Anosognosia in Early- and Late-Onset Dementia and Its Association With Neuropsychiatric Symptoms

Background: The symptom anosognosia or unawareness of disease in dementia has mainly been studied in patients with late-onset dementia (LOD, ≥65 years), whereas little is known on whether it is also present in patients with early-onset dementia (EOD, <65 years). We aimed at investigating differences in anosognosia between LOD and EOD, by also studying its association with different clinical variants of EOD and the presence of neuropsychiatric symptoms. Methods: A total of 148 patients, 91 EOD and 57 LOD, were recruited and underwent extended clinical assessment and caregiver interview that included questionnaires aimed at measuring anosognosia and neuropsychiatric symptoms. Differences in anosognosia between EOD and LOD and between subgroups with different clinical variants were investigated, as well as correlation between anosognosia and neuropsychiatric symptoms. A regression analysis was applied to explore the association between anosognosia and development of neuropsychiatric symptoms during disease progression. Results: Median levels of anosognosia were not significantly different between EOD and LOD. Anosognosia increased overtime with disease progression and was higher in frontotemporal dementia patients or, more precisely, in frontotemporal dementia and Alzheimer's disease variants associated with involvement of the frontal lobes. Higher levels of early anosognosia were associated with higher frequency and severity of subsequent neuropsychiatric symptoms, in particular apathy, later in the course of the disease. