The method, which uses data gathered from a single patient visit, is based on a complex model of Alzheimer's disease progression that the researchers developed by consecutively following two sets of Alzheimer's patients for 10 years each.
Senior author Yaakov Stern, PhD, professor of neuropsychology at Columbia University Medical Centre said that predicting Alzheimer's progression has been a challenge because the disease varies significantly from one person to another, but their method enables clinicians to predict the disease path with great specificity.
The new method also may be used in clinical trials- to ensure that patient cohorts are balanced between those with faster-progressing Alzheimer's and those with slower-progressing disease- and by health economists to predict the economic impact of Alzheimer's disease.
The prediction method is based on a Longitudinal Grade of Membership (L-GoM) model, developed by a research team also led by Dr. Stern.
The L-GoM includes 16 sets of variables, such as ability to participate in routine day-to-day activities; mental status; motor skills; estimated time of symptom onset; and duration of tremor, rigidity, or other neurological symptoms. It also includes data obtained postmortem (time and cause of death).
"In addition to time to nursing home residence or death, our method can be used to predict time to assisted living or other levels of care, such as needing help with eating or dressing, or time to incontinence," first author Ray Razlighi, PhD, assistant professor of neurology at CUMC and adjunct assistant professor of biomedical engineering at Columbia University said.
The study is published in Journal of Alzheimer's Disease.