Category Archives: children & adolescents

NICE’s Early Value Assessment for Medtech

The explosion in digital health products has left National Health Service commissioners in the UK wondering how they can possibly sift out what works and what provides maximum benefit for the service and for patients.

The Early Value Assessment for Medtech will offer a rapid assessment of digital products, devices and diagnostics for clinical effectiveness and value for money. The goal of this new approach is that the service and patients will be able to benefit sooner.

Early Value Assessment is being designed to draw in the most promising and impactful medical technologies where the evidence base is still emerging, starting with digital products, in areas where there is greatest need.  Whether its empowering patients to better manage their own health and seek clinical advice, reducing admissions and waiting lists or supporting clinicians and other front-line staff to provide better quality care this new program will help to alleviate system pressures as the NHS recovers from the COVID pandemic.

In this accelerated approach, the first two pilot digital health topics will begin their early value assessments this month (June) with a view to publishing findings in October. This is much faster than a full NICE Medtech evaluation meaning benefits will be realized sooner, while companies generate more evidence required for a full NICE evaluation at a later stage.  

The first two pilots are digital apps for depression and anxiety in children and others will quickly follow in adult mental health, early cancer diagnosis, cardiovascular disease and other areas that support elective recovery following the pandemic.

In this ‘fast-track’ approach, NICE is working closely with NHS England and all its system partners to help develop commercial and data collection arrangements to support the technologies that go through early value assessment in adoption and scaling and to make this new program of work deliver on the potential of digital health for patients.

Source: NHS Blog

Predicting Early Antidepressant Outcomes For Children and Adolescents with MDD Using AI

Mayo Clinic researchers have taken the first step in using artificial intelligence (AI) to predict early outcomes with antidepressants in children and adolescents with major depressive disorder, in a study published in The Journal of Child Psychology and Psychiatry.

In the study, researchers identified variation in six depressive symptoms: difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem and depressed feelings.

They assessed these symptoms with the Children’s Depression Rating Scale-Revised to predict outcomes to 10 to 12 weeks of antidepressant pharmacotherapy:

  • The six symptoms predicted 10- to 12-week outcomes at four to six weeks in fluoxetine testing datasets, with an average accuracy of 73%.
  • The same six symptoms predicted 10- to 12-week outcomes at four to six weeks in duloxetine testing datasets, with an average accuracy of 76%.
  • In placebo-treated patients, predicting response and remission accuracy was significantly lower than for antidepressants at 67%.

According to the researchers, ‘these outcomes show the potential of AI and patient data to ensure children and adolescents receive treatment that has the highest likelihood of delivering therapeutic benefits with minimized side effects. We designed the algorithm to mimic a clinician’s logic of treatment management at an interim time point based on their estimated guess of whether a patient will likely or not benefit from pharmacotherapy at the current dose,” says Dr. Athreya, lead author. “Hence, it was essential for me as a computer engineer to embed and observe the practice closely to not only understand the needs of the patient, but also how AI can be consumed and useful to the clinician to benefit the patient.”

This preliminary work suggests that AI has promise for assisting clinical decisions by informing physicians on the selection, use and dosing of antidepressants for children and adolescents with major depressive disorder,” says Paul Croarkin, D.O., a Mayo Clinic psychiatrist and senior author of the study. “We saw improved predictions of treatment outcomes in samples of children and adolescents across two classes of antidepressants.”

Key points from the study

  • The optimal treatment of depression in children and adolescent is a substantial public health problem.
  • Machine learning and probabilistic graphical models were used to predict treatment outcomes with antidepressants in a training and testing databases.
  • Variation in six depression symptoms predicted outcomes with fluoxetine or duloxetine.
  • Future work should augment probabilistic graphical models with biological data to refine tools to assist decision making in clinical practice.

This work was a collaborative effort between the departments of Molecular Pharmacology and Experimental Therapeutics, and Psychiatry and Psychology, at Mayo Clinic, with support from Mayo Clinic’s Center for Individualized Medicine.

Study:
Athreya AP, Vande Voort JL, Shekunov J, et al. The Journal of Child Psychology and Psychiatry. 15 March 2022