Posts Tagged Primary care

[WEB SITE] AI helps identify patients in need of advanced care for depression

Depression is a worldwide health predicament, affecting more than 300 million adults. It is considered the leading cause of disability and contributor to the overall global burden of disease. Detecting people in need of advanced depression care is crucial.

Now, a team of researchers at the Regenstrief Institute found a way to help clinicians detect and identify patients in need of advanced care for depression. The new method, which uses machine learning or artificial intelligence (AI), can help reduce the number of people who experience depressive symptoms that could potentially lead to suicide.

The World Health Organization (WHO) reports that close to 800,000 people die due to suicide each year, making it the leading cause of death among people between the ages of 15 and 29 years old.

Major depression is one of the most common mental illness worldwide. In the United States, an estimated 17.3 million adults had at least one major depressive episode, accounting to about 7.1 percent of all adults in the country.

Image Credit: Zapp2Photo / Shutterstock

Image Credit: Zapp2Photo / Shutterstock

Predicting patients who need treatment

The study, which was published in the Journal of Medical Internet Research, unveils a new way to determine patients who might need advanced care for depression. The decision model can predict who might need more treatment than what the primary care provider can offer.

Since some forms of depression are far more severe and need advanced care by certified medical health providers, knowing who is at risk is essential. But identifying these patients is very challenging. In line with this, the researchers formulated a method that scrutinizes a comprehensive range of patient-level diagnostic, behavioral, and demographic data, including past clinic visit history from a statewide health information.

Using the data, health care providers can now build a technique on properly predicting patients in need of advanced care. The machine learning algorithm combined both behavioral and clinical data from the statewide health information exchange, called the Indiana Network for Patient Care.

“Our goal was to build reproducible models that fit into clinical workflows,” Dr. Suranga N. Kasthurirathne, a research scientist at Regenstrief Institute, and study author said.

“This algorithm is unique because it provides actionable information to clinicians, helping them to identify which patients may be more at risk for adverse events from depression,” he added.

The researchers used the new model to train random forest decision models that can predict if there’s a need for advanced care among the overall patient population and those at higher risk of depression-related adverse events.

It’s important to consider making models that can fit different patient populations. This way, the health care provider has the option to choose the best screening approach he or she needs.

“We demonstrated the ability to predict the need for advanced care for depression across various patient populations with considerable predictive performance. These efforts can easily be integrated into existing hospital workflows,” the investigators wrote in the paper.

Identifying patients in need of advanced care is important

With the high number of people who have depression, one of the most important things to do is determine who are at a higher risk of potential adverse effects, including suicide.

Depression has different types, depending on the level of risk involved. For instance, people with mild depression forms may not need assistance and can recover faster. On the other hand, those who have severe depression may require advanced care aside from what primary care providers can offer.

They may need to undergo treatment such as medications and therapies to improve their condition. Hence, the new method can act like a preventive measure to reduce the incidence of adverse events related to the condition such as suicide.

More importantly, training health care teams to successfully identify patients with severe depression can help resolve the problem. With the proper application of the novel technique, many people with depression can be treated accordingly, reducing serious complications.

Depression signs and symptoms

Health care providers need to properly identify patients with depression. The common signs and symptoms of depression include feelings of hopelessness and helplessness, loss of interest in daily activities, sleep changes, irritability, anger, appetite changes, weight changes, self-loathing, loss of energy, problems in concentrating, reckless behavior, memory problems, and unexplained pains and aches.


Journal reference:

Suranga N Kasthurirathne, Paul G Biondich, Shaun J Grannis, Saptarshi Purkayastha, Joshua R Vest, Josette F Jones. (2019). Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach. Journal of Medical Internet Research. https://www.jmir.org/2019/7/e13809/


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[WEB SITE] Transcutaneous electrical stimulation (TENS) may help lower limb spasticity after stroke

Adult using TENS machine for lower limb pain

Published on 26 February 2019

doi: 10.3310/signal-000738

Transcutaneous electrical stimulation (TENS) delivered alongside standard physical therapies could reduce spasticity in the lower limbs following a stroke.

Spasticity is a muscle control disorder characterised by tight muscles. It is common after stroke and accounts for significant disability. TENS is often used to treat pain and can affect nervous stimulation of the muscles.

The main evidence in this systematic review came from five trials which suggested that TENS combined with other physical therapies has moderate effect on lower limb spasticity compared with placebo.

The review has limitations, with small studies and little evidence on use for upper limbs or comparing with other therapies. However, TENS machines are portable, inexpensive and widely accessible making them an appealing addition to other care.

NICE does not currently recommend the use of TENS in stroke rehabilitation, though guidance covers use of other types of electrical stimulation in certain other contexts.

