating any errors in usage. Specific usability/use-error risks that are known or foreseeable 
should be considered. Safety/training information currently provided to users of similar devices 
on the market must also be taken into account. The transparency of the software's knowledge 
recommendations, which simply explain the conditions that triggered them, allows the 
professional to interact with and critically analyze the suggestions. 
 
[GSPR #6] The characteristics and performance of a device shall not be adversely affected to 
such a degree that the health or safety of the patient or the user and, where applicable, of 
other persons are compromised during the lifetime of the device, as indicated by the 
manufacturer, when the device is subjected to the stresses which can occur during normal 
conditions of use and has been properly maintained in accordance with the manufacturer's 
instructions.  

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Relevant clinical data is to be located concerning: 
- 
Normal conditions of use of the DUE, including other devices interconnected or used 
together with the DUE 
- 
Information for proper maintenance currently provided with similar devices on the 
market 
- 
Clinical data to support the clinical safety and performance of the device throughout 
the claimed lifetime of the device. 
 
The system's knowledge is updated throughout the lifetime of the knowledge version, 
incorporating the necessary fine-tuning to facilitate updating and the correct interpretation of 
the recommendations. This includes normal conditions of use and clinical data to support the 
clinical safety and performance of the device throughout its claimed lifetime. 
 
 [GSPR #8] All known and foreseeable risks, and any undesirable side-effects, shall be 
minimised and be acceptable when weighed against the evaluated benefits to the patient 
and/or user arising from the achieved performance of the device during normal conditions of 
use. 
Relevant clinical data are to be located concerning: 
- 
State of the art concerning known clinical risks and side-effects 
- 
Clinical benefits related to the use of the device 
- 
Clinical risks related to the use of the device 
- 
Acceptability of the benefit-risk profile  
Risks and potential adverse effects will be monitored and controlled. 
 
In relation to the potential benefits, Naevia medical provides recommendations to doctors 
based on the latest evidence and clinical guidelines, supporting informed and updated 
decision-making. It promotes consistency in care by standardizing data presentation and 
encouraging evidence-based practice, reducing variability in patient treatment and clinical 
inertia. Additionally, the CDSS reduces medical errors by offering knowledge-based 
suggestions, particularly in high-pressure environments with complex patients, and serves as 
an educational tool, keeping professionals informed about scientific updates while they 
perform their clinical duties. 
 
[GSPR #23] Label and instructions for use: (4) Information in the instructions for use (e) the 
performance characteristics of the device.  
Relevant clinical data are to be located concerning: 
- 
State of the art concerning known clinical risks  

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Detailed usage instructions on the platform itself, as well as updated information on the latest 
evidence. 
 
5. Specification of the intended purpose of the device 
Naevia Medical is a clinical decision support software (CDSS) that, based on clinical input data 
(history, symptoms, complementary test data...) of adult patients with heart disease, provides 
healthcare professionals with prioritized, justified and referenced recommendations and 
knowledge suggestions related to diagnosis and/or treatment (recommended tests, 
medications, interventions,..). 
 
6. Intended target groups and user, indications, and contraindications 
Intended target groups  
• 
Adult patients with valvular heart disease, offering diagnostic, prognostic and therapeutic 
recommendations. 
 
Intended users 
Medical doctors or residents who are familiar with the user manual and how to use the tool. 
 
Indications  
- 
Naevia medical is indicated, as part of the assessment of a patient by healthcare 
professionals, to assist in making diagnostic prognostic and therapeutic decisions of 
patients suffering from valvular heart disease. 
 
Contraindications 
Population under 18 years of age.  
Medical emergency situations. 
 
7. Description of intended clinical benefits to patients. Clinical outcome 
parameters. 
It is expected that, compared to conventional clinical management without a clinical 
decision support system, naevia will increase the number of appropriate recommendations 
and significantly reduce the number of inappropriate and absent recommendations in all 
the domains analyzed. 

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7.1 Direct clinical benefit to provide healthcare professionals with recommendations 
and suggestions for knowledge related to diagnosis, prognosis and/or treatment  
• 
Positive impact on enhancing the ability of healthcare professionals to provide a greater 
number of recommendations appropriate to each patient's specific situation, supporting 
diagnostic and clinical decision making. 
Naevia medical provides recommendations based on the latest evidence and clinical 
guidelines, supporting informed and updated decision-making. It promotes consistency in care 
by standardizing data presentation and encouraging evidence-based practice, reducing 
variability in patient treatment and clinical inertia. Additionally, the CDSS reduces medical 
errors by offering knowledge-based suggestions, particularly in high-pressure environments 
with complex patients, and serves as an educational tool, keeping professionals informed 
about scientific updates while they perform their clinical duties. 
Naevia Medical has developed an advanced platform for managing clinical data, which has 
demonstrated its effectiveness, acceptability and usability in various studies. This platform has 
been key in COVID-19 research, especially in cardiac diseases such as cardiomyopathies and 
heart failure. 
 
Evidence from pre-clinical evaluation 
- 
Through the system functions and features, users have to be able to get 
recommendations and suggestions for knowledge related to diagnosis, prognosis 
and/or treatment.  
o V&V per EN ISO 62304 and install validation. All tests provide 
recommendations and suggestions and thus CDSS is verified by all tests. 
o V&V per EN 82304 and install validation. All tests provide recommendations 
and suggestions and thus CDSS is verified by all tests. 
o Usability testing of the software by representative users to successfully 
provides recommendations and suggestions for knowledge related to 
diagnosis, prognosis and/or treatment. 
 
- 
Capability of Naevia medical in the management of patients with cardiovascular 
diseases: a pre-clinical study of simulated cases in patients with valvular heart disease, 
using the “2021 ESC/EACTS Guidelines for the management of valvular heart 
disease”, must validate the Naevia medical’s capability in the management of patients 
with valvular heart disease. 
 
Evidence from clinical evaluation/investigations 
- 
Software enables to provide recommendations and suggestions for knowledge related to 
diagnosis, prognosis and/or treatment: scientific literature confirms that SW system 
functions and features are aligned with current user requirements as described in relevant 
publications and as included in the DUE. 

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- 
Increase of appropriate recommendations: number of appropriate recommendations – an 
observational, single-centre study consisting on a clinical validation of a CDSS applied to 
retrospective clinical cases of cardiology patients with valvular heart disease, shall confirm 
an increase in the number of appropriate recommendations by  by a factor of three over 
the recommendations generated with general management without the support system. 
 
- 
Inappropriate recommendations: number of inappropriate recommendations – an 
observational, single-centre study consisting on a clinical validation of a CDSS applied to 
retrospective clinical cases of cardiology patients with valvular heart disease, shall confirm 
that naevia medical, compared to conventional management, is able to reduce the number 
of inappropriate and mising recommendations based on scientific evidence in the 
diagnostic and therapeutic areas. 
 
