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From figures to findings - statistics in practice

At MedxTeam, the focus is on clinical data. In this context, as CRO, we not only carry out clinical tests (studies) with medical devices in accordance with MDR and ISO 14155, but also offer support in the statistical planning and evaluation of the study data. In this article, we explain in this article an overview of the most important statistical concepts in clinical studies, starting with basic explanations for practical examples and recesses for advanced users.

Abbreviations

GCP Good Clinical Practice

MDR Medical Device Regulation; EU Regulation 2017/745

Underlying regulations

EU Regulation 2017/745 (MDR)
General Data Protection Regulation (GDPR)
Medical Device Environmental Constitution (MPDG)
ISO 14155

1 Introduction

Statistical methods play a central role in the clinical testing of medical devices. They are the key to analyzing data, the interpretation of results and the fulfillment of regulatory requirements. The following topics are dealt with in this article:

  • Confidence intervals
  • Error 1. and 2nd order
  • Acceptance criteria
  • Boxing plot
  • Forest plot
  • Connected data
  • Sensitivity / specificity

2. How was that again with the confidence interval?

The confidence interval indicates the area in which an estimated parameter, such as the mean or an effect size, lies with a defined probability. It quantifies the uncertainty about an estimate and is therefore an indispensable tool of statistics.

The confidence interval provides information about how precisely an estimate is. The narrower the interval, the safer we can be that the real value is close to the estimated value. Conversely, a wide interval indicates greater uncertainties. A confidence interval is often given with a probability of trust of 95 %. This means that the true value in 95 out of 100 cases is within the specified area if the study is repeated under identical conditions.

Example: 

Suppose a study shows a medium wound healing time of 10 days with a confidence interval of [8, 12] at 95 % level of trust. This means that the real mean with 95%probability is within this area.

2.1 deepening

How do confidence intervals work and why are they decisive?

  • The basic idea of ​​the confidence interval The confidence interval is based on the uncertainty that is associated with every sample. It helps to quantify this uncertainty by specifying an area in which the true value of a parameter is very likely. The more data we collect and the lower the spread of the data, the more precise (i.e. narrower) the interval becomes.
    • Interpretation of a 95% confidence interval: It does not mean that the true value is "with 95% probability" in the interval. Instead, the statement refers to the fact that if we repeat the process of data collection infinitely often, the interval includes the true value in 95% of cases.
  • Which factors influence the width of a confidence interval? The width of the interval depends on three main factors:
  • Sample size: Larger samples provide precise estimates because the influence of random fluctuations is reduced. This leads to closer confidence intervals. With small samples, the intervals are wider because the uncertainty is greater.
  • Variability of the data: With a greater spread of the data (i.e. if the values ​​spread heavily for the mean), the intervals are further because the uncertainty over the actual value increases.
  • The level of trust selected: higher levels of trust (e.g. 99% instead of 95%) lead to wider intervals because more uncertainty is taken into account. However, a lower level of trust (e.g. 90%) results in closer intervals.

Practical consequences: A particularly wide interval indicates that additional data is necessary in order to better narrow the true value.

2.2 Alternative methods for estimating confidence intervals

The classic method requires that the distribution of the underlying data is normal and the sample is sufficiently large. In the event of deviations from these assumptions or for small samples, alternative approaches can be used:

  • Boat trapping:This method is ideal if the normal distribution assumption is injured or with small samples. A large number of samples are repeatedly drawn from the existing data. For each of these samples, the parameter of interest (e.g. the mean value) is calculated. The distribution of these estimated values ​​then serves as the basis to derive the confidence interval.
    • Advantages: robust over distribution injuries; flexibly applicable.
    • Application example: In the case of non-normal distributed data, such as strong asymmetrical blood pressure values, boot trapping provides precise estimate.
  • Bayesche confidence intervals (credit intervals):In contrast to classic statistics, the Bayesian approach works with probabilities. Here prior knowledge of the parameter is brought in by a so-called prior distribution. This is combined with the observation data (Likelihood) to calculate the posterior distribution. The credit interval then indicates the area in which the true value is with a certain probability.
    • Advantages: integration of prior knowledge; Better interpretability with small samples.
    • Application example: If earlier studies show that a medical device typically causes a wound healing period of about 10 days, this information can be included in the analysis in order to reduce uncertainty.

2.3 Practical relevance of confidence intervals in clinical research

  • Clinical relevance versus statistical significance: confidence intervals provide more information than a P value. While a P value only indicates whether an effect is statistically significant, the confidence interval also shows whether the effect is clinically significant. Example: A medical device could cause a statistically significant reduction in the wound healing period, but this reduction may be so low that it is clinically irrelevant.
  • Evaluation of uncertainties: In regulatory decisions, it is often checked whether the lower limit of the confidence interval is above a certain threshold value that is considered clinically significant.

3. Error 1. and 2nd order

Statistical tests cover the risk of errors, since decisions are made on the basis of stab test data that can only partially reflect reality. Errors 1. And 2nd order are therefore important concepts in statistics and particularly relevant in clinical research, where wrong decisions can have considerable consequences.

Two fault types can occur in statistical tests:

  • Error 1. Order (alpha error): This occurs when the null hypothesis is rejected even though it is true. This error is also referred to as "false alarm". Example: An ineffective medical device is classified as effective.
  • Error 2. Order (beta error): This error happens when the null hypothesis is retained, although the alternative hypothesis applies. This is often described as "overlooking an effect". Example: An effective medical device is not recognized.

The balance between these types of errors is a core task of planning clinical studies. The level of significance and test strength play a central role.

Example: 

A new medical device is tested. An alpha error would lead to approval of an ineffective product, an effective product could classify an effective product as ineffective.

Practical importance: While an alpha error is regulatory and economically problematic, a beta error can hinder medical innovation.

3.1 deepening

  • Connection between alpha and beta errors: There is a direct ratio between these two mistakes. If the significance level (alpha) is chosen more strictly to reduce the likelihood of an alpha error (e.g. from 0.05 to 0.01), the risk of a beta error often increases. Conversely: A loosening of the alpha value reduces the beta error, but increases the risk of recognizing incorrect effects.
  • Test strength: The test strength is a measure of how well a statistical test can discover an actual effect. A test strength of 80% means that a real effect is overlooked in 20% of cases (beta errors).
  • Influence factors on the test strength: sample size, effect size and the selected level of significance. A larger sample increases the likelihood of discovering small effects and reduces the beta error.
  • Adjustment for multiple analyzes:
  • Intermediate evaluations: In studies with regular data analyzes, the likelihood of an alpha error can increase, since with every analysis there is a chance to discover a random effect. Methods such as the O'Brien-fleming method use stricter limit values ​​in early evaluations to control the total error rate.
  • Bonferroni correction: This method divides the level of significance through the number of comparisons to keep the total error rate low. However, this is conservative and can reduce the test strength with a large number of tests.
  • Bayesian perspective:

Instead of using rigid levels of significance, Bayesian statistics evaluate probabilities. For example: How likely is it that an effect is greater than a clinically relevant threshold? This can lead to more flexible and interpretable results, especially for small samples.

  • ROC curves:

The Receiver Operating Characteristic (ROC) curve shows the trade-offs between sensitivity (true positive) and 1 specificity (wrong positive). It helps to identify threshold values ​​that minimize both alpha and beta errors.

4. Acceptance criteria

Acceptance criteria determine the conditions under which a clinical result is considered successful. They are crucial for the interpretation of study results and the decision as to whether a medical device is effective or safe.

Define acceptance criteria which results are required to achieve a specific goal. They influence the planning of the study, hypothesis formulation and ultimately the approval decision of a product.

