Prognostic importance of apathy in syndromes associated with frontotemporal lobar degeneration

Author:

Lansdall Claire J.,Coyle-Gilchrist Ian T.S.,Vázquez Rodríguez Patricia,Wilcox Alicia,Wehmann Eileen,Robbins Trevor W.,Rowe James B.

Abstract

ObjectiveTo determine the influence of apathy, impulsivity, and behavioral change on survival in patients with frontotemporal dementia, progressive supranuclear palsy, and corticobasal syndrome.MethodsWe assessed 124 patients from the epidemiologic PiPPIN (Pick's Disease and Progressive Supranuclear Palsy, Prevalence and Incidence) study. Patients underwent detailed baseline cognitive and behavioral assessment focusing on apathy, impulsivity, and behavioral change. Logistic regression identified predictors of death within 2.5 years from assessment, including age, sex, diagnosis, cognition, and 8 neurobehavioral profiles derived from a principal component analysis of neuropsychological and behavioral measures.ResultsAn apathetic neurobehavioral profile predicted death (Wald statistic = 8.119,p= 0.004, Exp(B) = 2.912, confidence interval = >1 [1.396–6.075]) and was elevated in all patient groups. This profile represented apathy, weighted strongly to carer reports from the Apathy Evaluation Scale, Neuropsychiatric Inventory, and Cambridge Behavioral Inventory. Age at assessment, sex, and global cognitive impairment were not significant predictors. Differences in mortality risk across diagnostic groups were accounted for by their neuropsychiatric and behavioral features.ConclusionsThe relationship between apathy and survival highlights the need to develop more effective and targeted measurement tools to improve its recognition and facilitate treatment. The prognostic importance of apathy suggests that neurobehavioral features might be useful to predict survival and stratify patients for interventional trials. Effective symptomatic interventions targeting the neurobiology of apathy might ultimately also improve prognosis.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Neurology (clinical)

Reference41 articles.

1. Including all predictors resulted in a significant fit to the model (−2 log likelihood = 88.401, χ2 = 29.9, degrees of freedom [df] = 14, p = 0.008, Cox and Snell R 2 = 0.288, Nagelkerke R 2 = 0.390). Model classification accuracy improved from 60.2% at baseline (including only a constant) to 73.9% following inclusion of the predictor variables. The model correctly classified 43 as alive while incorrectly classifying 13, and correctly classified 22 as dead while incorrectly classifying an additional 10, resulting in a positive predictive value of 81% and negative predictive value of 63% (positive predictive value = 43/43 + 10 = 0.811, negative predictive value = 22/22 + 13 = 0.629).

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