- 
Concordance between expert evaluators:   
 
o The analysis of the degree of agreement/disagreement in the different domains, 
evaluated using a Likert scale, analysed using ordinal regression (Joshi, Kale et al. 
2015). Response options: 1. Strongly disagree, 2. Disagree, 3. Neither agree nor 
disagree, 4. Agree, 5. Strongly agree. 
o The panel’s agreement with the CDSS was very high, with a mean Likert 
scale score above 4 in all domains (STD: 4.53, LTD: 4.68, CMT: 4.58, ITI: 
4.62). The probability of strongly agreeing was significantly higher than the 
probability any of the other categories (z.ratio (5|1)= 30.88, p-value< 0.001, 
z.ratio (5|2)= 31.67, p-value < 0.001, z.ratio (5|3) = 30.21, p-value< 0.001, 
z.ratio (5|4)= 22.54, p-value< 0.001).      
 
Evidence from registries 
Additionally, Naevia Medical’s platform has been or is being used in other significant registries, 
such as: 
 
- 
The “REDMIO20: Spanish Registry of Dilated Cardiomyopathy due to RBM20 Gene 
Mutation,” a multicenter registry coordinated by Dr. Eduardo Villacorta from the 
Salamanca University Assistance Complex, which included 168 cases up to December 
11, 2023. 
- 
The “PROCeso asistencial integrado de Estenosis Aórtica grave Sintomática 
(PROCEAS)” 2020/405 registry, led by Dr. Violeta González-Salvado from the 
Santiago de Compostela University Clinical Hospital, with 991 cases of aortic stenosis 
registered up to December 11, 2023. 
 

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Evidence from post-market surveillance: 
- 
PMS evidence to evaluate the absence of complications and low incidence of adverse 
events. 
- 
Recommendations that could lead to a serious adverse event shall be identified as 
patient safety risks, including: 
o Possibility of death. 
o The need for a medical or surgical intervention that would not otherwise be 
necessary. 
o Omission of critical recommendations that could adversely affect the patient’s 
prognosis. 
 
8. Alternative options for treatment, diagnostic and/or management 
The alternative to CDSS is conventional clinical management, where doctors alone face 
complex patients with limited decision time, contributing to medical errors (Newman-Toker, 
Nassery et al. 2023) (Makary and Daniel 2016). CDSS can reduce errors caused by oversight 
(Avery, Sheehan et al. 2021), but medical decisions still depend on doctors' experience, 
leading to avoidable harm and costs (Makary and Daniel 2016, 2017, Avery, Sheehan et al. 
2021) (Bates and Gawande 2003, Shrank, Rogstad and Parekh 2019). 
Many patients do not receive the benefits of scientific medical recommendations (Ghazi, 
Yamamoto et al. 2022, Bhatt, Varshney et al. 2023), despite the increase in health record data 
and new clinical guidelines (Balas and Boren 2000, Obermeyer and Lee 2017). Limited 
consultation time, patient complexity, and provider inexperience increase care variability 
(Singh and Sittig 2015, Jarjour, Henri et al. 2020). 
Medical progress relies on doctors' decisions and patient adherence (Walewska-Zielecka, 
Religioni et al. 2021, Fonarow, Yancy et al. 2011). However, doctors often use heuristics—
mental shortcuts that speed decisions but cause cognitive biases (Blumenthal-Barby and 
Krieger 2015). These biases, influenced by past experiences, clinical inertia, and patient 
factors like age or sex, affect decision accuracy (Blumenthal-Barby and Krieger 2015). 
 
9. Clinical Evaluation Strategy – Relevant clinical data and information about the 
state of the art – Sources and strategies to locate data. Methods to be used to 
assess data. 
9.1 Outline of the clinical evaluation strategy following Article 61 MDR 
Naevia medical is a Class IIa medical device software. 
Following Article 61(3) of the MDR, the clinical evaluation is based on: 
(a) A critical evaluation of the relevant scientific literature currently available relating to the 
safety, performance, design characteristics and intended purpose of the device, 
meeting the following conditions as described in the clinical evaluation report: 

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- 
It is demonstrated that this generation of Naevia medical subject to clinical 
evaluation for the intended purpose is potentially equivalent to the device to 
which the data relate, in accordance with Section 3 of Annex XIV, and 
- 
It is shown that the data adequately demonstrate compliance with the 
relevant GSPRs. 
 
(b) A critical evaluation of the results of all available clinical investigations, taking duly into 
consideration whether the investigations were performed under Articles 62 to 80 MDR, 
and acts adopted pursuant to Article 81 MDR, and Annex XV, and 
 
(c) A consideration of the currently available alternative treatment options for the same 
intended purpose. 
 
Moreover, Article 61(4) of the MDR, in the case of implantable devices and class III devices, 
clinical investigations shall be performed, except if:  
— the device has been designed by modifications of a device already marketed by the 
same manufacturer,  
— the modified device has been demonstrated by the manufacturer to be equivalent to 
the marketed device, in accordance with Section 3 of Annex XIV and this demonstration 
has been endorsed by the notified body, and  
— the clinical evaluation of the marketed device is sufficient to demonstrate conformity of 
the modified device with the relevant safety and performance requirements.  
This consideration enables a clinical evaluation strategy with the requirement to not perform 
specific pre-market clinical investigations with the DUE with the condition that clinical data 
provided from the equivalent device is sufficient and clinical evidence is available. 
Per Article 2(48) MDR, ‘clinical data’ means: 
Any information concerning the safety or performance that is generated from the use of a 
device and is sourced from the following: 
— Clinical investigation(s) of the device concerned, 
— Clinical investigation(s) or other studies reported in scientific literature, of a device for 
which equivalence to the device in question can be demonstrated, 
— Reports published in peer reviewed scientific literature on other clinical experience of 
either the device in question or a device for which equivalence to the device in question 
can be demonstrated, 
— Clinically relevant information coming from post-market surveillance, in particular the 
post-market clinical follow-up. 
Clinical data are sourced from clinical investigations of the DUE to support its specific clinical 
performance aspects.  

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In addition, despite meeting the regulatory definition of ‘clinical data’ through undergoing a 
clinical investigation with the device concerned, other sources of relevant information are 
considered, specifically pre-clinical data related to the DUE itself together with data pertaining 
to similar devices, to establish the current state of the art along with the criteria for the 
assessment of the safety and performance of this type of device. Data concerning similar 
devices and current alternatives are considered as supporting data and to enable planning of 
PMS and PMCF activities. 
The scientific quality of the available datasets is assessed during the appraisal stage of the 
clinical evaluation. Appraised datasets that qualify as ‘clinical data’ (as defined in Article 2 (48) 
of the MDR) are included in the analysis to determine if the available information provides 
sufficient clinical evidence. 
 
9.2 Sources and strategy to collect data 
Based on current experience during product development as well as information known to be 
available regarding other similar products, the following strategies are to be used to locate and 
gather all relevant information: 
(i) 
Initial general searches to establish the parameters used to determine the 
acceptability of the DUE benefit-risk ratio, and to identify early clinically relevant 
information pertinent to the DUE and its intended use. Preliminary general searches 
also contribute to elaborate an accurate PICO strategy for the literature review, and 
to establish more specific search terms with the purpose of obtaining state-of-the-
art relevant data. 
(ii) 
Searches of peer-reviewed scientific literature to establish the scientific basis, 
technical and clinical requirements as related to the identified clinical need, clinical 
conditions, standard of care, state of the art, similar devices, and alternative 
treatment methods. 
(iii) 
Search and review of background and clinical experience as related to similar 
devices, including clinically relevant information coming from post-market 
surveillance and/or post-market clinical follow-up.  
(iv) 
Search of scientific literature with published results of clinical investigation(s) and 
other studies to determine: 
a. Possible benefits  
b. Possible risks, side-effects and harm that may have been due to the DUE 
or similar devices 
c. Safety information as related to the DUE or similar devices. 
(v) 
(In-house / Unpublished) Clinical investigation report(s) as related to the device 
concerned. 
Strategy (i) will be used to define the clinical evaluation scope, the regulatory strategy, and the 
framework for the design of the consequent literature searches. These preliminary State of the 
Art Results should drive the Safety and Performance objectives for the DUE. 