Example: 

A medical device is developed to shorten the healing time after an operation. As an acceptance criterion, it is determined that the average healing time must be reduced by at least 20% compared to standard treatment. The study checked whether the confidence interval of the result exceeds this limit.

4.1 deepening

  1. Non-subdue, superiority and equivalence tests:
  • Non-subdness test: shows that the new product is no worse than existing treatment within an acceptable tolerance limit.
  • Supervision test: proves that the product is significantly better.
  • Equivalence test:Check whether the product is similar within a specified area (e.g. ± 10%).
    1. Bayesche approaches:
  • Instead of establishing a fixed threshold for acceptance, Bayesch methods calculate the likelihood that the true effect is greater than a pre -defined threshold. This allows a dynamic and probability -theoretical consideration.
    1. Clinical importance:
  • A statistically significant effect does not automatically meet an acceptance criterion, since the clinical relevance must also be assessed. Example: A pain reduction of 1% could be statistically significant, but could not be clinically significant.
    1. Cost-benefit assessment:
  • Strict acceptance criteria can increase the quality of the assessment, but often require larger samples, which increase the costs and duration of the study.

5. How do I read a box plot?

A box plot, also called the box graphic, is a versatile statistical tool that visualizes the distribution of data in a simple way. It helps to recognize central tendencies, variability and potential outliers at a glance and is particularly useful when comparing groups.

A box plot is compact to distribute a data record. The most important components are:

  • Median: The line in the middle of the box represents the central value of the data.
  • Quarters: The lower edge of the box is the 1st quartile (Q1), the upper edge of the 3rd quartile (Q3).
  • Interquartile distance (IQR): The area between Q1 and Q3 includes the middle 50% of the data.
  • Whisker: The lines above and below the box indicate the data values ​​outside the IQR, up to a defined limit (often 1.5 times of the IQR).
  • Extra: Data points that are outside of this border are shown separately as points.

Example 

Let us imagine that we have data on the healing time of two patient groups (group A and Group B):

  • Group A has shorter healing times with low variability, which results in a compact box with short whiskers.
  • Group B shows bigger differences between the patients, which leads to a wider box and longer whiskers.

A direct comparison of the two box plots can quickly show which group is more homogeneous and whether there are extreme outliers.

5.1 deepening

  1. Detailed interpretation:
  • Median: indicates the central tendency of the data and is robust towards outliers.
  • IQR: shows the diversification of the middle 50% of the data and gives an impression of the variability.
  • Whiskers and outliers:Help to identify extreme values ​​that could possibly distort the analysis.
    1. Comparison of groups: Box plots are ideal for presenting differences between groups, e.g. B. to compare the effect of a medical device on different age groups. Differences in the height of the box or the whisker can indicate variability or systematic effects.
    2. Extended visualizations:
  • Violin plots: A combination of box plot and density plot that shows the entire distribution of the data. Particularly useful for multimodal distributions (e.g. two tips in the data).
  • Parallel boxing plots:Several box plots side by side make it easier to compare groups.
    1. Application in clinical studies:
  • Outlier analysis: In a clinical study, outliers could indicate patients who react exceptionally well or poorly to treatment. Such insights can provide information about individual differences that are important for further research.
  • Stratification:Box plots can be used to stratify and visually present data according to subgroups (e.g. age groups, gender).
    1. Robustness: Since the median and the quarters are insensitive to outliers, the box plot is particularly robust. Nevertheless, strong asymmetrical distributions (e.g. long "cocks" on one side) can be misleading. In such cases, alternative representations such as the violin plot can be helpful.

6. How do I read a forest plot?

A forest plot is an indispensable tool in the meta-analysis and enables the presentation and interpretation of the results of several studies or subgroups. It shows estimates and their confidence intervals in a uniform diagram.

The Forest Plot consists of:

  • Estimated points: These points or squares represent the effect (e.g. mean value, odds ratio) of each study or sub -group.
  • Confidence intervals: The horizontal lines indicate the uncertainty of the estimate.
  • Vertical line: This represents the "no effect" point, e.g. B. an odds ratio of 1 or an effect size of 0.
  • Overall effect: A diamond at the lower end shows the weighted average of all studies, with the width of the diamond representing the confidence interval.

Example

A meta-analysis examines the effectiveness of a patch on the wound healing time in various studies.

  • Study A shows a significant reduction in the wound healing period, with a confidence interval that is completely below the "no effect" line.
  • Study B has a broad confidence interval that includes both positive and negative effects, which indicates uncertainty in the results.
  • The overall effect (diamond) is also below the line, which indicates a significant effectiveness of the pavement.

6.1 deepening

  1. Analysis of heterogeneity:
  • Cochran's Q-Test: Check whether the variation between the studies is larger than expected by chance.
  • I² statistics: indicates the percentage of variability, which is explained by heterogeneity. A high value (e.g. over 50%) indicates that a random effects model makes more sense.
  1. Fixed-Effects vs. Random-EffectS models:
  • Fixed effects model: assumes that all studies measure the same true effect and only result in differences.
  • Random-EffectS model:Considering that studies can have different populations and conditions and allows greater variability between the studies.
    1. Bayess's Forest Plots:
  • Bayesian approaches use prior knowledge to better model uncertainty. The forest plot could visualize posterior distributions and credit intervals here, which enables a deeper interpretation.
  1. Interpretation in practice:
  • A forest plot can be used to evaluate the consistency of results. Studies whose confidence intervals do not cut the "no effect" line provide strong evidence. Different results of individual studies can be signs of methodological differences or specific population effects.

7. What are connected data?

Connected data are measurements that are not independent of one another. This often occurs in clinical studies when, for example, the same patient is measured several times (e.g. before and after treatment) or if there are observations within couples or groups (e.g. twins or devices that are tested on the same patient).

In the case of connected data, one measurement affects the other directly. The best known examples are before and after measurements or paired samples. In such cases, it is important to use statistical procedures that take this dependency into account, otherwise incorrect conclusions can be drawn.

  • Typical scenario: data collection before and after the treatment of a patient. Since both measurements come from the same patient, they are not independent.

Example

A study examines the effectiveness of a new wound association to accelerate healing after operations. The healing time is measured in the same patients before and after the application of the wound association. Since both measurements come from the same patient, they are connected. A simple comparison of the mean without taking the connection would lead to distorted results. Instead, a paired T-test should be used to correctly analyze the differences in the healing times.

7.1 deepening

  1. Why is dependency important? Independent data follow the basic assumption of many statistical tests. In the case of connected data, however, this dependence violates this assumption. A special analysis is therefore required to avoid distorted results.
  2. Suitable statistical procedures:
  • Paired T-Test: This test compares the mean values ​​of two connected groups by analyzing the differences between the couples.
  • Wilcoxon sign ranking test: This is the non-parametric alternative if the data has no normal distribution.
  • Linear mixed models (LMM): These models are particularly useful for complex study designs with several times or groups. You can analyze random effects (e.g. individual differences) and solid effects (e.g. treatment) at the same time.
  1. Variance-covariance structure: In advanced models such as the ANOVA with measurement repetitions, the dependency between the measurements must be modeled correctly. Different assumptions about the structure (e.g. Compound Symmetry or Autoregressive Structures) influence the results.
  2. Practical challenges:
  • Missing values: Connected data are particularly susceptible to bias if measured values ​​are missing. Methods such as multiple imputation or maximum liikelihood estimates can help minimize distortions.
  • Complexity: The analysis of connected data often requires specialized software and knowledge in advanced statistical methods.

8. Difference in sensitivity/specificity

Sensitivity and specificity are fundamental dimensions to evaluate the quality of a diagnostic test. You describe how good a test is able to recognize the sick and correctly exclude healthy people.

Sensitivity: the proportion of actually sick people who are recognized correctly by the test (true positive). It measures the ability not to overlook the sick.

