New Method Could Predict When Alzheimer's Patients Will Need Extensive Assistance, Pass Away
A new method could predict the distance in time between when an individual is diagnosed with Alzheimer's to when they will need extensive assistance. It could also estimate their longevity.
Researcher's developed the method by observing 254 Alzheimer's patients over the course of 10 years, a Columbia University Medical Center news release reported.
"Until now, some methods of predicting the course of Alzheimer's have required data not obtained in routine clinical practice, such as specific neuropsychological or other measurements, and have been relatively inaccurate. This method is more practical for routine use," Nikolaos Scarmeas, a study co-author and associate professor of neurology, in the Taub Institute and the Sergievsky Center, said. "It may become a valuable tool for both physicians and patients' families."
So what benefits could come from putting an expiration date on an Alzheimer's patient?
"Families will be able to make financial and logistical plans for caring for their loved one with Alzheimer's disease. Furthermore, the new method 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," senior author Yaakov Stern, PhD, professor of neuropsychology at CUMC, told Headlines and Global News in an e-mail.
The method is based on a Longitudinal Grade of Membership (L-GoM) model, which takes 16 variables (such as the ability to participate in everyday acitivities) into account. The method could allow researchers to make these predictions after only one visit.
"The benefit of the L-GoM model is that it takes into account the complexity of Alzheimer's disease. Patients don't typically fall neatly into mild, moderate, or severe disease categories. For example, a patient may be able to live independently yet have hallucinations or behavioral outbursts," Yaakov said in the news release. "Our method is flexible enough to handle missing data. Not all 16 variables are needed for accurate predictions-just as many as are available."
The test was able to accurately predict when two patients would die; one passed away after three years, the other survived for 10.
"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, assistant professor of neurology at CUMC and adjunct assistant professor of biomedical engineering at Columbia University, said.