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Strategies (ii) and (iii) will be used to establish the clinical aspects and characteristics for which 
ample scientific, technical and/or clinical data are believed to be already available. 
Strategy (iv) will be used to locate clinically relevant data sets for the device concerned or for 
reference devices that may be demonstrated to be equivalent. 
Strategy (v) will be used if the device itself has undergone clinical investigation, clinical trial, or 
other clinical study. 
The following sources shall be considered: 
- 
Scientific databases: PubMed 
- 
Systematic review databases: Cochrane library  
- 
Manual searches (internet searches, vigilance databases, non-published data and 
Citations referenced in scientific literature).  
The clinical evaluation for the device under evaluation is planned, conducted, and documented 
according to article 61 and Annex XIV part A of the MDR and applicable MDCG guidelines. 
Nevertheless, the MEDDEV 2.7/1 (Rev.4 of June 2016) is useful to structure the clinical 
evaluation, to establish an appraisal plan and to establish the different stages of the Clinical 
Evaluation process (see figure 2 below). 
 
Stage 0: Clinical Evaluation Scoping and Planning (as set out in this document) 
Stage 1: Identification of clinical data relevant to the device and its intended use (as 
described above), and any gaps in clinical evidence, through a systematic scientific 
literature review. The details of the literature review to be conducted are established in a 
Literature Search Protocol. This protocol defines the specific search terms and search 
algorithms to be applied (as determined by current knowledge of the device, the conditions of 
use, intended use, indications for use), inclusion and exclusion criteria to be applied to articles 
and data sets found and to determine which datasets can only be considered as contribution 
to information about the state of the art and which datasets meet the definition of “clinical data” 
and hence can be routed to the appraisal stage. If data pertaining to a similar device is 
considered, clinical, biological, and technical equivalence to the device under evaluation must 
first be demonstrated with sufficient explanation and rationale for any gaps found. Search 
terms and search algorithms are defined so that the search is repeatable, thorough, objective, 
and comprehensive (depth and extent are proportionate and appropriate to the nature 
classification, intended purpose and risks of the device) taking into account both favourable 
and unfavourable data. 

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Stage 2: Appraisal of all clinical data by evaluating their suitability for establishing the 
safety and performance of the device. The Literature Search Protocol also contains the 
appraisal plan and methods to be applied to assess all relevant clinical data by evaluating their 
suitability to establish the safety and performance of the DUE. The results of the execution of 
the Literature Search Protocol shall be documented in the Literature Search Report. A 
summary with the most relevant results and conclusions shall be extracted to the Clinical 
Evaluation Report. 
Stage 3: Analysis of all relevant “clinical data” in order to reach conclusions about the 
safety and performance of the device, including its clinical benefits.  
Appraised clinical data is analysed to determine if there is “sufficient clinical evidence” to 
conclude that the device is safe and achieves its intended benefits. The level of clinical 
evidence to be attained is specified in Section 13 below. The analysis contemplates relevant 
information collected concerning clinical benefits that are confirmed, quantification of risk 
frequency and severity as well as confirmation of the effectiveness of risk control measures, a 
qualitative and, if possible, quantitative determination of the benefit-risk ratio (e.g., marginally 
positive versus clearly positive) Any missing data or gaps in clinical data are identified. If it 
cannot be concluded that sufficient clinical evidence is available, then additional clinical data 
must be generated, e.g. through additional literature searching and/or through specific clinical 
investigations in accordance with the clinical development plan to address outstanding gaps 
and/or issues. 
 
Stage 4: Clinical Evaluation Report. PMS Plan. PMCF Plan. 
Results of the previous steps along with the conclusions concerning the availability of sufficient 
clinical evidence are documented, reviewed, and approved in the Clinical Evaluation Report. 
Ongoing steps to continuously update the clinical evaluation are set out in the PMS Plan and 
PMCF Plan. 

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Figure 2. Stages of Clinical evaluation according to MEDDEV 2.7/1 (Rev.4) 
 
9.3 Preliminary systematic review 
A pilot scientific literature review was performed to identify initial State of the Art clinical data 
relevant to the device safety and performance objectives and its intended use, as detailed 
below. 
Based on the results and outcomes from this preliminary search, the full PICO strategy, search 
terms and search algorithms are established and detailed in the Literature Search Protocol.  
 
• 
Search details: "clinical decision support systems"[Title] NOT artificial intelligence / 
Filters: Review [article type], Systematic Review [article type], Humans [species] → 135 
results 
Search query: ("clinical decision support system"[Title]) AND ((y_5[Filter]) AND (humans[Filter])) 
• 
Search details: "clinical decision support systems"[Title] NOT artificial intelligence AND 
recommendations / No filters applied → 84 results  
Search query: ("clinical decision support systems"[Title] NOT ("artificial intelligence"[MeSH Terms] OR 
("artificial"[All Fields] AND "intelligence"[All Fields]) OR "artificial intelligence"[All Fields])) AND 
("recommend"[All Fields] OR "recommendable"[All Fields] OR "recommendation"[All Fields] OR 

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"recommendation s"[All Fields] OR "recommendations"[All Fields] OR "recommended"[All Fields] OR 
"recommending"[All Fields] OR "recommends"[All Fields]) 
• 
Search details: "ischemic heart disease"[Title] / Filters: in the last 5 years [publication 
date], Review [article type], Systematic Review [article type], Humans [species] → 142 
results 
Search query: ("ischemic heart disease"[Title]) AND ((y_5[Filter]) AND (review[Filter] OR 
systematicreview[Filter]) AND (humans[Filter])) 
• 
Search details: "valvular heart disease"[Title] / Filters: in the last 5 years [publication 
date], Review [article type], Systematic Review [article type], Humans [species] → 91 
results 
Search query: ("valvular heart disease"[Title]) AND ((y_5[Filter]) AND (review[Filter] OR 
systematicreview[Filter]) AND (humans[Filter])) 
• 
Search details: "clinical decision support system"[Title] AND (side effects OR adverse 
effects OR clinical risks) / No filters applied → 217 results 
Search query: "clinical decision support system"[Title] AND ("adverse effects"[MeSH Subheading] OR 
("adverse"[All Fields] AND "effects"[All Fields]) OR "adverse effects"[All Fields] OR ("side"[All Fields] 
AND "effects"[All Fields]) OR "side effects"[All Fields] OR ("adverse effects"[MeSH Subheading] OR 
("adverse"[All Fields] AND "effects"[All Fields]) OR "adverse effects"[All Fields]) OR (("ambulatory care 
facilities"[MeSH Terms] OR ("ambulatory"[All Fields] AND "care"[All Fields] AND "facilities"[All Fields]) 
OR "ambulatory care facilities"[All Fields] OR "clinic"[All Fields] OR "clinic s"[All Fields] OR "clinical"[All 
Fields] OR "clinically"[All Fields] OR "clinicals"[All Fields] OR "clinics"[All Fields]) AND ("risk"[MeSH 
Terms] OR "risk"[All Fields] OR "risks"[All Fields]))) 
 