Specification: the proportion of actually healthy that are correctly recognized as healthy (True negative). She describes how well the test can avoid false alarms.

Why is that important? A perfect test would have a sensitivity and specificity of 100%. In practice, however, compromises often have to be made, e.g. B. for mass screenings, in which a high sensitivity test is preferred in order not to overlook a patient.

Example

 Has a test for diagnosis of a rare disease:

  • 90% sensitivity: The test 90 recognizes from 100 people actually sick patients correctly; 10 are incorrectly classified as healthy.
  • 80% specificity: Out of 100 healthy people, 80 are correctly recognized as healthy; 20 are wrongly classified as sick.

8.1 deepening

  1. Connection with prevalence:
  • The positive and negative predictive values ​​(PPV and NPV) depend directly on the prevalence of the disease. A test with high sensitivity could provide many false-positive results at low prevalence.
  1. ROC curves and threshold values:
  • A receiver Operating Characteristic (ROC) curve shows how sensitivity and specificity change in different threshold values ​​of a test. The ideal threshold maximizes the sensitivity and specificity and minimizes false-positive and false-negative results. The area under the ROC curve (AUC) is a measure of the overall performance of the test.
  1. Trade offs between sensitivity and specificity:
  • Tests with high sensitivity (e.g. screening tests) often have a lower specificity and create more false-positive results. Combined test strategies (e.g. a sensitive screening test followed by a specific confirmation test) can improve diagnostic accuracy.
  1. Bayesche consideration:
  • Bayesian analyzes enable the probability that a patient is actually sick, based on a positive test result and the known prevalence. This helps to better inform diagnostic decisions.
  1. Practical applications:
  • Diagnostic tests such as Covid-19 anti-tests or mammography screenings.
  • Evaluation of new diagnostic devices or methods in clinical studies.

9. Conclusion

Statistical methods are indispensable tools in clinical research and the development of medical devices. They enable data to analyze precisely, quantify uncertainties and make well -founded decisions. From the calculation of confidence intervals to the avoidance of errors 1. And 2nd order to the interpretation of box plots and forest plots - the statistics offer a variety of techniques to improve the quality and meaningfulness of clinical studies. Through the targeted use of these methods, we can not only demonstrate the effectiveness and safety of medical devices, but also meet the regulatory requirements and ultimately optimize patient care. In a world in which data play an increasingly important role, the statistics remain an indispensable part of evidence -based medicine.

10. How we can help you

We are happy to support you with regard to the structure and implementation and use of a database -based system. As Cro, we also take over the complete data management via the EDC system and monitoring.

So we support you during your complete project with your medical device, starting with a free initial consultation, help with the introduction of a QM system, study planning and implementation to technical documentation - always with primary reference to the clinical data on the product: from the beginning until to the end.

Do you already have some initial questions?

You can find free initial consultation here : Free initial consultation

Database instead of Excel lists: Why a structured data storage in clinical studies with medical devices is decisive

At MedxTeam, the focus is on clinical data. In this context, as CRO, we not only carry out clinical tests (studies) with medical devices in accordance with MDR and ISO 14155, but also offer all other options and forms of data collection. We attach particular importance to structured and quality -assured data storage in order to create a reliable evidence base. Our expertise ranges from the planning and implementation of electronic data acquisition systems to the analysis of complex studies data. In this article, we explain in this article why a database is the key to efficient and regulatory data.

Abbreviations

GCP Good Clinical Practice

MDR Medical Device Regulation; EU Regulation 2017/745

Underlying regulations

EU Regulation 2017/745 (MDR)
General Data Protection Regulation (GDPR)
Medical Device Environmental Constitution (MPDG)
ISO 14155

1 Introduction

The implementation of clinical studies in the field of medical devices requires precise and ruling data acquisition and management. The data collected must not only be complete and correct, but also the regulatory requirements of the Medical Devices Ordinance (EU) 2017/745 (MDR-Medical Device Regulation), the Good Clinical Practice (GCP) and the General Data Protection Regulation (GDPR) . Nevertheless, many study centers continue to use Excel lists to manage study data-a practice associated with considerable risks and disadvantages.

In this blog post, the essential requirements for data storage in clinical studies are first explained. Then it is analyzed in detail to what extent Excel lists can meet these requirements-or not-and what advantages a database-based solution offers.

2. Requirements for data storage in clinical studies

Clinical studies with medical devices are subject to strict regulatory requirements, in particular the Medical Devices Ordinance (EU) 2017/745 (MDR-Medical Device Regulation) and the Good Clinical Practice (GCP) guidelines. Data protection requirements according to the General Data Protection Regulation (GDPR) also apply. The following criteria must therefore meet the rule -compliant data storage:

  • Audit trails for traceability of changes An audit trail is automatic logging of all changes to a database. He records who made which changes when. This ensures that all changes are transparent and understandable, which is essential for regulatory exams. Excel lists do not offer an integrated audit function, so that changes can be manipulated unnoticed or deliberately manipulated.
  • Roll -based access control to protect sensitive data not every user of a clinical study should have access to all data. Roll -based access control ensures that only authorized persons can view or edit certain data. For example, test doctors can view patient data, while statisticians can only process anonymized data records. Excel does not offer fine granular access control, so that sensitive data are often insufficiently protected.
  • Safe encryption and data backup A safe data storage requires encryption both during the transmission and in the storage ("data-at rest" and "data-in-transit"). Regular backups are also necessary to prevent data loss. While database systems support encryption and automatic backups by default, this functionality lacks Excel, which means that data is susceptible to security gaps and accidental deletion.
  • Standardized and validated data input Incorrect or inconsistent data can falsify the results of a clinical study. Databases enable a consistent and error -free data entry through defined input formats, mandatory fields and validation mechanisms. In Excel, such validations can only be limited and often feasible.

3. Excel lists vs. database systems

Excel lists are widespread in many study centers, but only insufficiently meet the above requirements. While they are suitable for simple spreadsheets, they reach their limits when processing large amounts of data and compliance with regulatory requirements. Database systems, on the other hand, offer a structured and safe environment for the administration of clinical studies.

Audit trails: Changes can be made at any time in Excel and saved without tracking. Any change is logged in a database system so that manipulations can be excluded.

Access control: Excel files can be protected with passwords, but they do not offer differentiated access control. Databases make it possible to define different user roles with specific permissions.

Security: Databases offer encryption and regular backups, while Excel files are susceptible to data losses and security violations.

Data quality: Excel does not allow comprehensive validation of inputs, while databases offer mechanisms to check and ensure data consistency.

Scalability: Excel reaches its performance limits for large amounts of data, while databases are designed for large studies and enable efficient processing.

The most important differences are summarized below.

Table 1

Table 1: Differences in the sales of the requirements

4. Additional challenges and risks in Excel lists

In addition to the above restrictions, the use of Excel lists harbors further risks:

  • Missing automation: Repeated manual data entries increase the error potential.
  • Difficulties in multi-user access: At the same time working on an Excel file can lead to inconsistencies and data loss.
  • High hidden costs: Errors in Excel lists often lead to additional workload, since manual corrections, double tests and research are necessary. This can lead to considerable time delays and additional costs.
  • No robust validation mechanisms: While modern database systems ensure that only valid and complete data can be entered, Excel only offers very limited options for validation. For example, incorrect or double entries cannot be reliably prevented.
  • High susceptibility to human errors: Excel tables are susceptible to accidental changes, overwriting or the unintentional deletion of important data.
  • A lack of securing data integrity: Data can be easily changed or manipulated without this being understandable, which can lead to problems with regulatory exams.

5. Advantages and disadvantages of Excel lists and database systems

When deciding between Excel lists and a professional database system, both the advantages and the disadvantages of both options should be carefully weighed.

