Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Tuesday, September 26, 2023

Machine learning model able to better predict AD from driving data, biomarkers

 

Because of your extra risk of dementia from your stroke, does your hospital have enough functioning brain cells to get this? 

Do you prefer your hospital incompetence NOT KNOWING OR NOT DOING anything on this?

Your risk of dementia, has your doctor told you of this?  Your doctor is responsible for preventing this!

1. A documented 33% dementia chance post-stroke from an Australian study?   May 2012.

2. Then this study came out and seems to have a range from 17-66%. December 2013.`    

3. A 20% chance in this research.   July 2013.

4. Dementia Risk Doubled in Patients Following Stroke September 2018 

The latest here:

Machine learning model able to better predict AD from driving data, biomarkers

Key takeaways:

  • The study included 139 adults aged 65 years and older to drive vehicles for 1 year.
  • When a machine learning model was applied, more variables meant greater prediction accuracy for preclinical Alzheimer’s.

PHILADELPHIA — A machine learning model was better able to predict preclinical Alzheimer’s disease from participant biomarkers as well as driving data from a year-long study, according to a speaker.

“As we age, there are declines in sensory and motor abilities and when we think about driving, it really is one of the most complex activities that most of us do,” Ganesh M. Babulal, PhD, OTD, MSCI, an associate professor in the department of neurology at Washington University School of Medicine, said during his presentation at the American Neurological Association annual meeting. “It requires sustained and dynamic engagement.”

Older person driving
According to research, a machine learning model was better able to predict preclinical Alzheimer’s disease with more variables taken from information of cognitively normal older adults whose vehicles were chipped for 1 year. Image: Adobe Stock

Babulal and colleagues sought to identify older drivers at risk of cognitive decline and at risk for accidents and crashes, and to do so before cognitive decline occurs.

In the first 4 years of their research, the researchers found those with preclinical AD as measured by positive readings on amyloid or cerebrospinal fluid (CSF) biomarker testing made 2.5 more errors on a standard road test and were faster to fail a road test although remaining cognitively normal.

Their current study involved the DRIVES program, in which each 139 adults aged 65 years and older who were deemed cognitively normal as measured by the Clinical Dementia Rating scale, required to drive a non-adaptive vehicle with a valid driver’s license at least once per week.

Each participant had their vehicle fitted with a chip that for 1 year measured latitude and longitude for each vehicle as well as the number of trips, miles, unique destinations along with speed, the number of instances of hard braking and sudden acceleration. An accelerometer also measured the kind of accident impact as well as level of accident impact as either minor or major. All participants with CSF data were categorized as positive or negative and logistic regression models were employed to measure area under the curve. A machine learning model was applied with data to further analyze AUC with respect to biomarkers predicting preclinical disease.

According to results, when more variables were added to analysis, the more closely the AUC approached 1, indicating greater accuracy for preclinical disease prediction. When driving, baseline age, APOE, e4 status, race and gender were factored, AUC reached 0.963, compared with 0.774 when only driving was analyzed.

“Plasma biomarkers have really been the holy grail for Alzheimer’s disease,” Babulal said. “These are data that’s easily captured on any patient.”

No comments:

Post a Comment