RESUMO
PURPOSE: To evaluate the effect of a broad range of covariates on the survival of a real-life long-term follow-up cohort of community-dwelling patients with behavioural and psychological symptoms of dementia who were new users of atypical antipsychotic medications (APMs). METHODS: This was a prospective cohort study of 1,618 subjects aged ≥65 years with dementia and BPSD ("behavioural and psychological symptoms of dementia") who were new users of atypical APMs and registered in a Dementia Evaluation Unit of Campania Region (Italy) from September 2006 to March 2010. The potential of baseline features to predict mortality was assessed with the Cox proportional hazards model. RESULTS: The average follow-up was 309 days. Of the 1,618 new users of atypical antipsychotics, 9.3 % experienced at least one adverse event, including death (5.1 %), drug therapeutic failure (3.0 %), extrapyramidal symptoms (0.5 %) and stroke (0.2 %). The crude all-cause mortality rate was 6.0 per 100 person-years [95 % confidence interval (CI) 4.8-7.4]; the rate was higher in patients aged >85 years (9.0 per 100 person-years, 95 % CI 6.4-12.7) and among male patients (7.5 per 100 person-years, 95% CI 5.3-10.6). In the multivariate analysis, only age was associated to all-cause mortality [hazard ratio (HR) 1.1; 95 % CI 1.0-1.1 and HR 1.4; 95 % CI: 0.9-2.2, respectively). In contrast, hallucination (HR 0.4; 95 % CI 0.2-0.6) and dosage change (HR 0.4; 95 % CI 0.2-0.78) were significantly associated with a lower risk of all-cause mortality. CONCLUSIONS: Among our patient cohort, the mortality rate of patients with BPSD receiving long-term treatment with atypical APMs was lower than that reported in other studies, and only age was found to be significant predictor factor of mortality.
Assuntos
Antipsicóticos/uso terapêutico , Demência/mortalidade , Idoso , Idoso de 80 Anos ou mais , Antipsicóticos/efeitos adversos , Estudos de Coortes , Demência/psicologia , Feminino , Humanos , Vida Independente , Itália/epidemiologia , Masculino , FarmacovigilânciaRESUMO
In the framework of symbolic data analysis (SDA), distribution-valued data are defined as multivalued data, where each unit is described by a distribution (e.g., a histogram, a density, or a quantile function) of a quantitative variable. SDA provides different methods for analyzing multivalued data. Among them, the most relevant techniques proposed for a dimensional reduction of multivalued quantitative variables is principal component analysis (PCA). This paper gives a contribution in this context of analysis. Starting from new association measures for distributional variables based on a peculiar metric for distributions, the squared Wasserstein distance, a PCA approach is proposed for distribution-valued data, represented by quantile-variables. An application of the proposed PCA method, performed on simulated distribution-valued data, shows interesting interpretative results in terms of location, variability, and shape of the distributions on the factorial planes.