Advantages of Excel lists

  • Simple use: Most users are familiar with Excel, which enables quick implementation.
  • Low costs: no additional software or license costs, since Excel is often already available.
  • Flexibility: tables can be created and adapted quickly.
  • No high technical effort: no elaborate IT infrastructure required.

Disadvantages of Excel lists

  • Missing audit trails: Changes are not understandable and can be changed unnoticed or deliberately changed.
  • Inadequate data security: no integrated mechanisms for encryption or differentiated access control.
  • Missing scalability: With growing data volume, the file becomes confusing and slow.
  • Increased susceptibility to errors: no automatic validation or plausibility tests possible.
  • Difficult cooperation: Simultaneous processing by several users can lead to inconsistencies or data loss.

Advantages of database systems

  • Regulatory compliance: meets the requirements of the MDR, GCP and GDPR through audit trails and access controls.
  • Higher data security: integrated encryption and differentiated authorization levels.
  • Automatic validations: Plausibility tests minimize errors and ensure data consistency.
  • Better scalability: Even large amounts of data can be processed and managed efficiently.
  • Efficient cooperation: Several users can access the system with clearly defined roles at the same time.

Disadvantages of database systems

  • Higher initial investment: Introduction and licensing can initially be expensive.
  • Training effort: Users have to familiarize themselves with the new software.
  • Technical infrastructure necessary: ​​usually requires IT support and regular maintenance.

Excel lists offer a quick and inexpensive possibility of data storage, but have considerable deficits in terms of security, scalability and traceability. It is particularly problematic that you do not meet the regulatory requirements of the MDR, GCP and GDPR. Missing audit trails, lack of access control and a high susceptibility to errors make you unsuitable for use in clinical studies.

Database systems, on the other hand, are specially designed to meet regulatory requirements. They offer automated test mechanisms, ensure safe and scalable data storage and enable efficient cooperation with clearly defined access rights. Although the introduction is initially associated with higher costs and training effort, the long -term advantages in relation to security, compliance and efficiency outweigh.

6. Best practice for the introduction of a database system

The conversion of Excel lists to a database-based solution requires careful planning and implementation. The following best practices can help:

  • The specific requirements of the clinical study should be analyzed before the introduction of a database system . This includes regulatory requirements, safety requirements and desired functions such as automated reports or interfaces to other systems.
  • Training of users: Successful implementation requires that all users are familiarized with the new software. This reduces errors and increases the acceptance of the system.
  • Data migration: Exist data from Excel must be carefully transferred to the new database. It should be checked whether inconsistencies or incorrect entries must be adjusted.
  • Safety and access concept: A comprehensive authorization system ensures that only authorized users can access certain data. In addition, security measures such as two-factor authentication should be implemented.
  • Regular validation and maintenance: The system should be regularly checked for functionality and compliance with regulatory requirements. This also includes updates and backups to ensure data integrity.
  • Documentation and inspection reading: All processes in connection with the use of the database should be documented in order to be able to demonstrate compliance in regulatory inspections.

7. Conclusion

At first glance, Excel lists may be a simple and inexpensive way to manage clinical studies. However, the associated risks - from a lack of data security to limited traceability to high hidden costs through error corrections - make an unsuitable solution for controlling clinical studies.

Database systems offer a structured, safe and scalable alternative. You ensure that the data storage is compliant, reduce sources of error and significantly improve the efficiency of clinical studies. The introduction of such a solution requires an initial investment in technology and training, but offers significant advantages with regard to security, compliance and cost reduction in the long term.

For study centers and sponsors, it is therefore advisable to rely on database-based systems at an early stage and to say goodbye to Excel lists as a primary tool for study data management.

8. How we can help you

We are happy to support you with regard to the structure and implementation and use of a database -based system. As Cro, we also take over the complete data management via the EDC system and monitoring.

So we support you during your complete project with your medical device, starting with a free initial consultation, help with the introduction of a QM system, study planning and implementation to technical documentation - always with primary reference to the clinical data on the product: from the beginning until to the end.

Do you already have some initial questions?

You can find free initial consultation here : Free initial consultation

Quality management for Class I medical devices

At medXteam, the focus is on clinical data. In this context, as CRO we not only carry out clinical trials with medical devices in accordance with MDR and ISO 14155, but also offer all other options and forms of data collection. This time it is not about the topic of clinical data, but rather about a basis for medical device manufacturers in general and in particular the manufacturers of Class I products. The MDR requires a quality management system (QMS) for all manufacturers of medical devices. Class I manufacturers must now also have one.

What are the special features? Are there any differences to the manufacturers of products with a higher classification? We will now devote ourselves to this topic in this blog post.

Abbreviations

MDR Medical Device Regulation; EU Regulation 2017/745

QMS quality management system

Underlying regulations

EU Regulation 2017/745 (MDR)
Medical Devices Implementation Act (MPDG)

1 Introduction

The development and manufacture of medical devices are subject to strict regulations to ensure safety and effectiveness. The Medical Device Regulation (MDR) sets clear requirements, particularly for manufacturers of Class I medical devices, which are often classified as low-risk. Article 10 (9) of the MDR requires a quality management system (QMS) for all medical devices - regardless of their risk class.

2. Requirements for a quality management system (QMS)

In the following sections we will now take a closer look at how an effective and compliant QMS for Class I products is set up and operated in order to meet the requirements of the MDR. From the basic principles to practical implementation, you will gain a comprehensive insight into the special features and challenges of quality management for this product class.

In the following sections we will now take a closer look at how an effective and compliant QMS for Class I products is set up and operated in order to meet the requirements of the MDR. From the basic principles to practical implementation, you will gain a comprehensive insight into the special features and challenges of quality management for this product class.

2. Requirements for a quality management system (QMS)

2.1 Requirements of Article 10 (9) MDR

Article 10 (9) of the MDR describes the comprehensive requirements for a quality management system (QMS) that manufacturers of medical devices, including Class I devices, must implement and maintain. The aim of the QMS is to ensure that the requirements of the regulation are continuously met, even in series production.

An essential part of this approach is the ability to take changes in product design, properties or relevant normative requirements into account in a timely and appropriate manner. This also includes the continuous improvement of the QMS, which is based on the risk classes and specific features of the products.

Comprehensive QMS requirements

The quality management system must cover all organizational areas of a manufacturer that influence the quality of processes, procedures and products. This applies, among other things:

  • Structure and responsibilities: Clear definition of tasks and responsibilities within the company.
  • Processes and procedures: Development and application of effective processes for all relevant business areas.
  • Management resources: ensuring the availability of expertise, human resources and financial resources.

Minimum aspects to consider

According to MDR, the QMS must cover a variety of aspects including:

  • Regulatory compliance: Adhering to conformity assessment procedures and managing product changes.
  • Risk management: Implementation of a systematic risk management process in accordance with Annex I, Section 3 of the MDR.
  • Clinical evaluation: Conduct and update clinical evaluations and post-marketing clinical follow-up.
  • Product realization: Covering all phases, from planning to manufacturing to deployment.
  • Post-market surveillance (PMS): Building a robust system to collect and analyze market surveillance data.
  • Communication: Ensuring the exchange of information with authorities, notified bodies and other interest groups.

Importance for manufacturers of Class I products

Although the QMS of Class I manufacturers does not need to be certified, its implementation and ongoing maintenance is critical. It serves as evidence of compliance with regulatory requirements and forms the basis for safe and efficient product development and delivery.

2.2 Requirements of ISO 13485

ISO 13485 is the internationally recognized standard for quality management systems (QMS) in the field of medical devices. It already existed under the Medical Devices Directive (MDD) and forms an essential basis for compliance with regulatory requirements. With the introduction of the MDR, ISO 13485 remains of central importance: A QMS must both meet the MDR requirements and continue to comply with those of ISO 13485.