The following articles were considered relevant to reach the objective of this search. Another 
approach used to identify relevant information for the clinical evaluation of the DUE was the 
manual search used as a supplemental path to find the following additional relevant articles.  
Copies of the selected articles are gathered attached to the present document. 
1. Muhiyaddin, R., Abd-Alrazaq, A. A., Househ, M., Alam, T., & Shah, Z. (2020). The 
Impact of Clinical Decision Support Systems (CDSS) on Physicians: A Scoping 
Review. 
Studies 
in 
health 
technology 
and 
informatics, 
272, 
470–473. 
https://doi.org/10.3233/SHTI200597  
2. Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, 
K. I. (2020). An overview of clinical decision support systems: benefits, risks, and 
strategies for success. NPJ digital medicine, 3, 17. https://doi.org/10.1038/s41746-020-
0221-y  
3. Kwan, J. L., Lo, L., Ferguson, J., Goldberg, H., Diaz-Martinez, J. P., Tomlinson, G., 
Grimshaw, J. M., & Shojania, K. G. (2020). Computerised clinical decision support 
systems and absolute improvements in care: meta-analysis of controlled clinical trials. 
BMJ (Clinical research ed.), 370, m3216. https://doi.org/10.1136/bmj.m3216  

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4. Jensen, R. V., Hjortbak, M. V., & Bøtker, H. E. (2020). Ischemic Heart Disease: An 
Update. 
Seminars 
in 
nuclear 
medicine, 
50(3), 
195–207. 
https://doi.org/10.1053/j.semnuclmed.2020.02.007  
5. Writing Committee Members, Otto, C. M., Nishimura, R. A., Bonow, R. O., Carabello, 
B. A., Erwin, J. P., 3rd, Gentile, F., Jneid, H., Krieger, E. V., Mack, M., McLeod, C., 
O'Gara, P. T., Rigolin, V. H., Sundt, T. M., 3rd, Thompson, A., Toly, C., ACC/AHA Joint 
Committee Members, O'Gara, P. T., Beckman, J. A., Levine, G. N., … Woo, Y. J. 
(2021). 2020 ACC/AHA guideline for the management of patients with valvular heart 
disease: A report of the American College of Cardiology/American Heart Association 
Joint Committee on Clinical Practice Guidelines. The Journal of thoracic and 
cardiovascular 
surgery, 162(2), 
e183–e353. 
https://doi.org/10.1016/j.jtcvs.2021.04.002  
6. Graham, T. A., Kushniruk, A. W., Bullard, M. J., Holroyd, B. R., Meurer, D. P., & Rowe, B. 
H. (2008). How usability of a web-based clinical decision support system has the potential 
to contribute to adverse medical events. AMIA ... Annual Symposium proceedings. AMIA 
Symposium, 2008, 257–261.  
 
10. Methods for assessment of clinical safety  
Clinical safety concerns, including known residual risks and side-effects of medical device 
software, shall be identified from Risk analysis/Risk assessment, vigilance/adverse events 
databases and published scientific literature. Specific benefit-risk assessment includes the 
parameters described in Section 11 of the present report. 
Identification of initial data concerned to clinical safety shall be identified through consultation 
of vigilance and/or adverse-events databases for similar devices. This strategy is also 
described in section 9 of the present document.  
Specific databases that shall be used for clinical safety data identification are: 
• 
Agencia Española de Medicamentos y Productos Sanitarios (AEMPS) – Medical 
Devices Alerts 
• 
Medicine and Healthcare Products and Regulatory Agency (MHRA) – Alerts and recalls 
for drugs and medical devices 
• 
FDA Medical Devices Recalls 
• 
FDA Total Product Life Cycle (TPLC)  
• 
FDA Manufacturer and User Facility Device Experience (MAUDE) 
The following sections detail the potential specific residual clinical risks and the foreseeable 
side effects related to the use of the device under evaluation that shall be addressed in the 
Clinical Evaluation and supported with clinical data. 
As stated previously, the device has no special characteristic or novel features. However, 
residual risks will be addressed in the device risk management documentation to manage any 
special performance or safety concerns.  
Clinical safety of the DUE will be determined: 

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- 
Indirectly on the basis of software verification and validation. 
- 
Through the assessment of the characteristics of the finished device and fulfilment of 
technical specifications. 
- 
Through review and assessment of clinical data assessing the safety and performance 
of similar / equivalent devices. 
- 
Through review and assessment of incidents, adverse events, or other relevant safety 
reports for similar / equivalent devices. 
In particular, information concerning potential specific residual clinical risks and the 
foreseeable side effects related to the use of the device under evaluation that shall be 
addressed in the Clinical Evaluation and supported with clinical data are gathered in the 
following sub-sections. 
 
10.1. Residual risks 
Residual risks were analysed and addressed following the Risk Management Plan. Risks that 
are relevant during clinical use and residual risks are listed below and shall be considered 
when searching for relevant clinical data.  
Relevant risks during clinical use and residual risks are listed below and shall be considered 
when searching for pertinent clinical data to the DUE. 
 
Risk 
ID 
Risk 
Description 
2 
Misdiagnosis 
caused by product’s 
error 
The user makes a clinical decision based on wrong 
recommendations. 
3 
Misdiagnosis 
caused 
by 
user’s 
errors 
The output data given by the software is not adequate for the 
patient. The user makes a clinical decision based on wrong 
recommendations. 
4 
Patient’s privacy and 
data security 
The output data given by the software is not adequate for the 
patient due to the corruption of patient's data. The user makes 
a clinical decision based on wrong recommendations. 
 
10.2. Foreseeable side-effects  
No side-effects have been identified for this device. 
 
11. Basis to determine the acceptability of the benefit-risk ratio  

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Naevia Medical is a clinical decision support software (CDSS) that, based on clinical input data 
(history, symptoms, complementary test data...) of adult patients with heart disease, provides 
healthcare professionals with prioritized, justified and referenced recommendations and 
knowledge suggestions related to diagnosis and/or treatment (recommended tests, 
medications,...). 
 
The main clinical benefit obtained with the DUE is the following: 
• 
Direct clinical benefit to provide healthcare professionals with recommendations and 
suggestions for knowledge related to diagnosis, prognosis and/or treatment. 
 
The main complications associated with the use of product category are the following: 
- 
Misdiagnosis caused by product’s error. 
- 
Misdiagnosis caused by user’s errors. 
- 
Patient’s privacy and data security. 
 
The risk-benefit ratio is favorably justified when the benefits in improved quality of care, 
increased appropriate recommendations and decreased inappropriate recommendations 
outweigh the risks associated with the use of CDSS. This requires careful knowledge 
modelling, correct implementation, constant monitoring and continuous updating. Acceptance 
of the system by clinical users is crucial and must be ensured through appropriate training, 
support and participation in system improvement. 
Clinical benefits and possible complications or side effects related to the use of naevia Medical 
will be further investigated and assessed in the planned literature searches and included in the 
Clinical Evaluation Report (see Benefit-Risk Analysis in section 5 of the Technical 
Documentation). 
Based on the state of the art in medicine, the acceptability of the DUE benefit-risk ratio for the 
indications mentioned above, depends on clinical safety and performance parameters and 
outcomes as listed below. 
 
