Noninvasive drug adherence monitoring of antipsychotic patients via finger sweat testing

Collection of finger sweat is explored here as a rapid and convenient way of monitoring patient adherence to antipsychotic drugs. Finger sweat samples (n = 426) collected from patients receiving treatment with clozapine, quetiapine and olanzapine were analysed by liquid chromatography mass spectrometry, including a subgroup of patients with paired plasma samples. Finger sweat samples were also analysed from a negative control group and patients who had handled antipsychotic medication only. The finger sweat test (based on the detection of parent drug in one donated sample) was 100% effective in monitoring adherence within commonly prescribed dosing ranges. In comparison to participants who handled the medication only, the test could distinguish between contact and administration through monitoring of the drug metabolite, or the level of parent drug. Additionally, in a subgroup of patients prescribed clozapine, a statistically significant correlation was observed between the mass of parent drug in finger sweat and plasma concentration. The finger sweat technology shows promise as a dignified, noninvasive method to monitor treatment adherence in patients taking antipsychotics.

1362 ANTICHOLINERGIC PRESCRIBING HABITS AND ITS ASSOCIATIONS IN A COMMUNITY POPULATION OF PEOPLE LIVING WITH DEMENTIA

Introduction

Many commonly prescribed medications have inadvertent anticholinergic effects. People with Dementia (PwD) are more vulnerable to these effects and at risk of adverse outcomes, the risk being higher with a greater degree of anticholinergic exposure. We investigated prescribing patterns and Anticholinergic burden (ACB) in a cohort of community-dwelling older adults with dementia and aimed to explore the effect of ACB on cognition, mood, and quality of life(QoL).

Method

The medication and demographic information for 87 (39 female) community-dwelling PwD were obtained from Electronic Care Summaries. We used the German Anticholinergic Burden Scale (GABS) to measure ACB. Additionally, we investigated associations between ACB and cognitive (ADAS-Cog), functional (BADL) and QoL (DemQoL) assessments.

Results

28.7% of participants had a clinically significant score (ACB> 2). The most commonly prescribed medications with ACB were Lansoprazole(18.3%), Mirtazapine(12.6%) and Codeine(12.6%). ACB was higher in males and negatively correlated with age, r(87)=-.21,p=.03. There was no association between ACB and cognition, QoL, functional independence, and neuropsychiatric symptoms. Over six months, PLWD with no ACB had a greater negative change in neuropsychiatric symptoms[t(18)=2.27,p=.04] and functional independence[t(23)=-3.8,p=.001], indicating greater dependence and worsening neuropsychiatric symptoms.

Conclusion

A third of PLWD in the community had clinically significant ACB. No ACB was associated with worsening neuropsychiatric symptoms and functional dependence over a six-month period. Community prescribers should consider regular medication reviews with PLWD and carers to ensure medications are prescribed safely and appropriately.

The use of artificial intelligence and machine learning in the care of people with dementia: A literature review

Introduction

Artificial intelligence and machine learning are increasingly being researched within the field of psychiatry to find out what use it might be. With this review, therefore, we would like to assess what literature, if any, exists that answers the question of whether this technology can be useful for providing dementia care. We also wanted to consider the ethical questions of autonomy, consent and privacy when working with this vulnerable group of patients.

Objectives

To identify and appriase the literature to assess the existing research landscape of the area of machine learning and AI, relating to the care of people with dementia.

Methods

A literature search was conducted, searching the PsychInfo, Medline, PubMed and Embase databases. We assessed the quality of the research and considered what overall findings there were in the existing literature.

Results

619 papers were identified, of which 28 related to the use of AI in the care of people with dementia. The papers were divided into categories to show the utility and effectiveness these technologies may have: 1: to alert caregivers to problems 2: to facilitate activities for people with dementia 3: to help plan care for people with dementia 4: to consider the ethical implications of the use of artificial intelligence and machine learning

Conclusions

Despite a paucity of literature in the area, existing studies show potential, if used well, for technologies to be a useful addition to care of people with dementia. The experience of patients and their carers must be integral to their development and use.