DementiaMLDLBNCT04448340
A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias
An observational study of Dementia, run by National and Kapodistrian University of Athens at 1 site in Greece.
Trial summary
Parkinson's disease dementia (PDD) and Dementia with lewy bodies (DLB) are dementia syndromes that overlap in many clinical features, making their diagnosis difficult in clinical practice, particularly in advanced stages. Read more about this study
- Who is it for?
- Ages 50 to 90 years
- Conditions the study is for
- Dementia
- What is being tested?
- machine learning model
- How many people
- 200 participants
- Where
- 1 siteGreece
- Study dates
- 1 Sep 2019to 1 Mar 2021
- 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 50 to 90 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: the PDD group comprised of patients fulfilling the Criteria for probable PDD and the DLB group.Patients will be enrolled from the register-based database of two clinics. The following data were collected: gender, age, education, hand dominance, Disease duration (years) and levodopa equivalent daily dose (LEDD). The burden of disease will be assess by the Movement Disorders Society-United Parkinson's Disease Rating Scale (MDS-UPDRS) part III in the Off medication state and the following six cognitive/behavioral tests: Mini-Mental State Examination (MMSE), PD- Cognitive Rating Scale (PD-CRS), Brief Visuospatial Memory test (BVMT-TR), Symbol digit written (SDMT), Trail making test (TMT A,B), Wechsler adultintelligence scale (WAIS-V). All patients will undergo brain MRI and blood test to exclude secondarycauses of dementia.
Who may be able to join
- the PDD group comprised of patients fulfilling the Criteria for probable PDD of the Movement Disorders Society (b) the DLB group comprised of patients, according to the recent revised criteria for probable DLB .
Who cannot join
- major psychiatrics disorders, depression
Trial locations and contacts
1 site in 1 country.
Greece1 site, 1 recruiting
- Recruiting
Anastasia Bougea
Athens, Attica, 16674
EFTHYMIA EFTHYMIIOPOULOU, dr, 00306943061632, faih.efthymiopoulou@gmail.com
Christos Goumas, dr
About this study
Official title: A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias Using Clinical and Neuropsychological Scores
Parkinson's disease dementia (PDD) and Dementia with lewy bodies (DLB) are dementia syndromes that overlap in many clinical features, making their diagnosis difficult in clinical practice, particularly in advanced stages. We propose a machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify these disorders with a high prognostic performance.
Read the detailed description
The algorithm will be develop using dataset from two specialized memory centers, employing a sample of PDD and DLB subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding clinico- demographic characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail making A and B) was used as predictors. Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will be investigated for their ability to predict successfully whether patients suffered from PDD or DLB.
- Conditions
- Dementia
- Being tested
- machine learning model
- Medical subject headings
- Dementia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
machine learning modelDiagnostic test
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), were investigated for their ability to predict successfully whether patients suffered from PDD or DLB.
| Study group | Role | What they receive |
|---|---|---|
| Parkinson Disease Dementia | the PDD group comprised of 58 patients fulfilling the Criteria for probable PDD of the Movement Disorders Society Receives: Diagnostic Test: machine learning model | |
| Dementia with Lewy Bodies | the DLB group comprised of 40 patients, according to the recent revised criteria for probable DLB Receives: Diagnostic Test: machine learning model |
What is being measured
The primary outcome is the main question the study is designed to answer.
MMSE predictive for dlb or PDD
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Measured: 1 year
Parkinson's Disease - Cognitive Rating Scale (PD-CRS) predictive for DLB or PDD
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Measured: 1 year
Brief Visuospatial Memory Test (BVMT-TR) predictive for DLB or PDD
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Measured: 1 year
Symbol digit written predictive for DLB or PDD
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Measured: 1 year
Wechsler adult intelligence scale,predictive for DLB or PDD
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Measured: 1 year
trail making A and B predictive for DLB or PDD
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Jun 2020
- Study start1 Sep 2019
- Primary completion1 Oct 2020 (estimated)
- Study completion1 Mar 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for DementiaStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National and Kapodistrian University of Athens
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Anastasia Bougea, National and Kapodistrian University of Athens
- Investigators
- ANASTASIA BOUGEA, Principal investigator, National and Kapodistrian University of Athens
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04448340
- Sponsor's protocol ID
- 251ATHENS HOSPITAL
- Acronym
- MLDLB
- Status last verified
- Sep 2020
- First submitted
- 21 Jun 2020
- Last updated
- 10 Sep 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Bougea A, Efthymiopoulou E, Spanou I, Zikos P. A Novel Machine Learning Algorithm Predicts Dementia With Lewy Bodies Versus Parkinson's Disease Dementia Based on Clinical and Neuropsychological Scores. J Geriatr Psychiatry Neurol. 2022 May;35(3):317-320. doi: 10.1177/0891988721993556. Epub 2021 Feb 8.