 

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Table 1 – Naevia Medical – Indicative List of Clinical Safety and Performance Parameters and Quantitative Measures 
Parameter / 
Outcome 
Quantitative Measure of Performance 
Source / Level of Evidence Required 
Performance of Naevia medical to provide healthcare professionals with recommendations and suggestions for knowledge related to diagnosis, prognosis 
and/or treatment 
Software enables to 
provide 
recommendations 
and suggestions for 
knowledge 
related 
to 
diagnosis, 
prognosis 
and/or 
treatment 
Goal: all basic key characteristics of CDSS 
are 
supported 
and 
all 
supported 
characteristics 
work 
correctly 
and 
as 
expected. 
[references to sources] 
*EN ISO 62304 – Medical device software – 
Software life-cycle processes 
EN 82304 – Health Software – Part 1: 
General requirements for product Safety 
Preclinical 
Accepted if using the system functions and features, users are able to get 
recommendations and suggestions for knowledge related to diagnosis, prognosis 
and/or treatment.  
- 
V&V per EN ISO 62304 and install validation. All tests provide 
recommendations and suggestions and thus CDSS is verified by all tests. 
- 
V&V per EN 82304 and install validation. All tests provide recommendations 
and suggestions and thus CDSS is verified by all tests. 
- 
Usability testing of the software by representative users to successfully 
provides recommendations and suggestions for knowledge related to 
diagnosis, prognosis and/or treatment. 
Clinical 
Accepted if system features cover user needs for getting recommendations and 
suggestions for knowledge related to diagnosis, prognosis and/or treatment aligned 
with the tools required for medical diagnosis support and using the system functions 
and features, users successfully use CDSS to make clinical decisions regarding  
diagnosis, prognosis and/or treatment.  
- 
V&V per EN ISO 62304 and install validation. All tests provide 
recommendations and suggestions and thus CDSS is verified by all tests*. 
- 
V&V per EN 82304 and install validation. All tests provide recommendations 
and suggestions and thus CDSS is verified by all tests. 

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- 
Usability testing of the software by representative users to successfully 
provide recommendations and suggestions for knowledge related to 
diagnosis, prognosis and/or treatment. 
Capability of Naevia 
medical 
in 
the 
management 
of 
patients 
with 
cardiovascular 
diseases 
Goal: Naevia medical is capable to manage 
patients with cardiovascular diseases 
[reference to sources] 
*Lorenzo Monserrat, Roberto Barriales-Villa, 
Fernando De Frutos, José María Larrañaga, 
Ángela López Sainz, Carlos Peña Gil, Belén 
Peiró, Nerea Mora, Lorena Gómez-Burgueño, 
Marta Fernández-Galindo, Rocío Blanco, Victoria 
Espejo, Iago Mosquera, Lidia María Carrillo, 
Francisco José Bermúdez, David López-Cuenca, 
Blanca 
Figueres, 
Alicia 
Ferradas, 
Javier 
Pumares, Carmen Zapata, Eduardo Villacorta, 
Nicolas López-Canoa, Eva Cabrera, María Isabel 
García-Álvarez, Elías Grande, Javier Loureiro, 
Eloy Sobrido, Óscar Vázquez, Valentina Capelli, 
Enrique Jiménez-Jáimez, Juan Jiménez-Jaímez, 
Pablo García-Pavía, Tomás Ripoll-Vera, Vicente 
Climent-Paya, Julián Palomino-Doza, y Juan 
Ramón Gimeno-Blanes. Development and pre-
clinical evaluation of an advanced decision 
support system for hypertrophic cardiomyopathy 
clinical management 
Vahanian, A., Beyersdorf, F., Praz, F., Milojevic, 
M., Baldus, S., Bauersachs, J., Capodanno, D., 
Conradi, L., De Bonis, M., De Paulis, R., Delgado, 
V., Freemantle, N., Gilard, M., Haugaa, K. H., 
Jeppsson, A., Jüni, P., Piérard, L., Prendergast, 
B., Sádaba, J. R., . . . Sharipov, I. (2021). 2021 
ESC/EACTS Guidelines for the management of 
valvular heart disease. European Heart Journal, 
Preclinical 
- 
A pre-clinical study of simulated cases in patients with valvular heart disease, 
using the "2021 ESC/EACTS Guidelines for the management of valvular heart 
disease", must validate the Naevia medical's capability in the management of 
patients with valvular heart disease*. 
Clinical 
- 
Clinical studies and registries must show that Naevia medical is capable to 
manage patients with cardiovascular diseases by providing healthcare 
professionals with recommendations and suggestions for knowledge related to 
diagnosis, prognosis and/or treatment.  

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43(7), 
561-632. 
https://doi.org/10.1093/eurheartj/ehab395 
Appropriate 
recommendations 
Increase  the number of appropriate 
recommendations by a factor of three 
Clinical 
- 
An observational study consisting on a clinical validation of a CDSS applied to 
retrospective clinical cases of cardiac patients with valvular heart disease, shall 
confirm an increase in the number of appropriate recommendations by a factor 
of three over the recommendations generated with conventional management 
without the support system. 
Inappropriate 
recommendations 
Reduction 
of 
inappropriate 
recommendations  
Clinical 
- 
An observational study consisting on a clinical validation of a CDSS applied to 
retrospective clinical cases of cardiac patients with valvular heart disease, shall 
confirm that Naevia medical, compared to conventional management, is able to  
significantly 
decreased 
the 
number 
of 
inappropriate 
and 
missing 
recommendations in all the domains analyzed. 

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Concordance 
between 
expert 
evaluators 
Professionals agreement 
[reference to sources] 
*Joshi, A., S. Kale, S. Chandel and D. K. Pal 
(2015). 
"Likert 
Scale: 
Explored 
and 
Explained." Current Journal of Applied 
Science and Technology 7: 396-403. 
Clinical 
- 
 The panel’s agreement with the CDSS was very high, with a mean Likert scale 
score above 4 in all domains (STD: 4.53, LTD: 4.68, CMT: 4.58, ITI: 4.62). The 
probability of strongly agreeing was significantly higher than the probability any 
of the other categories (z.ratio (5|1)= 30.88, p-value< 0.001, z.ratio (5|2)= 31.67, 
p-value < 0.001, z.ratio (5|3) = 30.21, p-value< 0.001, z.ratio (5|4)= 22.54, p-
value< 0.001).      
- 
 
Safety of Naevia medical 
Misdiagnosis 
caused by product's 
error 
Incidence 
with 
SW 
System 
not 
> 
documented incidence with similar devices 
Clinical 
- 
Incidents and adverse events (if any) related to the DUE and linked to 
misdiagnosis caused by product’s error in the DUE do not result in serious harm, 
injury or impairment to the patient. 
o 
No serious clinical complications or other effects linked to misdiagnosis 
caused by product’s error in the DUE are observed in the clinical studies 
and registries performed with the DUE. 
- 
Incidents and adverse events (if any) related to the DUE and linked to 
misdiagnosis caused by product’s error in the DUE do not frequently result in 
serious harm, injury or impairment to the patient. 
o 
Output from ongoing review of incidents and adverse events (if any) 
related to the DUE and similar devices confirms a low incidence of 
clinical complications or other effects linked to misdiagnosis caused by 
product’s error. 
Incidence 
with 
SW 
System 
not 
> 
documented incidence with similar devices 
Clinical 
- 
Incidents and adverse events (if any) related to the DUE and linked to 
misdiagnosis caused by user’s error in the DUE do not result in serious harm, 
injury or impairment to the patient. 

