Real-world effectiveness, its predictors and onset of action of cholinesterase inhibitors and memantine in dementia: retrospective health record study

Abstract

Background: The efficacy of acetylcholinesterase inhibitors and memantine in the symptomatic treatment of Alzheimer’s disease is well-established. Randomised trials have shown them to be associated with a reduction in the rate of cognitive decline. Aims: To investigate the real-world effectiveness of acetylcholinesterase inhibitors and memantine for dementia-causing diseases in the largest UK observational secondary care service data-set to date. Method: We extracted mentions of relevant medications and cognitive testing (Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores) from de-identified patient records from two National Health Service (NHS) trusts. The 10-year changes in cognitive performance were modelled using a combination of generalised additive and linear mixed-effects modelling. Results: The initial decline in MMSE and MoCA scores occurs approximately 2 years before medication is initiated. Medication prescription stabilises cognitive performance for the ensuing 2–5 months. The effect is boosted in more cognitively impaired cases at the point of medication prescription and attenuated in those taking antipsychotics. Importantly, patients who are switched between agents at least once do not experience any beneficial cognitive effect from pharmacological treatment. Conclusions: This study presents one of the largest real-world examination of the efficacy of acetylcholinesterase inhibitors and memantine for symptomatic treatment of dementia. We found evidence that 68% of individuals respond to treatment with a period of cognitive stabilisation before continuing their decline at the pre-treatment rate.

Andrey Kormilitzin

Senior Researcher

My research is centred around translating advances in mathematics, statistical machine learning and deep learning to address challenges involved in learning, inference and ethical decision making using complex biomedical and health data.

Alejo J Nevado-Holgado

Associate Professor

I am an Associate Professor of the Department of Psychiatry and the Big Data Institute, and part of Dementia Research Oxford. I am very glad to supervise the AI team in the TNDR, formed by 10 excellent machine learners and bioinformaticians. Our focus is on the applications of machine learning and bioinformatics to mental health care. In addition, I also hold a position at the Big Data Institute, where we collaborate in the application of machine learning to genomics and target discovery. I am also consultant to a number of AI companies.