Cognitive ImpairmentNCT06611475
Prediction of MMSE Scores for Cognitive Impairment
An observational study of Cognitive Impairment and Healthy Control, run by Blekinge Institute of Technology at 1 site in Sweden.
Trial summary
This study aims to explore the potential of using machine learning (ML) algorithms to predict cognitive status, specifically MMSE scores, based on oral health and demographic data. Read more about this study
- Who is it for?
- Ages 60 years and olderHealthy volunteers can take part
- Conditions the study is for
- Cognitive Impairment, Healthy Control
- What is being tested?
- MMSE ≤26
- How many people
- 693 participants
- Where
- 1 siteSweden
- Study dates
- 10 Jun 2024to 10 Nov 2024
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 60 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Data collected from the European collaborative study Support Monitoring and Reminder Technology for Mild Dementia (SMART4MD) and the Swedish National Study on Aging and Care (SNAC-B) will be analyzed. Participants aged 60 years or older will be included in the analysis.
Who may be able to join
- Individuals aged 60 years or older.
- Participants with recorded oral health parameters and MMSE scores of either 30 or ≤26.
Who cannot join
- Individuals with MMSE scores of 27, 28, or 29, as these scores represent a transition phase between normal cognition and cognitive impairment, which could introduce variability.
- Individuals younger than 60 years.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Prediction of MMSE Scores for Cognitive Impairment: A Machine Learning Analysis of Oral Health and Demographic Data in Individuals Over 60 Years of Age
This study aims to explore the potential of using machine learning (ML) algorithms to predict cognitive status, specifically MMSE scores, based on oral health and demographic data. The objective is to evaluate the effectiveness of various ML models and identify the most relevant oral health indicators for predicting MMSE scores of 30 (normal cognition) or ≤26 (cognitive impairment) in individuals aged 60 and above.
Read the detailed description
This cross-sectional study utilizes oral health and demographic data from two existing cohort studies: the European collaborative study Support Monitoring and Reminder Technology for Mild Dementia (SMART4MD) and the Swedish National Study on Aging and Care (SNAC-B). Participants aged 60 years or older will be included in the analysis. The data will be used to develop and evaluate machine learning models for predicting cognitive status.
Objectives:
- Primary Objective: To assess the potential of oral health parameters for binary classification of MMSE scores (30 vs. ≤26).
- Secondary Objective: To identify the most influential oral health parameters contributing to cognitive impairment predictions.
- Tertiary Objective: To compare the performance of Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB) classifiers in predicting MMSE scores using oral health data.
- Conditions
- Cognitive Impairment, Healthy Control
- Being tested
- MMSE ≤26
- Keywords
- Classification, Machine Learning, Mini-Mental State Examination, Cognitive Impairment, Oral Health
- Medical subject headings
- Cognitive Dysfunction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MMSE ≤26Other
Also called MMSE 30
A dataset comprising participants with MMSE scores of ≤26 and 30 will be used to evaluate the classification performance of various machine learning techniques.
| Study group | Role | What they receive |
|---|---|---|
| MMSE ≤26 | 339 participants Receives: Other: MMSE ≤26 | |
| MMSE 30 | 354 participants Receives: Other: MMSE ≤26 |
What is being measured
The primary outcome is the main question the study is designed to answer.
Detection perfomance
The study measures the classification performance of Machine Learning classifiers. Performance metrics, Accuracy, precision, recall, F1-Score and confusion matrix will be used for the evaluation. The examination of the most important features relied on SHAP summary plots, providing visualizations of the influence of parameter groups on the output, organized by their importance. This importance is based on SHAP values, offering insights into features' effects on the ML model's decision-making process
Measured: 5 mounths
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Sep 2024
- Study start10 Jun 2024
- Primary completion10 Oct 2024
- Study completion10 Nov 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cognitive ImpairmentStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 693 participants (actual)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Blekinge Institute of Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06611475
- Sponsor's protocol ID
- BTH-6.1.1-0156-2024
- Status last verified
- Sep 2024
- First submitted
- 17 Sep 2024
- Last updated
- 25 Nov 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Participant data can not be shared due to the GDPR.