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Misdiagnosis 
caused by user's 
errors 
o 
No serious clinical complications or other effects linked to misdiagnosis 
caused by user’s error in the DUE are observed in the clinical studies 
and registries performed with the DUE. 
- 
Incidents and adverse events (if any) related to the DUE and linked to 
misdiagnosis caused by user’s error in the DUE do not frequently result in serious 
harm, injury or impairment to the patient. 
o 
Output from ongoing review of incidents and adverse events (if any) 
related to the DUE and similar devices confirms a low incidence of 
clinical complications or other effects linked to misdiagnosis caused by 
user’s error in these systems. 
Patient's 
privacy 
and data security 
Incidence 
with 
SW 
System 
not 
> 
documented incidence with similar devices 
Clinical 
- 
Incidents and adverse events (if any) related to the DUE and linked to patient's 
privacy and data security issues in the DUE do not result in serious harm, injury 
or impairment to the patient. 
o 
No serious clinical complications or other effects linked to patient's 
privacy and data issues in the DUE are observed in the clinical studies 
and registries performed with the DUE. 
- 
Incidents and adverse events (if any) related to the DUE and linked to patient's 
privacy and data security issues in the DUE do not result in serious harm, injury 
or impairment to the patient. 
o 
Output from ongoing review of incidents and adverse events (if any) 
related to the DUE and similar devices confirms a low incidence of 
clinical complications or other effects linked to patient's privacy and data 
security issues in these systems. 

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Recommendations 
that could lead to a 
serious 
adverse 
event 
Naevia 
medical 
does 
NOT 
provide 
recommendations that could lead to a 
serious adverse event shall be identified as 
patient safety risks, including: 
- 
Possibility of death. 
- 
The need for a medical or surgical 
intervention that would not otherwise be 
necessary. 
- 
Omission of critical recommendations 
that could adversely affect the patient's 
prognosis 
Clinical 
- 
An observational, study consisting on a clinical validation of a CDSS applied to 
retrospective clinical cases of patients with valvular heart disease; must 
determine whether Naevia medical, when faced with real clinical cases, issues 
inappropriate recommendations that may represent a risk to patient safety. 
Recommendations that could lead to a serious adverse event shall be identified 
as patient safety risks, including: 
o 
Possibility of death. 
o 
The need for a medical or surgical intervention that would not otherwise 
be necessary. 
o 
Omission of critical recommendations that could adversely affect the 
patient's prognosis.  

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The benefit-risk balance is focused on the assessment of: 
 
- 
History of safe use of similar devices. 
- 
The residual clinical risks when using the device as intended by the manufacturer 
should not be higher than those already described in the literature and post-
commercialization experience for similar devices already available on the market.  
- 
The preclinical and clinical studies performed for the DUE following the parameters 
included in the applicable standards are available and will support the safety profile 
of the DUE. 
- 
A market experience search regarding the history of safe use of similar devices will 
be performed and will be detailed in the Literature Search Report in order to identify 
the possible clinical risks arising from the use of similar products that are already 
available in the market. 
 
The clinical benefits expected by the use of the DUE are detailed in Section 7 of this 
Clinical Evaluation Plan.  
Possible risks arising from the use of the device have been identified in the Risk 
Management Report and the benefit/risk analysis related are detailed in sub-section 10.1 
of this Clinical Evaluation Plan.  
 
12. Benefit-risk issues relating to the specific components – use of 
pharmaceuticals, non-viable animal, or human tissue. 
The device under evaluation does not include pharmaceuticals, medicinal products or 
non-viable animal or human tissue. 
 
13. Required level of clinical evidence  
As outlined previously, literature search and review shall be conducted to establish the 
state of the art. Literature search is also expected to yield up-to-date information 
concerning current alternatives and standard of care. 
Taking into account the characteristics of the device and its intended purpose, it is 
expected that robust, published clinical data concerning similar devices shall be found 
and can be obtained in order to incorporate it fully into the technical documentation. 
Equivalence with one of these similar devices is anticipated enabling the leveraging and 
use of this data to establish sufficient clinical evidence. Scientific quality of the available 
data shall be assessed during the appraisal stage of the clinical evaluation. Appraised 
datasets that qualify as clinical data shall be the basis for analysis to determine if the 
available information provides sufficient clinical evidence. 
 

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In order to define the required level of clinical evidence, reference is taken from what is 
established in Appendix III of Guidance document MDCG 2020-6. Note that this 
guidance document refers to legacy devices (medical devices previously CE marked 
under Directives 93/42/EEC), thus it is only considered to establish the hierarchy of the 
clinical evidence. The level of clinical evidence found may be ranked (e.g., Rank 1 to 
Rank 12 where Rank 1 are results of high-quality clinical investigations, Rank 4 are 
Outcomes from studies with potential methodological flaws but where data can still be 
quantified and acceptably justified, and Rank 12 are Preclinical and bench testing / 
requirements to standards) and are likely to vary between: 
- 
High Level (Rank 1 to Rank 4): Includes data sourced from clinical 
investigation(s) with the DUE itself or data sourced from other clinical studies with 
the DUE itself where the data can be quantified, and it is acceptability justified. 
 
- 
Medium Level (Rank 5, 6): Includes data sourced from clinical investigation(s) 
or other clinical studies with a device demonstrated to be equivalent to the DUE. 
 
- 
Low Level (Rank 7, 8, 9): Includes data sourced from curated complaints and 
vigilance data or structured, proactive PMS data pertaining to the DUE. Case 
reports on the DUE. Clinically relevant information pertaining to similar (but non-
equivalent) devices. 
 
- 
Non-clinical (Rank 10, 11, 12): Evaluation of the state of the art based on data 
from similar devices. Compliance to non-clinical elements of common 
specifications. Simulated use, animal or cadaver testing involving end-users. Pre-
clinical and bench testing showing compliance to standards. 
For this device, taking into account the device risk class, characteristics and intended 
purpose, it is anticipated that HIGH level clinical evidence can provide enough specific 
information for conformity assessment purposes. 
 
14. Clinical development plan (CDP) 
The purpose of this section is to establish a plan indicating the progression of our clinical 
development as referred to in Annex XIV Part A of MDR with an indication of milestones 
and a description of potential acceptance criteria.  
Any clinical investigation envisaged shall be planned and conducted in line with 
regulatory and ethical requirements applicable in the country where the investigation is 
to be performed and in accordance with good clinical practice as set out in standard ISO 
14155 (ISO/TC194 2021). 
 
