Artificial intelligence might help doctors in determining whether individuals are likely to have adverse effects from widely used antidepressants, antihistamines, and bladder medications.
An evaluation of a new tool to determine which medications are more likely to have adverse anticholinergic effects on the body and brain was conducted under the direction of the University of Exeter and the Kent and Medway NHS and Social Care Partnership Trust. Their findings were recently published in the journal Age and Ageing. Many prescription and over-the-counter medications that affect the brain by inhibiting the neurotransmitter acetylcholine may result in adverse anticholinergic effects. Numerous drugs, including certain bladder medications, antidepressants, stomach medicines, and Parkinson’s disease have some degree of anticholinergic impact. These types of drugs are often consumed by the elderly.
Anticholinergic adverse effects include disorientation, blurred vision, dizziness, falls, and a decline in brain function. The effects of anticholinergic drugs may also make people more likely to fall and may be associated with an increased risk of death. Long-term usage of them has also been linked to a higher risk of dementia.
New Tool for Assessing Medication Side Effects
Researchers have now created a tool that uses artificial intelligence to calculate the negative effects of medications. The researchers developed the International Anticholinergic Cognitive Burden Tool (IACT), an online tool that employs chemical structure analysis and natural language processing to identify drugs that have an anticholinergic effect.
This tool is the first to use machine learning technology to create an automatically updated tool that is accessible via a website portal. The anticholinergic burden is calculated by giving a score based on documented adverse events and closely matching the chemical structure of the medicine under consideration for prescription, resulting in a more accurate and up-to-date scoring system than any prior approach. Ultimately, the tool developed after further investigation and modeling using actual patient data could help to reduce risks from common medications.
Potential Impact on Personalized Medicine
Professor Chris Fox, at the University of Exeter, is one of the study authors. He said: “Use of medicines with anticholinergic effects can have significant harmful effects for example falls and confusion which are avoidable, we urgently need to reduce the harmful side effects as this can lead to hospitalization and death. This new tool provides a promising avenue towards a more tailored personalized medicine approach, of ensuring the right person gets a safe and effective treatment whilst avoiding unwanted anticholinergic effects.”
The team surveyed 110 health professionals, including pharmacists and prescribing nurses. Of this group, 85 percent said they would use a tool to assess the risk of anticholinergic side effects, if available. The team also gathered usability feedback to help improve the tool further.
Dr. Saber Sami, at the University of East Anglia, said: “Our tool is the first to use innovative artificial intelligence technology in measures of anticholinergic burden – ultimately, once further research has been conducted the tool should support pharmacists and prescribing health professionals in finding the best treatment for patients.”
Professor Ian Maidment, from Aston University, said: “I have been working in this area for over 20 years. Anticholinergic side effects can be very debilitating for patients. We need better ways to assess these side effects.”
Reference: “A novel Artificial Intelligence-based tool to assess anticholinergic burden: a survey ” by Agostina Secchi, Hulkar Mamayusupova, Saber Sami, Ian Maidment, Simon Coulton, Phyo Kyaw Myint and Chris Fox, 27 August 2022, Age and Ageing.
DOI: 10.1093/ageing/afac196
The research team includes collaboration with AKFA University Medical School, Uzbekistan, and the Universities of East Anglia, Aston, Kent, and Aberdeen. They aim to continue the development of the tool with the aim that it can be deployed in day-to-day practice which this study supports.
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