Dosing of valproate in an elderly population with dementia: Evaluation of discontinuations and dose reductions

Bipolar Disorder and Manic-Like Symptoms in Alzheimer's, Vascular and Frontotemporal Dementia: A Systematic Review

BACKGROUND

An increased risk of manic episodes has been reported in patients with neurodegenerative disorders, but the clinical features of bipolar disorder (BD) in different subtypes of dementia have not been thoroughly investigated.

OBJECTIVES

The main aim of this study is to systematically review clinical and therapeutic evidence about manic syndromes in patients with Alzheimer's disease (AD), vascular dementia (VaD), and frontotemporal dementia (FTD). Since manic-mixed episodes have been associated to negative outcomes in patients with dementia and often require medical intervention, we also critically summarized selected studies with relevance for the treatment of mania in patients with cognitive decline.

METHODS

A systematic review of the literature was conducted according to PRISMA guidelines. PubMed, Scopus, and Web of Science databases were searched up to February 2022. Sixty-one articles on patients with AD, VaD, or FTD and BD or (hypo) mania have been included.

RESULTS

Manic symptoms seem to be associated to disease progression in AD, have a greatly variable temporal relationship with cognitive decline in VaD, and frequently coincide with or precede cognitive impairment in FTD. Overall, mood stabilizers, and electroconvulsive therapy may be the most effective treatments, while the benefits of short-term treatment with antipsychotic agents must be balanced with the associated risks. Importantly, low-dose lithium salts may exert neuroprotective activity in patients with AD.

CONCLUSION

Prevalence, course, and characteristics of manic syndromes in patients with dementia may be differentially affected by the nature of the underlying neurodegenerative conditions.

Artificial Neural Network vs. Pharmacometric Model for Population Prediction of Plasma Concentration in Real-World Data: A Case Study on Valproic Acid

We compared the predictive performance of an artificial neural network to traditional pharmacometric modeling for population prediction of plasma concentrations of valproate in real-world data. We included individuals aged 65 years or older with epilepsy who redeemed their first prescription of valproate after the diagnosis of epilepsy and had at least one valproate plasma concentration measured. A long short-term memory neural network (LSTM) was developed using the training data set to fit the LSTM and the test data set to validate the model. Predictions from the LSTM were compared with those obtained from the population predictions from a pharmacometric model by Birnbaum et al. which had the best predictive performance for population predictions of valproate concentrations in Danish databases. We used the cutoff of ± 20 mg/L of prediction error to define good predictions. A total of 1,252 individuals were included in the study. The LSTM fitted using the training data set had poor predictive performance in the test data set, but better than that of the pharmacometric model. The proportion of individuals with at least one predicted concentration within ± 20 mg/L of observed concentration was largest in case of the LSTM (64.4%, 95% confidence interval (CI): 58.4-70.2%) compared with the pharmacometric model by Birnbaum et al. (49.8%, 95% CI: 47.0-52.6%). LSTM shows better predictive performance to predict valproate plasma concentrations compared with a traditional pharmacometric model in the investigated setting with real-world data in older patients with epilepsy where information on exact timepoints for both dosing and plasma concentration measurement are missing.