 
 

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Overview 
This is the first Clinical Evaluation Plan according to the Regulation (EU) 2017/745 on 
medical devices, since the DUE, NAEVIA MEDICAL, has not been on the market before 
(2017). 
  
Clinical Investigations Undertaken 
The following tables summarize the clinical investigations that have been placed for the 
DUE: 
Main clinical investigation 
Title 
Clinical validation study of naevia medical, a decision support 
tool for valvular heart disease 
Registration number 
ClinicalTrials.gov ID NCT06392464  
Start and End 
2024 
Publication 
Pending 
Design 
pre-test-post-test of a randomly assigned group, using a longitudinal 
repeated measures approach and retrospective validation 
Duration 
3 months 
Centres 
Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS) 
Sponsor 
DILEMMA SOLUTIONS SL 
Main objective 
Demonstrate that the use of the naevia medical CDSS significantly 
increases appropriate recommendations based on clinical practice 
guidelines, and decreases the number of inappropriate recommendations 
received by the patient, compared to conventional management. 
Device 
under 
investigation 
Naevia medical 
Number of participants 
101  

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Title 
Clinical validation study of naevia medical, a decision support 
tool for valvular heart disease 
Inclusion criteria 
Retrospective clinical retrospective cases of patients with valvular heart 
disease will be selected in a stratified randomised manner from among: 
(i) patients presented at a medical-surgical session. 
ii) patients from the monographic consultation of valvopathies. 
 
Inclusion criteria: 
 
- Clinical cases of patients randomly selected from among those presented 
during the year 2022 in medical-surgical session with the main diagnosis 
of aortic valvular stenosis, aortic insufficiency, mitral stenosis and mitral 
insufficiency (greater than moderate grade). Randomisation will be 
stratified ensuring that 50% come from the medical-surgical session. 
- Clinical cases of patients randomly selected from clinical records of the 
Valvopathy Consultation seen in 
2022 with the main diagnosis of aortic stenosis, aortic insufficiency, mitral 
stenosis and mitral insufficiency (major degree). 
mitral regurgitation (greater than moderate grade). Randomisation will be 
stratified by ensuring that at least 40% of patients have aortic valve disease 
and 20% have mitral valve disease. 
- Age ≥18 years. 
Exclusion criteria 
- Subjects under 18 years of age. 
- Inability to anonymise the case. 
- Clinical cases of valvular heart disease involving complex congenital 
heart disease. 
- Clinical cases that do not meet the minimum set of data necessary for 
decision-making.  
- Clinical cases with a principal diagnosis other than valvular heart disease.  
- Presence of bacterial endocarditis. 
Primary and Secondary 
Outcomes 
It is considered clinically beneficial to increase the number of appropriate 
recommendations by 15% over the recommendations generated with 
general management without the support system. 
 
The baseline/pre-test value is the number of appropriate recommendations 
based on clinical practice guidelines during conventional management 
(without CDSS). Inappropriate recommendations as judged by the 
committee of expert assessors during conventional management will also 
be collected. 
 
The post-intervention/post-test value is the number of appropriate 
guideline-based recommendations after activation of the naevia medical 
decision support system. Inappropriate recommendations in the judgement 
of the committee of assessors during CDSS management will be collected. 
 
To calculate the percentage increase between two values we will use the 
following formula: 
 
Percentage increase = (Post-intervention value-Baseline value/Baseline 

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Title 
Clinical validation study of naevia medical, a decision support 
tool for valvular heart disease 
value) ×100%. 
Protocol reference 
TD01-06-1C-06-02 CIP_NCT06392464 
 
Other supporting studies/registries 
Title 
Development of knowledge-based clinical decision support 
system for patients included in colorectal screening program 
Registration number 
2020-640-1 
Start and End 
Start: September 1, 2020 
End: October 30, 2020 
Publication 
Lorenzo-Zúñiga V, Bustamante-Balén M, Pons-Beltrán V, Peña-Gil C. 
Development of knowledge-based clinical decision support system for 
patients included in colorectal screening program. Gastroenterol Hepatol. 
2022;45(6):419-423. doi:10.1016/j.gastrohep.2021.05.01    
(Lorenzo-Zuniga, Bustamante-Balen et al. 2022) 
Design 
Retrospective cohort study 
Duration 
2 months 
Centres 
Hospital Univesitari I Politecnic La Fe- Valencia 
Sponsor 
Hospital Univesitari I Politecnic La Fe- Valencia 
Main objective 
To develop a K-CDSS for the management of CRC screening patients, and 
to evaluate its efficacy. 
Device 
under 
investigation 
DILEMMA Solutions Platform© ( (naevia medical) 
Number of participants 
69 
Inclusion criteria 
Patients at a large referral center in Spain who underwent colonoscopy 
under CRC screening program. 
Exclusion criteria 
Patients with a personal history of CRC, inflammatory bowel disease (IBD), 
hereditary polyposis syndromes, prior colectomy, Boston Bowel 
Preparation Scale (BBPS) < 5 or an endoscopic report of poor preparation 
quality (inadequate visualization of polyps < 5 mm), incomplete 
colonoscopy (defined as failure to intubate the cecum), and polyp retrieval 
failure. 
Primary Outcomes 
The degree of agreement between expert physicians and nurses using a 
CDSS based on colonoscopy data was very high. There were no 
differences in erroneous recommendations with PoliCare CDSS (Kappa 
value 1.0). 

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Title 
Development of knowledge-based clinical decision support 
system for patients included in colorectal screening program 
Protocol reference 
TD01-06-1C-06-06 CIP_2020-640-1 
 
Title 
Development 
and 
pre-clinical 
evaluation 
of 
an 
advanced 
decisionsupport system for hypertrophic cardiomyopathy clinical 
management 
Protocol code 
2021-2-19-HCUVA 
Publication 
NA 
Design 
Observational, multicenter study for the validation of a digital clinical 
decision support tool (CDSS) applied to clinical cases of patients with 
hypertrophic cardiomyopathy. 
Duration 
9 months 
Centres 
Hospital Virgen de la Arrixaca 
Sponsor 
Amicus Therapeutics. 
Main objective 
To evaluate the degree of concordance between management decisions 
of experts (Standard Output) versus non-experts supported by the CDSS 
(non-experts+CDSS output), in patients with HCM. 
Device 
under 
investigation 
Naevia medical 
Number of participants 
4101 rules (algorithms) covering HCM and related problems (heart failure, 
atrial 
fibrillation, 
valvular 
heart 
disease, 
arterial 
hypertension, 
dyslipidaemias, diabetes, coronary syndromes) were coded in the CDSS 
and were evaluated in 60 cases (internal validation). 
Inclusion criteria 
Patients evaluated consecutively with a main diagnosis of hypertrophic 
cardiomyopathy in which the available clinical information is considered 
sufficient 
for 
the 
generation 
of 
diagnostic 
and 
therapeutic 
recommendations. 
Exclusion criteria 
Absence of relevant clinical information for medical decisions. Problems for 
case anonymization. 
Primary Outcomes 
External validation phase 1: For each question (n=42), the experts (n=10) 
provided a total of 56 answers (28 with and 28 without CDSS) and non-
experts (n=9) provided 62 answers (31 with and 31 without CDSS), to sum 
a total of 2184 individual questions (42 per case in 26 cases evaluated 
twice) and 4956 answers (including 248 answers that were considered the 
references, and 4708 answers to be evaluated). In experts, the number of 
right answers improved with CDSS on from 1020 to 1032 (1.1%) and the 
number of wrong answers decreased from 156 to 144 (7.6%) (p=0.46). In 
non-experts, the number of right answers improved from 1074 to 1094 

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Title 
Development 
and 
pre-clinical 
evaluation 
of 
an 
advanced 
decisionsupport system for hypertrophic cardiomyopathy clinical 
management 
(1.9%) and the number of wrong answers decreased from 228 to 208 
(8.7%) (p=0.29). External validation phase 2: 21 additional cases were 
evaluated by 4 experts (2 with, 2 without CDSS) and 4 non-experts (2 with, 
2 without CDSS). The answers showed low concordance both inter and 
intra group, with and without CDSS (kappa <5 for all evaluated answers). 
Protocol reference 
TD01-06-1C-06-01 CIP_2021-2-19-HCUVA 
 
Additionally, Naevia Medical's platform has been or is being used in other significant 
registries, such as: 
- 
The "REDMIO20: Spanish Registry of Dilated Cardiomyopathy due to RBM20 
Gene Mutation," a multicenter registry coordinated by Dr. Eduardo Villacorta from 
the Salamanca University Assistance Complex, which included 168 cases up to 
December 11, 2023. 
o Protocol reference: TD01-06-1C-06-03 CIP_REDMIO 
 
- 
The "PROCeso asistencial integrado de Estenosis Aórtica grave Sintomática 
(PROCEAS)" 2020/405 registry, led by Dr. Violeta González-Salvado from the 
Santiago de Compostela University Clinical Hospital, with 991 cases of aortic 
stenosis registered up to December 11, 2023. 
o Protocol reference: TD01-06-1C-06-04 CIP_PROCEAS 
 
Ongoing Clinical Investigations  
 
- 
Preliminary Study on Ischemic Heart Disease: This study focuses on the "2023 
ESC Guidelines for the management of acute coronary syndromes". As a result of 
an agreement with the European Society of Cardiology, simulations of 731 cases 
have been created up to December 11, 2023, along with 22 representative cases. 
This study is especially important to validate the system's capability in managing 
patients with ischemic heart disease, a highly prevalent area in cardiovascular 
medicine. 
o Protocol reference: TD01-06-1C-06-08 CIP_ESC Guidelines. Preliminary 
Study on Ischemic Heart Disease 
 
- 
Case registry and quality indicators in cardiac rehabilitation centers:  
o Protocol reference: TD01-06-1C-06-05 CIP_ReCardio. 
 
 

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Future investigations / PMCF 
PMCF Studies will be performed with the aim of potentially widen the indications of 
Naevia medical. 
- 
Preliminary Study on Breast Cancer: Studies based on the "ESMO Clinical 
Practice Guidelines for diagnosis, treatment, and follow-up" for early breast cancer 
have been conducted. This study is crucial to ensure that the Naevia Medical system 
can effectively manage breast cancer cases, following the established clinical 
guidelines. 
- 
Diabetes Mellitus: Validation study for the management of type 2 diabetes: Based 
on the ADA (American Diabetes Association) guidelines. 
 
Active Vigilance and Monitoring 
After the device is placed on the market, strategies, and methods to systematically 
collect, summarize, and assess post-market clinical data shall be defined in the specific 
Post-market Surveillance (PMS) plan, as well as in the Post-market Clinical Follow-up 
(PMCF) Plan. 
The purpose is to proactively collect and evaluate clinical data from the use in humans 
of the device within its intended purpose with the aim of confirming the safety and 
performance throughout the expected lifetime of the device, of ensuring the continued 
acceptability of identified risks and of detecting emerging risks based on factual 
evidence. 
Feedback information from PMS and PMCF shall be evaluated to determine any 
necessary product changes or improvements.  
The impact of any proposed changes or improvements shall be reviewed and considered 
in the annual Risk Management and Clinical Evaluation reviews. Also, design changes 
shall be evaluated for impact on the Certificate(s) issued by the Notified Body. All such 
changes shall be controlled and documented updating the corresponding sections of this 
Technical Documentation. 
 
 
 
 
 
 
 
 

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15. References  
• 
MDCG 2020-6 Guidance on “Clinical evidence needed for medical devices 
previously certified under Directives 93/42/EEC and 90/385/EEC (legacy medical 
devices)” 
• 
MDCG 2020-5 Guidance on “Clinical evaluation – Equivalence. A guide for 
manufacturers and notified bodies” 
• 
MEDDEV 2.7/1 rev.4 (2016) “Clinical evaluation – A guide for manufacturers and 
notified bodies under Directives 93/42/EEC and 90/385/EEC” 
1. Muhiyaddin, R., Abd-Alrazaq, A. A., Househ, M., Alam, T., & Shah, Z. (2020). The 
Impact of Clinical Decision Support Systems (CDSS) on Physicians: A Scoping 
Review. 
Studies 
in 
health 
technology 
and 
informatics, 
272, 
470–473. 
https://doi.org/10.3233/SHTI200597  
2. Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & 
Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, 
risks, 
and 
strategies 
for 
success. 
NPJ 
digital 
medicine, 
3, 
17. 
https://doi.org/10.1038/s41746-020-0221-y  
3. Kwan, J. L., Lo, L., Ferguson, J., Goldberg, H., Diaz-Martinez, J. P., Tomlinson, G., 
Grimshaw, J. M., & Shojania, K. G. (2020). Computerised clinical decision support 
systems and absolute improvements in care: meta-analysis of controlled clinical 
trials. BMJ (Clinical research ed.), 370, m3216. https://doi.org/10.1136/bmj.m3216  
4. Jensen, R. V., Hjortbak, M. V., & Bøtker, H. E. (2020). Ischemic Heart Disease: An 
Update. 
Seminars 
in 
nuclear 
medicine, 
50(3), 
195–207. 
https://doi.org/10.1053/j.semnuclmed.2020.02.007  
5. Writing Committee Members, Otto, C. M., Nishimura, R. A., Bonow, R. O., Carabello, 
B. A., Erwin, J. P., 3rd, Gentile, F., Jneid, H., Krieger, E. V., Mack, M., McLeod, C., 
O'Gara, P. T., Rigolin, V. H., Sundt, T. M., 3rd, Thompson, A., Toly, C., ACC/AHA 
Joint Committee Members, O'Gara, P. T., Beckman, J. A., Levine, G. N., … Woo, Y. 
J. (2021). 2020 ACC/AHA guideline for the management of patients with valvular 
heart disease: A report of the American College of Cardiology/American Heart 
Association Joint Committee on Clinical Practice Guidelines. The Journal of thoracic 
and 
cardiovascular 
surgery, 162(2), 
e183–e353. 
https://doi.org/10.1016/j.jtcvs.2021.04.002  
6. Graham, T. A., Kushniruk, A. W., Bullard, M. J., Holroyd, B. R., Meurer, D. P., & Rowe, 
B. H. (2008). How usability of a web-based clinical decision support system has the 
potential to contribute to adverse medical events. AMIA ... Annual Symposium 
proceedings. AMIA Symposium, 2008, 257–261.