A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias Using Clinical and Neuropsychological Scores
试验速览
- 阶段
- 不适用
- 入组人数
- 200
- 试验地点
- 1
- 主要终点
- MMSE predictive for dlb or PDD
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 50 Years 至 90 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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 .
排除标准
- •major psychiatrics disorders, depression
结局指标
主要结局
MMSE predictive for dlb or PDD
时间窗: 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
时间窗: 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
时间窗: 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
时间窗: 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
时间窗: 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
时间窗: 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.
次要结局
未报告次要终点
研究者
Anastasia Bougea
DR
National and Kapodistrian University of Athens