By Dr. Priyom Bose, Ph.D.Reviewed by Benedette Cuffari, M.Sc.Jul 18 2024 A recent eClinicalMedicine study utilized machine learning techniques to develop and test a predictive prognostic model for early dementia prediction using real-world patient data.
To date, there remains a lack of effective tools for the early diagnosis of dementia. Memory tests are particularly ineffective at the early stage, as they lack sensitivity. Furthermore, most patients cannot access more specific tests such as lumbar punctures for the assessment of cerebrospinal fluid biomarkers, nor positron emission tomography scans, which are invasive and costly.
To demonstrate the clinical utility of the PPM, the researchers trained the system on baseline, non-invasive, and low-cost data. Thereafter, the PPM was tested on real-world out-of-sample patient data and validated against longitudinal diagnoses in real-world data. The training was achieved using Addenbrooke’s Cognitive Examination Revised memory scale , Mini-Mental State Examination , and grey matter density from ADNI data.
The model-derived prognostic index was clinically relevant for predicting cognitive health trajectories. For two independent datasets, the PPM-derived prognostic index was derived from the baseline data and was significantly different across groups. The index was significantly higher when trained with MRI and cognitive data for multiple test cases such as AD, moderate MCI, mild MCI, or CN3.
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