Why was this study needed?

More than 1.2 million people in the UK are living with the effects of stroke. About two-thirds of stroke survivors leave hospital with residual disability and one quarter experience spasticity.

Electrical stimulation is sometimes used as treatment after a stroke. It includes functional electrical stimulation and neuromuscular electrical stimulation, which both focus on muscle contraction. Transcutaneous electrical stimulation (TENS) targets the sensory nerves in a different way.

Transcutaneous electrical stimulation has been suggested as an adjunct to other rehabilitation therapy to try and reduce spasticity. The device is portable and can be self-administered at home, so its potential for managing spasticity is appealing.

There have been a number of small studies of TENS with conflicting results. This review aimed to combine the results to see if there was evidence for its use to treat spasticity after stroke.

What did this study do?

This systematic review identified 15 studies (10 randomised controlled trials) reporting the effectiveness of TENS on spasticity after stroke.

Studies compared TENS, used alone or alongside other therapies such as functional exercises, with placebo, no treatment or other treatments. Thirteen studies assessed lower limb spasticity, with 11 targeting the ability to flex the foot. Most assessed use in the chronic rather than acute phase of stroke.

Transcutaneous electrical stimulation regimen varied widely. Intervention periods ranged from one day to 12 weeks, the number of TENS sessions from one to seven per week, and the duration of sessions ranged from less than 20 minutes up to 60 minutes.

Trials were small with maximum participant size 80. The quality of randomised controlled trials was good overall, with lack of participant blinding being the most likely source of bias. Seven trials were pooled in meta-analysis.

What did it find?

  • Transcutaneous electrical stimulation used alongside other physical therapies was moderately effective in reducing spasticity in the lower limbs compared with placebo (standard mean difference [SMD] -0.64, 95% confidence interval [CI] -0.98 to -0.31). This was from meta-analysis of five trials (221 adults) with broadly similar results.
  • Pooled results of two trials (60 adults) also found that TENS alongside other physical therapies was more effective at reducing spasticity than no TENS (SMD -0.83, 95% CI -1.51 to -0.15).
  • Five studies assessed longer-term effects on spasticity. Three studies found the effects were maintained for a period of two to five weeks whilst two studies found the effects lasted for less than a day and that spasticity returned to baseline levels immediately following the intervention.
  • None of the studies reported any adverse effects of TENS.

What does current guidance say on this issue?

The NICE guideline on stroke rehabilitation (2013) does not currently include recommendations for use of TENS. NICE advises against the routine use of electrical stimulation for the hand and arm but suggests a trial of treatment may be considered if there is sign of muscle contraction, and the person cannot move their arm against resistance.

NICE guidance from 2009 advises that there is sufficient evidence that functional electrical stimulation can improve walking in people with drop foot following a stroke, provided the normal arrangements are in place for clinical governance, consent and audit.

What are the implications?

This review suggests that TENS, when delivered alongside other physical therapies, could be considered for lower limb spasticity as part of a stroke rehabilitation programme.

The findings are similar to a 2015 systematic review which found that electrical stimulation gave small but significant improvements in spasticity following stroke. Again this earlier review was limited by small sample sizes, varied treatment regimens and few studies that could be pooled in meta-analysis.

There was insufficient evidence to support use for upper limbs.

Cost was not assessed, but TENS is a non-invasive therapy and devices are widely available and could easily be used at home.

Citation and Funding

Mahmood A, Veluswamy SK, Hombali A, et al. Effect of transcutaneous electrical nerve stimulation on spasticity in adults with stroke: a systematic review and meta-analysis. Arch Phys Med Rehabil. 2018; 16 November. doi: 10.1016/j.apmr.2018.10.016. [Epub ahead of print].

No funding information was provided for this study.

Bibliography

NICE. Functional electrical stimulation for drop foot of central neurological origin. IPG278. London: National Institute for Health and Care Excellence; 2009.

NICE. Stroke rehabilitation in adults. CG162. London: National Institute for Health and Care Excellence; 2013.

NICE. Spasticity (after stroke) – botulinum toxin type A. ID768. London: National Institute for Health and Care Excellence; in development.

Stein C, Fritsch CG, Robinson C et al. Effects of electrical stimulation in spastic muscles after stroke: systematic review and meta-analysis of randomized controlled trials. Stroke. 2015;46(8):2197-205.

Stroke Association. State of the nation: stroke statistics. London: Stroke Association; 2018.

 

  1. Analysis of the Faster Knee-Jerk In the Hemiplegic Limb
    TAKAO NAKANISHI et al., JAMA Neurology, 1965
  2. Transcutaneous Electrical Stimulation
    WILLIAM BAUER et al., JAMA Otolaryngology Head Neck Surgery, 1986

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