A new app can measure the strength of alcohol withdrawal tremors and shows promise in being able to predict whether they are real or fake.
It’s a common scenario in emergency rooms—someone stops regular, excessive alcohol consumption and develops withdrawal, a condition that is potentially fatal, but easily treated with sedatives.
Benzodiazepine is a class of sedatives used to treat alcohol withdrawal, anxiety, seizures, and insomnia. But physicians are often reluctant to prescribe them because they’re frequently abused and can be dangerous when mixed with other drugs, especially alcohol and opiates.
The most commonly used clinical sign of withdrawal is tremor, especially in the hands and arms, but judging tremor severity is harder than it sounds. It requires considerable medical expertise, and even experienced doctors’ estimates can vary widely.
Chronic alcohol abusers often come to the emergency department claiming to be in withdrawal in an effort to obtain benzodiazepines, and it can be difficult for inexperienced clinicians to determine if the patient is actually in withdrawal or “faking” a withdrawal tremor. Front-line healthcare workers have had no objective way to tell the sufferers from the fakers, until now.
Real or fake tremors?
Researchers tested the new app—that uses data from an iPod’s built-in accelerometer to measure the frequency of tremor for both hands for 20 seconds—on 49 patients experiencing tremors in the emergency room, and 12 nurses trying to mimic the symptom.
Three-quarters of patients with genuine symptoms had tremors with an average peak frequency higher than seven cycles per second. Only 17 percent of nurses trying to “fake” a withdrawal tremor were able to produce a tremor with the same characteristics, suggesting that this may be a reasonable cut-off for discriminating real from fake.
In the emergency room, clinicians filmed patients’ hand tremor while using the app and showed the footage to doctors afterward. The app’s ability to assess tremor strength matched that of junior physicians. More senior doctors were able to judge symptoms with better accuracy.
The next step is to continue honing the tool and comparing its performance to doctors’ subjective assessments, and to further study the effects of left- or right-handedness, says Narges Norouzi, PhD candidate at University of Toronto.
“There’s so much work to do in this field,” she says. “There is other work out there on Parkinson’s tremors, but much less on tremors from alcohol withdrawal.”
“The exciting thing about our app is that the implications are global,” says Bjug Borgundvaag, professor of medicine, who is also an emergency physician at the Schwartz/Reisman Emergency Centre at Mount Sinai Hospital.
“Alcohol-related illness is commonly encountered not only in the emergency room, but also elsewhere in the hospital, and this gives clinicians a much easier way to assess patients using real data.
“Our app may also be useful in assisting withdrawal management staff, who typically have no clinical training, and determining which patients should be transferred to the emergency department for medical treatment or assessment. We think our app has great potential to improve treatment for these patients overall.”
“We have just begun to scratch the surface of what is possible by applying signal processing and machine learning to body connected sensors,” says Parham Aarabi, professor of electrical and computer engineering.
“As sensors improve and algorithms become smarter, there’s a good chance that we may be able to solve more medical problems and make medical diagnosis more efficient.”
Norouzi and the team presented the work in August at the International Conference of the IEEE Engineering in Medicine and Biology Society in Chicago.
Source: University of Toronto