ISO 13485 focuses on meeting regulatory and customer-related requirements throughout the life cycle of a medical device - from development to market support. The core requirements include:

  • Risk management: Systematic identification, assessment and control of risks throughout the entire product life cycle.
  • Process-oriented approach: Structuring the QMS along defined processes that are linked to one another and focused on quality and safety.
  • Documented procedures: Clear definition and documentation of all essential procedures and processes.

The standard requires a comprehensive system aimed at continuous improvement and compliance with safety and performance standards. The requirements of the standard are therefore divided into different process groups, which together ensure an effective QMS.

Leadership processes

Management processes create the strategic basis for the QMS and contribute to continuous improvement:

  • Management review: Regular review of the QMS by management to ensure its suitability, adequacy and effectiveness.
  • Corrective and preventive action (CAPA): Procedures for systematically investigating and correcting nonconformities and preventing future errors.
  • Internal audits: Scheduled assessments of QMS processes to identify weak points and highlight potential for improvement.

Core processes

Core processes are directly related to the development, manufacture and provision of medical devices, for example (list is not complete):

  • Change management: Control of product or process changes to ensure that all requirements are still met.
  • Sales: Control and documentation of the provision of products, including compliance with delivery conditions and regulatory requirements.
  • Complaints: Handling customer feedback with root cause analysis and evaluation and, if necessary, transferring it to further processes (change management, problem solving)
  • Purchasing: Purchase of products from suppliers, selection and monitoring of suppliers to ensure that purchased materials or services meet the specified requirements.
  • Development: Structured planning, execution and monitoring of the development process, including review and validation of product designs.

Support processes

Support processes ensure the basis for the smooth operation of the QMS. This includes z. B. Document steering: It serves to ensure topicality, availability and traceability of all quality -relevant documents. This includes the release, distribution and monitoring of changes in the documentation.

In addition to the MDR, ISO 13485 ensures that a QMS not only meets the MDR requirements, but also offers a proven basis for quality, security and regulatory conformity. Management, core and support processes are closely interlinked and form a robust system that enables manufacturers to meet the high standards of the medical device industry.

3. Requirements of the MDR and ISO 13485 for manufacturers of class I products

Manufacturers of class I products are faced with the task of building a fully extensive quality management system (QMS) that meets both the requirements of the MDR and ISO 13485. Despite the comparatively low risk class of these products, expectations of the structure and implementation of the QMS are not less than with higher risk classes.

An effective QMS requires manufacturers to define, implement and actively live all required processes. This includes in particular:

  • Management assessment: regular review by the management level to ensure the adequacy and effectiveness of the QMS.
  • Internal audits: Systematic control and evaluation of internal processes to identify weaknesses and use potential for improvement.

In addition to the basic structuring of the QMS, the operational processes are of central importance, including:

  • Advanced processing and customer feedback: An effective system for recording, analyzing and processing complaints and feedback, which serves as the basis for improvement measures.
  • Correction and preventive measures (CAPA): Processes for the analysis of errors, correction of causes and prevention of future problems.
  • Monitoring according to the placing on the market (PMS): Building a robust PMS system according to MDR articles 83, which also includes the vigilance, i.e. the message and processing of serious occurrences.

The close connection between QMS and technical documentation plays a central role. Data and insights from processes such as PMS, complaint processing and vigilance must flow directly into the technical documentation in order to ensure their topicality and completeness. This is crucial for the continuous conformity of the product with the regulatory requirements.

For manufacturers of class I products, the implementation of the MDR and ISO-13485 requirements means that they have to operate a comprehensive and dynamic QMS as manufacturers of higher risk classes. Active care and the "life" of the system - through regular ratings, internal audits and the consistent implementation of operational processes - is just as crucial as the "armoring" for inspections by the surveillance authorities, which are now carried out regularly at manufacturers of class I products to ensure that all requirements are met here too.

Such a QMS is not certified by the named area, but is regularly inspected by the responsible authority.

4. Conclusion and conclusion

The requirements for a quality management system (QMS) are the same for all manufacturers of medical devices - regardless of their risk class. Manufacturers of class I products are therefore faced with the same responsibility as companies that develop products of higher classes. The requirements of the MDR and ISO 13485 require a full QMS that not only has to be documented but actively lived.

However, a significant difference for class I manufacturers is that your QMS does not have to be certified by a notified area. Although this means saving costs and resources compared to higher -class products, this does not change the obligation to effectively build up the QMS, to regularly check and continuously improve.

Monitoring by authorities remains an integral part of the regulation. Official inspections and the examination of the technical documentation ensure that the requirements are observed. A QMS that not only exists formally but is actively integrated into the processes forms the basis for the permanent conformity and marketability of the products.

For manufacturers of class I products, it is therefore crucial to establish and maintain a functioning QMS. The lack of certification by a named area must not hide the fact that the requirements are comprehensive and must be met in full. A well -structured, actively used QMS is not only a regulatory obligation, but also a decisive factor for the quality, security and success of the products on the market.

5. How we can help you

We are happy to support you with regard to the structure and implementation of your QMS. As an external QMB, we also take over the implementation and the "active life" of the system with their inclusion at QMS for class manufacturers. Because it is not we live the QMS, but you as a manufacturer.

So we support you during your complete project with your medical device, starting with a free initial consultation, help with the introduction of a QM system, study planning and implementation to technical documentation - always with primary reference to the clinical data on the product: from the beginning until to the end.

Do you already have some initial questions?

You can find free initial consultation here : Free initial consultation

The path to biosafety of medical devices

In this blog post we focus on the biological assessment of medical devices, a crucial process step for ensuring biocompatibility and patient safety. The article walks through the basics of biocompatibility, highlights the requirements of the MDR and explains the important role of the comprehensive series of standards EN ISO 10993, which consists of many parts and deals with different aspects of biological safety. The most important and fundamental part is EN ISO 10993-1, which specifies the general requirements and procedures for biological assessment.

In addition, we provide a detailed insight into the latest changes to the standard as well as the strategic steps for test planning and risk assessment. The article shows what a modern test strategy can look like, taking into account material characterization and alternative methods for reducing animal testing, and provides practical tips for complete documentation in the Biological Evaluation Plan (BEP) and Biological Evaluation Report (BER). Our goal is to provide in-depth knowledge in order to effectively implement the complex requirements of biological assessment while at the same time ensuring the highest safety standards for medical devices.

Underlying regulations

EU Regulation 2017/745 (MDR)

EN ISO 10993-1

1 Introduction

Biocompatibility – the ability of a material not to cause negative reactions when in contact with the human body – is a crucial factor in the safety of medical devices. Whether a product comes into direct contact with tissue, blood or other body fluids or has an indirect effect on organs and systems: biological safety must always be guaranteed. Ultimately, the well-being of the patient depends on it. The challenge for manufacturers is to ensure that every material, every substance and every combination of components used does not have any undesirable effects on the human organism. This includes not only direct physical interactions, but also chemical interactions that can arise from degradation products or changes in the body. Safety during repeated use also plays an important role, as some products remain in contact with the human body for long periods of time.

The Medical Devices Regulation (MDR) has formulated strict requirements for this that require manufacturers to carry out comprehensive biological assessments and carefully document them. Annex I, point 10.2, specifies the basic safety and performance requirements that each medical device must meet to ensure safe use. Annex II, point 6.1 also requires complete documentation of the biological assessment as part of the technical documentation.

