Timeline Of Alzheimer’s Onset To Death Predicted Using New Method

Alan McStravick for redOrbit.com – Your Universe Online

Known as ‘The Long Goodbye,’ Alzheimer’s disease slowly and methodically ravages the minds of its sufferers, rendering memories and relationships to the darkest recesses of the brain. While there are therapies and new medications meant to prolong the inevitable, there had been no way to discern the scope and severity of the disease from one patient to another.

“Predicting Alzheimer’s progression has been a challenge because the disease varies significantly from one person to another,” says Yaakov Stern, PhD. “Two Alzheimer’s patients may both appear to have mild forms of the disease, yet one may progress rapidly, while the other progresses much more slowly.”

Stern is the senior author of research conducted by Columbia University Medical Center (CUMC) and is a professor of neuropsychology at CUMCs Taub Institute for Research on Alzheimer’s Disease and the Aging Brain and the Gertrude H. Sergievsky Center.

The CUMC research team was able to clinically validate a new method meant to predict time to full-time care, nursing home residence, or death for sufferers of the disease. Data collected for this new method is derived from a single patient visit. The model was developed as a result of consecutively following two sets of Alzheimer’s patients over a decade.

“Our method enables clinicians to predict the disease path with great specificity,” stated Stern. Their results have been published online ahead of print in the Journal of Alzheimer’s Disease.

Study co-author and associate professor of neurology in the Taub Institute and the Sergievsky Center, Nikolaos Scarmeas, MD, commented, “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. It may become a valuable tool for both physicians and patients’ families.”

Additionally, the team sees great value in the use of their new method in future clinical trials. They claim it would benefit the trials by allowing practitioners to better balance the patient cohorts between those with faster-progressing Alzheimer’s and those whose form of the disease progresses more slowly. Health economists will also be able to employ this method to better predict the economic impact of the disease.


Dr. Stern began development of this method in 1989, when he received a grant from the National Institutes of Health to begin his study, ‘Predictors of Severity in Alzheimer’s Disease.’ “The fact that work on this prediction method began nearly 25 years ago underlines the difficulties of studying Alzheimer’s disease,” said Richard Mayeux, MD, MSc. Mayeux serves as professor of neurology, psychiatry and epidemiology and is chair of neurology at the Gertrude H. Sergievsky Center, as well as being co-director of Sergievsky and the Taub Institute.

In 1989, Stern and colleagues affiliated with Massachusetts General Hospital and Johns Hopkins first followed 252 non-familial Alzheimer’s patients every six months over a period of 10 years. The resultant data from that study was used by Eric Stallard, co-author of the paper and an actuary at Duke University, to create a Longitudinal Grade of Membership (L-GoM) model of Alzheimer’s progression. Those results were originally published in the journal Medical Decision Making in 2010.

After the L-GoM was created, Stern and colleagues followed a new group of 254 patients with the intent of using data collected from only a single patient visit to predict outcomes for the group.

In these visits the team, using the L-GoM, looked at 16 sets of variables. Among these variables are: ability to participate in day-to-day activities; mental status; motor skills; estimated time of symptom onset; and duration of tremor, rigidity or other neurological symptoms. Additionally, the L-GoM also includes data obtained after the patient’s death (time and cause of death).

Dr. Stern lauded the new method stating, “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.” Stern continues, “For example, a patient may be able to live independently yet have hallucinations or behavioral outbursts. 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 predictive results of the L-GoM are presented as the expected time to a particular outcome. As an example, the team tells of two 68-year-old Alzheimer’s patients who presented similar mental status scores on the initial visit. However, the first patient was far more dependent on his caregiver and was experiencing delusions. The subtle differences between the two patients, having been taken into account with the L-GoM, resulted in different predictions each patient had to their time of death. The method accurately predicted the first patient would die within three years, while the other would survive more than 10 years.

First author and assistant professor of neurology at CUMC, Ray Razlighi, PhD, commented, “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.” Razlighi is also an adjunct assistant professor of biomedical engineering at Columbia University.


The next step for Stern and his colleagues is to develop a computer program which would generate a report for clinicians after they have input the available variables in the L-GoM. Expectations are that this program will be available for use within the next two years. This program, or one like it, could eventually be incorporated into electronic health records. As Stern notes, “At our Alzheimer’s center, patients are already filling out much of their clinical information electronically.”

The first two sets of patients the method looked at were fairly homogenous, being white, educated and of a high socioeconomic status. For this reason, the team next plans to test their method following a more diverse group of participants. To do this, they will seek participants at the CUMC Washington Heights-Inwood Columbia Aging Project (WHICAP).

WHICAP is an ongoing, community-based study of aging and dementia comprised of elderly, urban-dwelling residents. The team believes because many participants are dementia-free when they join the study, they might better be able to capture the age of dementia onset and track symptom development over time.

The paper, titled ‘A New Algorithm for Predicting Time to Disease Endpoints in Alzheimer’s Disease Patients’ was contributed to by researchers from Duke University, Johns Hopkins, Massachusetts General Hospital, Harvard University and the Philadelphia VA Medical Center. Grant funding from the National Institutes of Aging supported the study.

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