Study at a glance
From the registry record, last updated by the sponsor on 25 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of MMSE Scores for Cognitive Impairment: A Machine Learning Analysis of Oral Health and Demographic Data in Individuals Over 60 Years of Age
- ClinicalTrials.gov ID
- NCT06611475
- Acronym / sponsor ID
- BTH-6.1.1-0156-2024
- Conditions
- Cognitive Impairment, Healthy Control
- Keywords
- Classification, Machine Learning, Mini-Mental State Examination, Cognitive Impairment, Oral Health
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 693 participants (actual)
- Lead sponsor
- Blekinge Institute of TechnologyUniversity, hospital or other
- Study start
- 10 Jun 2024 (actual)
- Primary completion
- 10 Oct 2024 (actual)
- Study completion
- 10 Nov 2024 (actual)
- First posted
- 25 Sep 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to explore the potential of using machine learning (ML) algorithms to predict cognitive status, specifically MMSE scores, based on oral health and demographic data. The objective is to evaluate the effectiveness of various ML models and identify the most relevant oral health indicators for predicting MMSE scores of 30 (normal cognition) or ≤26 (cognitive impairment) in individuals aged 60 and above.
Detailed description
This cross-sectional study utilizes oral health and demographic data from two existing cohort studies: the European collaborative study Support Monitoring and Reminder Technology for Mild Dementia (SMART4MD) and the Swedish National Study on Aging and Care (SNAC-B). Participants aged 60 years or older will be included in the analysis. The data will be used to develop and evaluate machine learning models for predicting cognitive status.
Objectives:
- Primary Objective: To assess the potential of oral health parameters for binary classification of MMSE scores (30 vs. ≤26).
- Secondary Objective: To identify the most influential oral health parameters contributing to cognitive impairment predictions.
- Tertiary Objective: To compare the performance of Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB) classifiers in predicting MMSE scores using oral health data.
Study design
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Target follow-up
- 1 Day
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| MMSE ≤26 | – | Other: MMSE ≤26339 participants |
| MMSE 30 | – | Other: MMSE ≤26354 participants |
Who may qualify
- Ages
- 60 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Data collected from the European collaborative study Support Monitoring and Reminder Technology for Mild Dementia (SMART4MD) and the Swedish National Study on Aging and Care (SNAC-B) will be analyzed. Participants aged 60 years or older will be included in the analysis.
Key inclusion criteria (2 listed)
- Individuals aged 60 years or older.
- Participants with recorded oral health parameters and MMSE scores of either 30 or ≤26.
Key exclusion criteria (2 listed)
- Individuals with MMSE scores of 27, 28, or 29, as these scores represent a transition phase between normal cognition and cognitive impairment, which could introduce variability.
- Individuals younger than 60 years.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| MMSE ≤26Also: MMSE 30A dataset comprising participants with MMSE scores of ≤26 and 30 will be used to evaluate the classification performance of various machine learning techniques. | Other | MMSE 30, MMSE ≤26 |
Primary outcome
Detection perfomance
Time frame: 5 mounths
The study measures the classification performance of Machine Learning classifiers. Performance metrics, Accuracy, precision, recall, F1-Score and confusion matrix will be used for the evaluation. The examination of the most important features relied on SHAP summary plots, providing visualizations of the influence of parameter groups on the output, organized by their importance. This importance is based on SHAP values, offering insights into features' effects on the ML model's decision-making process
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Nov 2024.
- Individual participant dataSharing planned: No. Participant data can not be shared due to the GDPR.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cognitive Impairment and Healthy Control, run by Blekinge Institute of Technology at 1 site in Sweden.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 25 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Sweden. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Detection perfomance (time frame: 5 mounths). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Nov 2024.