“The products are designed, manufactured and packaged in such a way that the risks from pollutants and residues for patients - taking into account the intended purpose of the product - as well as for transport, storage and operating personnel are kept as low as possible. Particular attention will be paid to tissues exposed to these pollutants and residues, as well as the duration and frequency of exposure.” (MDR Annex I, paragraph 10.2)

“Detailed information on the test setup, complete test or study protocols, methods of data analysis, in addition to data summaries and test results, in particular with regard to the biocompatibility of the product, including the identification of all materials in direct or indirect contact with the patient or user […].” (MDR Annex II, paragraph 6.1)

2. The EN ISO 10993-1 standard

EN ISO 10993 is the basic standard of a comprehensive series of standards for the biological assessment of medical devices, which consists of many specific parts, each covering different aspects of biocompatibility. The most important and fundamental part of this series of standards is EN ISO 10993-1, which specifies the general requirements and procedures for biological assessment. This standard plays an essential role in the approval process as it provides manufacturers with clear instructions on how to identify, minimize and document potential biological risks. Compliance with EN ISO 10993-1 is therefore often a basic requirement for meeting the safety and performance requirements of the MDR.

The new version of EN ISO 10993-1:2020 introduced some changes and additions to make the assessment even more precise and safer. Particular emphasis was placed on the need for integrated risk management that takes into account not only the chemical composition of the product, but also possible long-term effects and degradation products in the body.

A further, updated version of EN ISO 10993-1 is currently being drafted and brings with it significant innovations that are intended to make the process of biological assessment even more comprehensive and specific. The changes include new wording and more precise definitions intended to ensure more consistent interpretation. An additional section is also introduced that describes specific requirements for the entire life cycle of a product, supporting a more holistic view of biosafety.

The standard itself aims to offer manufacturers a structured biological assessment procedure that covers all relevant aspects of biocompatibility. The area of ​​application includes all medical devices that come into direct or indirect contact with the human body - from skin contact to implantable products to products that enter the bloodstream. The important terms and definitions therefore include central concepts such as biocompatibility , risk analysis and material compatibility , which ensure uniform language and uniform testing standards.

The basic principles of EN ISO 10993-1 are based on a risk-based approach: First, the product design is analyzed to assess the potential contact with the body and the material composition. Depending on the risk profile, specific biological tests are then determined - from cytotoxicity to sensitization to long-term tests. This structured approach helps manufacturers to systematically identify and minimize all biological risks to ensure the safety of the product throughout its entire life cycle.

3. Biological assessment and testing strategy

The biological evaluation and testing strategy for medical devices is a structured process intended to ensure the safety and biocompatibility of a product throughout its entire life cycle. In EN ISO 10993-1, this systematic procedure for biological assessment is clearly shown in Figure 1.

Figure 1: Systematic procedure for biological assessment (according to Figure 1 from EN ISO 10993-1)

A central aspect of biological assessment is now material characterization in accordance with EN ISO 10993-18. Through the detailed physical-chemical characterization of the materials and their potential degradation products, many risks can be identified and minimized at an early stage. This reduces the need for extensive in-vivo testing and supports the use of alternative testing methods.

Annex A of EN ISO 10993-1 describes specific biological endpoints that must be evaluated depending on the type and application of the medical device. The product is first assigned to a category:

  • Medical devices that only come into contact with the surface of the body,
  • Products that come into external contact with the inside of the body
  • Implantable medical devices

In addition, the contact is determined:

  • Medical devices that only come into contact with the body surface:
    • Intact skin
    • mucous membrane
    • Injured or damaged areas of skin
  • Products that come into external contact with the inside of the body
    • Blood vessel system, indirectly
    • Tissue/bone/dentin
    • Circulating blood
  • Implantable medical devices
    • Tissue/bone
    • blood

and the contact duration is defined, which is divided into levels A (≤24 h), B (>24 h to 30 d) and C (>30 d).

This detailed categorization helps determine a specific testing strategy for each product. The choice of tests depends not only on the type of contact, but also on the expected long-term effects and the possible risks from degradation products. A detailed risk analysis can often demonstrate that certain tests are not necessary, thereby avoiding unnecessary animal testing.

Figure 2: Example table from Annex A of EN ISO 10993-1:2020 for medical devices in contact with the body surface

These endpoints must be evaluated in detail in the Biological Evaluation Report (BER). However, this does not mean that all endpoints necessarily have to be processed through testing, like a checklist. EN ISO 10993-1 also allows the use of existing data, such as scientific literature or other validated information, to cover specific endpoints. On this basis, it can be justified why some tests can be omitted if the existing data supports the biosafety evidence. This enables a targeted and resource-saving assessment that still meets all relevant security requirements.

This enables a flexible assessment that meets both safety requirements and ethical aspects by minimizing animal testing as much as possible. The integration of alternative test methods, such as in vitro procedures and computer-based simulations, are an essential part of modern biological assessment.

4. Documentation and reporting

An essential part of the biological safety assessment of medical devices is careful documentation, which ensures that all assessment and testing processes are recorded in a comprehensible and transparent manner. Two central documents play an important role here: the Biological Evaluation Plan (BEP) and the Biological Evaluation Report (BER).

The Biological Evaluation Plan (BEP) defines the strategy for the biological evaluation of the product:

  • Product description: Details on components, materials and manufacturing processes.
  • Intended use and type of contact: Information about the intended use and type of physical contact.
  • Manufacturing process: Description of the manufacturing process and auxiliary materials used.
  • Reusable products: information on cleaning and/or sterilization.
  • Physical/chemical information: Existing data to characterize the medical device and the materials it contains.
  • Risk assessment: identification of biological endpoints.
  • Existing biological safety data: Existing biological tests.
  • GAP Analysis: Gap analysis to identify missing information in current security data.
  • Test strategy: Selection and justification of the necessary tests, including physical-chemical characterization according to EN ISO 10993-18 (if not yet available).

The Biological Evaluation Report (BER) documents the implementation and results of the evaluation and contains:

  • Product description: Details on components, materials and manufacturing processes.
  • Intended use and type of contact: Information about the intended use and type of physical contact.
  • Manufacturing process: Description of the manufacturing process and auxiliary materials used.
  • Reusable products: information on cleaning and/or sterilization.
  • Physical/chemical information: Existing data to characterize the medical device and the materials it contains.
  • Risk assessment: identification of biological endpoints.
  • Material and product characterization: Details of physico-chemical characterization and test results.
  • Test results: Description of tests, results and interpretation of in vitro and in vivo tests, including cytotoxicity tests.
  • Conclusions: Overall biosafety assessment and recommendations.
  • Determine next steps: If additional testing or assessment is required.

5. Conclusion

The biological assessment of medical devices is a complex and multi-layered process that makes a crucial contribution to ensuring patient safety. With the requirements of the MDR and EN ISO 10993-1, manufacturers have a well-founded set of rules at their disposal that enables a structured and risk-based assessment. The material characterization according to EN ISO 10993-18 as well as the clearly defined biological endpoints help to identify risks at an early stage and specifically address them. The systematic approach, supported by precise categorization of contact type and duration, ensures that only necessary and relevant tests are carried out.

A key advantage of the modern approach to biological assessment is the flexibility to incorporate existing data and, if necessary, replace tests with scientifically based reasoning. This not only enables resource-saving but also ethically responsible product evaluation, as the burden on laboratory animals is minimized. The detailed documentation in the BEP and BER ensures that all steps are traceable and compliance with the regulatory requirements is transparently documented. The biological assessment not only creates safety for the patient, but also strengthens trust in medical devices and their responsible development and approval.

6. How we can help you

At medXteam we provide you with comprehensive support in the biological assessment of your medical devices and compliance with regulatory requirements. Thanks to our expertise in the analysis and evaluation of clinical data, we offer you tailor-made solutions for the creation of the Biological Evaluation Plan (BEP) and the Biological Evaluation Report (BER). Our team will help you develop an effective testing strategy that covers all relevant biological endpoints while maximizing the use of clinical data and scientific literature to avoid unnecessary testing.

We accompany you through the entire process - from material characterization according to EN ISO 10993-18 to the assessment and documentation of all biological risks according to MDR. Our experts are at your side to ensure that your products meet the highest safety standards and meet all regulatory requirements for approval. Let us systematically address the biological risks of your products together and achieve clinically sound, reliable results. Contact us to find out more about how we can help you with your next project.

Do you already have some initial questions?

You can get a free initial consultation here: free initial consultation

At medXteam, the focus is on clinical data. In this context, as CRO we not only carry out clinical trials with medical devices in accordance with MDR and ISO 14155, but also offer all other options and forms of data collection. This time, in this context, the topic of clinical trials in the dental sector is again the focus. Since this topic is very extensive, we have divided it into two parts. In the first part of the blog post we looked at basic study design considerations in dental studies. As an example of a topic in dental research, we have taken a closer look at the endpoints in periodontal clinical trials. Part 2 continues with periodontal study designs with endpoints that are used for specific clinical situations, e.g. B. in the treatment of localized gum recession, missing keratinized gingiva or furcation defects. Finally, we turn to the endpoints in implant research.  

Abbreviations

MDR Medical Device Regulation; EU Regulation 2017/745

Underlying regulations

EU Regulation 2017/745 (MDR)
Medical Devices Implementation Act (MPDG)

Sources

WV Giannobile, NP Lang, MS Tonetti, eds.: “Osteology guidelines for oral and maxillofacial regeneration: clinical research”. Quintessence Publishing, 2014.

1. Endpoints in studies evaluating the treatment of localized gingival recessions

The case definition of localized gingival recession is when the loss of the periodontal attachment affects the buccal surface of the tooth, with the attachment to the interdental tissues being partially or not affected. The extent of recession is measured by probing to determine the distance between the CEJ (cemento-enamel junction) and the gum line. The aim of regenerative therapies is therefore to treat these lesions by completely covering the buccal root surface and returning the gingival margin to the cementoenamel junction (=CEJ) or above. Achieving this goal is referred to as "root coverage" and therefore the ultimate end point of these procedures is the achievement of 100% root coverage.

Achieving 100% complete coverage is considered the primary outcome of these procedures. This primary result is usually expressed as a percentage and can be expressed as a percentage of root coverage, namely

  1. between the baseline and the end of the study period or
  2. as a percentage of sites where full coverage could be achieved.

Strictly speaking, the actual result requires evidence of complete regeneration of the soft tissue attachment at the root, which can only be determined histologically.

Therefore, there are specific surrogate endpoints that are commonly used in evaluating the effectiveness of these regenerative procedures. These are assessed by linear measurements using clinical probing and the key findings are:

  • Gain in clinical attachment (CEJ – PPD / cementoenamel border - pocket bottom)
  • Reduction of clinical recession (CEJ – GM / cementoenamel junction - gingival margin)
  • Gain in width of the keratinized gingiva (GM-MGJ / gingival edge - mucogingival border)

Several factors may be important when it comes to fully covering recession defects, such as: B. plaque levels, smoking status and surgical procedure used. From the patient's perspective, the main reason for recession coverage surgery is usually to improve the aesthetic appearance or reduce root hypersensitivity or pain. Therefore, capturing patient-related outcomes is very important when evaluating these interventions. The aesthetic result is usually assessed by the patients themselves using questionnaires. Likewise, the assessment of changes in pain and sensitivity by the patient is carried out using questionnaires or more objective assessments, e.g. B. with visual analogue scales (VAS). A composite index also exists to evaluate the aesthetic results of these procedures (Root Coverage Esthetic Score [RES]) by calibrated assessors. This score is based on the assessment of five variables: (a) the level of the gingival margin, (b) the marginal contour, (c) the soft tissue surface, (d) the position of the MGJ (mucogingival junction), and (e) the gingival color.

2. Endpoints in studies evaluating soft tissue augmentation procedures:

These procedures aim to increase the dimension of the keratinized gingiva or mucous membrane in specific areas or in places where it is present to a small extent or not at all. The primary outcome of these studies is the assessment of the increase in the width of the keratinized tissue, measured by probing from the GM (gingival margin) to the MGJ (mucogingival junction).

Surrogate outcomes are often used in these studies:

  • Changes in the width of the attached gingiva or mucosa, that is, the width of the keratinized gingiva or mucosa minus the probing depth of the sulcus or pocket (mm)
  • Changes in gingival thickness (mm)
  • Changes in vestibular depth (mm)

3. Endpoints in studies evaluating periodontal regeneration procedures for furcation defects

Unlike infraalveolar lesions, the extent of these furcation lesions is assessed horizontally rather than vertically, and the degree of horizontal impairment is used to classify furcation lesions into grades I, II, and III.

Therefore, the change in the degree of furcation is usually used as the primary endpoint.

Other surrogate endpoints to evaluate the efficacy of regenerative methods to resolve furcation infestation are changes in horizontal attachment levels. This result is measured by inserting the periodontal probe horizontally at the entrance to the furcation and assessing the probing depth. The remaining surrogate and secondary endpoints described in regeneration studies for infraalveolar defects can also be used in the evaluation of furcation defects.

4. Endpoints in studies on implant therapy

Over the past 20 years, this therapy has become the most important and widespread measure for restoring lost teeth.

The success of this form of therapy is based on achieving what is known as “osseointegration”, i.e. direct contact between the surface of a functioning implant and the bone. And of course maintaining that contact over time. Similar to periodontal therapy, it is not ethically possible to comprehensively assess osseointegration in humans histologically across studies, and therefore the true clinical endpoint is maintenance of the implant in function, with no apparent pathology, no symptoms, and no significant bone loss.

In clinical research, this endpoint is assessed using various success criteria, including those described by Albrektsson et al. (2009) are the most commonly used criteria. Success rates are expressed as the percentage of implants that achieve success over time.

Many clinical studies have reported the effectiveness of implant therapy using less stringent criteria, assessing only the presence of the implant in the mouth and functioning over a period of time. This result is expressed as a percentage (%) of implants surviving (remaining functional) or as a survival rate. It is also expressed in lifetime analysis using Kaplan-Meier survival curves. This endpoint has been and continues to be heavily criticized because it does not assess the status of the peri-implant tissue, but only the presence of the implant in the oral cavity.

In principle, it should be noted in implant studies that - unlike in dental studies - there is no fixed orientation point for measurements (e.g. the cementoenamel junction). This means that, for example, markings (e.g. incisal edge) on the remaining teeth or an acrylic stent must be used for precise and reproducible measurements.

4.1 Primary surrogate endpoints

The primary endpoints in implant research are the assessment of crestal bone level through radiological assessment. As with periodontal regeneration studies, assessment of crestal bone level changes requires standardization of radiographic technique.

The digitalized images obtained are used to record the distance from the implant shoulder to the most coronal implant-bone contact. The changes in these values ​​between baseline and the end of the study period are typically the primary outcome of clinical trials evaluating the effectiveness of dental implants. Depending on the study design, two different baseline values ​​can be used. Either the value of the initial radiographic examination is made at the time of implantation or at the time of insertion of the restoration. In the latter case, physiological bone remodeling after the surgical insertion of the implant is avoided and, as a rule, less bone loss will take place. However, this so-called remodeling process seems to depend on the implant design; therefore, the recommendation should be to assess bone levels at both time points and then in subsequent study periods.

In implant therapy, although maintaining implant-bone anchorage in the oral cavity depends on maintaining crestal bone levels, it is also important to assess the health parameters of the peri-implant tissue.

Examples would be here:

  • Probing depth: at six locations per implant using a pressure-calibrated probe
  • Bleeding on probing: A dichotomous score can be used at six sites per implant (0, no bleeding; 1, bleeding)
  • Presence/absence of suppuration after probing (Yes/No)
  • Width of the keratinized gingiva (distance gingiva to the mucogingival border)

4.2 Secondary surrogate endpoints:

The health of the peri-implant tissue depends on various etiological and risk factors that may influence the long-term effectiveness of dental implants and therefore should be controlled in any long-term clinical trial evaluating the effectiveness of implant therapy. The most commonly used are:

  • Presence of plaque at the peri-implant mucosal edge (usually assessed dichotomously and expressed as a percentage).
  • History of the patient's previous periodontal disease (also assessed dichotomously and expressed as a percentage).
  • Patient's current and past tobacco use.
  • Stability of the implant. This is measured by resonance frequency analysis (RFA) and expressed in RF units. It is an indirect measure of implant-bone contact and is used to evaluate primary implant stability at the time of implantation and during the osseointegration process (from primary to secondary stability).

5. Endpoints in clinical research evaluating bone regenerative therapies

Ideally, a dental implant is placed in a location where there is sufficient crestal bone to ensure good primary stability and ensure the osseointegration process. In reality, the situation is unfortunately different, as patients often wait before therapy so that bone loss has already occurred at the insertion site.

For these situations in which there is not enough bone, there are various augmentative, bone regenerative therapies. These therapies can be performed in conjunction with implant placement or before implant placement. Socket preservation techniques aim to prevent resorption of the alveolar walls. Lateral augmentation techniques build the jaw ridge laterally, vertical techniques create a vertical gain in bone.

5.1 Jaw chamber posture techniques

Basically, the primary endpoint in these studies is the measurement of the extent of vertical and horizontal resorption of the alveolar walls after tooth extraction.

Fabrication of acrylic templates with fixed landmarks enables a reproducible method for measuring horizontal and vertical dimensional changes of the alveolar ridge. Measurements are taken from these templates at standardized points on the bone crest after a small flap is raised. The most commonly taken horizontal measurement is the mid-buccal width of the bone ridge. Additional measurements can be taken on the mesial and distal sides of the donor sites. The same points can be used for the vertical measurements. The horizontal and vertical changes between baseline (tooth extraction) and the end of the study period (usually the time of implantation) are then calculated.

Similar measurements can be made indirectly using plaster models by measuring the horizontal and vertical changes at various standardized locations. This method involves taking silicone impressions before tooth extraction and at various times after extraction. This method can be used to quantify both vertical and horizontal changes in the alveolar ridges.

5.2 Lateral bone augmentation

Various surgical techniques exist for the treatment of bone defects around implants, partly because these defects have different dimensions and shapes (dehiscences, fenestrations, etc.). However, the common goal of these procedures is to increase the bone volume around the implant. The specific endpoints of these procedures are the assessment of the increase in bone volume between the procedure and the last follow-up. The direct linear measurements are taken with a periodontal probe in mm and recorded intraoperatively using the following reference points:

  • Defect height (mm), measured from the implant shoulder or edge to the first bone-implant contact (BIC)
  • Defect width (mm) measured from the mesial to distal tips of the bone crest
  • Defect depth (mm) measured from the bone crest to the implant surface in a direction perpendicular to the long axis of the implant
  • Infraalveolar defect height (mm), measured from the bone crest to the first bone-to-implant contact (bone-to-implant contact).

The comparison between the second surgical procedure, which usually occurs 3 to 4 months after the implant placement, and the bone grafting can be expressed in “mm” or as a percentage, reaching 100% when the contact between the bone and the implant is at the level of the implant shoulder.

To evaluate the volumetric changes, three-dimensional digital images can be recorded with optical scanners and measured with the appropriate software. The key advantage of this technique is its non-invasive nature, although this endpoint represents the combination of soft and hard tissue changes. Similar volumetric assessments can be made on plaster models that are then scanned. The captured images can be measured using a special CAD/CAM system by merging and superimposing the before-and-after images in a coordinate system and thus evaluating the increase in volume. The 3D changes in hard tissue volume must be evaluated radiographically using digital volume tomography (conebeam, CBCT) images.

5.3 Vertical bone augmentation

These procedures are usually before implantation, so the implant cannot be used as a reference. The same volumetric and radiographic techniques as described above can be used to evaluate vertical and horizontal changes between the regenerative procedure and follow-up. A special feature of these procedures are those in which the maxillary sinus is used for vertical bone augmentation (sinus lift procedure).

The effectiveness of vertical bone augmentation is often examined histologically. The biopsies are usually taken during implantation using a trephine drill. This procedure is ethically harmless because the trephine drill has the same diameter as the implant and the implant bed would have had to be prepared anyway.

 The following parameters are recorded histologically:

  • Percentage of new bone
  • Percentage of remaining bone substitute material or augmented autologous bone
  • Percentage of soft tissue or empty space remaining.

Assessment of vertical bone formation can be measured by standardized periapical radiographs immediately after the procedure and usefully 6 months after the procedure. If the sinus lift is performed in conjunction with implant placement (simultaneous approach), a similar assessment of vertical bone formation can be made on standardized periapical radiographs.

6. Endpoints in clinical research evaluating implant prosthetics

The specific parameters for evaluating the outcome of implant-supported restorations are:

  • Changes to the gingival margin by measuring the distance between the edge of the restoration and the most apical point of the soft tissue margin on the buccal side of the implant bed.
  • Changes in papillary filling by measuring the degree of soft tissue filling on the mesial and distal aspects of the implants. This is usually done according to the criteria of Jemt et al. (1997) described papilla index system (grades O to 4), where the grade O stands for no filling of the papilla, 1 for a filling of < 50%, 2 for a filling of > 50% and 4 for a complete filling Filling of the papilla.

In addition to these specific endpoints, other secondary outcomes that have been shown to contribute to the results of restorative procedures should also be assessed and reported, such as plaque accumulation, smoking status, bleeding on probing, gingival thickness, keratinized gingival width.

Important indices allow an objective and comprehensive assessment of the aesthetic results; These include the Pink Esthetic Score (PES) (Furhauser et al., 2005), the Implant Crown Aesthetic Index (Meijer et al., 2005) and the modified PES/White Esthetic Score (WES) (Belser et al., 2009) . The implant crown esthetics index is based on the anatomical shape, color and surface condition of the crown as well as the anatomical shape, color and surface condition of the peri-implant soft tissues. The modified PES/White Esthetic Score (WES) system complements the PES system with general tooth shape, clinical crown outline and circumference, color, surface finish and translucency.

These indices should be assessed on standardized clinical photographs by calibrated examiners.

7. Conclusion

In summary, many factors must be taken into account in clinical studies of periodontal regeneration to achieve reliable and meaningful results. The defect category, the choice of surgical technique and methodology, and the patient's smoking habit play an important role. In addition, secondary endpoints such as plaque accumulation and gingival inflammation should be carefully evaluated as they may significantly influence the study outcome. These factors contribute to ensuring the effectiveness and safety of the treatment methods studied and ultimately improving the periodontal health of patients. As we see in this Part 2, the choice of the correct endpoint also plays a crucial role in the design and planning of clinical trials with medical devices in the dental sector.

8. How we can help you

We would be happy to support you with successful planning and implementation of dental studies. Thanks to our comprehensive expertise in this area with the special features that need to be taken into account, we generate the clinical data you need for your medical device. 

We support you throughout your entire project with your medical device, starting with a free initial consultation, help with the introduction of a QM system, study planning and implementation through to technical documentation - always with primary reference to the clinical data on the product: from the beginning to the end End.

Do you already have some initial questions?

You can get a free initial consultation here: free initial consultation

  1. Clinical research in the oral and maxillofacial area
  2. FDA - MDR: Transfer of the approval strategy to the US market
  3. How is a clinical assessment based on performance data created?
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