Classification of Neuropsychiatric Symptoms Requiring Antipsychotic Treatment in Patients with Alzheimer's Disease: Analysis of the CATIE-AD Study

Abstract

Background: The Neuropsychiatric Inventory (NPI) comprises 12 items, which were conventionally determined by psychopathological symptoms of patients with dementia. The clinical rating scales with structured questionnaires have been useful to evaluate neuropsychiatric symptoms (NPSs) of patients with dementia over the past twenty year.

Objective: The aim of this study was to classify the conventional NPSs in patients with Alzheimer's disease (AD) requiring antipsychotic treatment for their NPSs into distinct clusters to simplify assessment of these numerous symptoms.

Methods: Twelve items scores (product of severity and frequency of each symptom) in the NPI taken from the baseline visit were classified into subgroups by principle component analysis using data from 421 outpatients with AD enrolled in the Clinical Antipsychotic Trials of Intervention Effectiveness-Alzheimer's Disease (CATIE-AD) Phase 1. Chi square tests were conducted to examine the co-occurrence of the subgroups.

Results: We found four distinct clusters: aggressiveness (agitation and irritabilities), apathy and eating problems (apathy and appetite/eating disturbance), psychosis (delusions and hallucinations), and emotion and disinhibition (depression, euphoria, and disinhibition). Anxiety, aberrant motor behavior, and sleep disturbance were not included by these clusters. Apathy and eating problems, and emotion and disinhibition co-occurred (p = 0.002), whereas aggressiveness and psychosis occurred independent of the other clusters.

Conclusions: Four distinct category clusters were identified from NPSs in patients with AD requiring antipsychotic treatment. Future studies should investigate psychosocial backgrounds or risk factors of each distinct cluster, in addition to their longitudinal course over treatment intervention.

Keywords: Alzheimer’s disease; delusions; neuropsychiatric symptoms; principal component analysis.