PubMed 33550890
Study at a glance
From the registry record, last updated by the sponsor on 10 Sep 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias Using Clinical and Neuropsychological Scores
- ClinicalTrials.gov ID
- NCT04448340
- Acronym / sponsor ID
- MLDLB / 251ATHENS HOSPITAL
- Conditions
- Dementia
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- National and Kapodistrian University of AthensUniversity, hospital or other
- Study start
- 1 Sep 2019 (actual)
- Primary completion
- 1 Oct 2020 (estimated)
- Study completion
- 1 Mar 2021 (estimated)
- First posted
- 25 Jun 2020 (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.
Parkinson's disease dementia (PDD) and Dementia with lewy bodies (DLB) are dementia syndromes that overlap in many clinical features, making their diagnosis difficult in clinical practice, particularly in advanced stages. We propose a machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify these disorders with a high prognostic performance.
Detailed description
The algorithm will be develop using dataset from two specialized memory centers, employing a sample of PDD and DLB subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding clinico- demographic characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail making A and B) was used as predictors. Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will be investigated for their ability to predict successfully whether patients suffered from PDD or DLB.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Parkinson Disease Dementia | – | Diagnostic Test: machine learning modelthe PDD group comprised of 58 patients fulfilling the Criteria for probable PDD of the Movement Disorders Society |
| Dementia with Lewy Bodies | – | Diagnostic Test: machine learning modelthe DLB group comprised of 40 patients, according to the recent revised criteria for probable DLB |
Who may qualify
- Ages
- 50 to 90 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- the PDD group comprised of patients fulfilling the Criteria for probable PDD and the DLB group.Patients will be enrolled from the register-based database of two clinics. The following data were collected: gender, age, education, hand dominance, Disease duration (years) and levodopa equivalent daily dose (LEDD). The burden of disease will be assess by the Movement Disorders Society-United Parkinson's Disease Rating Scale (MDS-UPDRS) part III in the Off medication state and the following six cognitive/behavioral tests: Mini-Mental State Examination (MMSE), PD- Cognitive Rating Scale (PD-CRS), Brief Visuospatial Memory test (BVMT-TR), Symbol digit written (SDMT), Trail making test (TMT A,B), Wechsler adultintelligence scale (WAIS-V). All patients will undergo brain MRI and blood test to exclude secondarycauses of dementia.
Key inclusion criteria (1 listed)
- the PDD group comprised of patients fulfilling the Criteria for probable PDD of the Movement Disorders Society (b) the DLB group comprised of patients, according to the recent revised criteria for probable DLB .
Key exclusion criteria (1 listed)
- major psychiatrics disorders, depression
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 |
|---|---|---|
| machine learning modelTwo classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), were investigated for their ability to predict successfully whether patients suffered from PDD or DLB. | Diagnostic test | Dementia with Lewy Bodies, Parkinson Disease Dementia |
Primary outcome
MMSE predictive for dlb or PDD
Time frame: 1 year
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Parkinson's Disease - Cognitive Rating Scale (PD-CRS) predictive for DLB or PDD
Time frame: 1 year
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Brief Visuospatial Memory Test (BVMT-TR) predictive for DLB or PDD
Time frame: 1 year
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Symbol digit written predictive for DLB or PDD
Time frame: 1 year
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Wechsler adult intelligence scale,predictive for DLB or PDD
Time frame: 1 year
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
trail making A and B predictive for DLB or PDD
Time frame: 1 year
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Sep 2020.
- Derived from this study: PubMed 33550890Bougea A, Efthymiopoulou E, Spanou I, Zikos P. A Novel Machine Learning Algorithm Predicts Dementia With Lewy Bodies Versus Parkinson's Disease Dementia Based on Clinical and Neuropsychological Scores. J Geriatr Psychiatry Neurol. 2022 May;35(3):317-320. doi: 10.1177/0891988721993556. Epub 2021 Feb 8.
- Individual participant dataSharing planned: No
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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.
ANASTASIA BOUGEA principal investigator
National and Kapodistrian University of Athens
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 Dementia, run by National and Kapodistrian University of Athens at 1 site in Greece.
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 1 inclusion and 1 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: status not confirmed, as of the sponsor's last update on 10 Sep 2020. 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 Greece. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact ANASTASIA BOUGEA, DR (+306930481046, annita139@yahoo.gr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
MMSE predictive for dlb or PDD (time frame: 1 year). The study lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04448340, which also lists 1 linked publication.
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 10 Sep 2020.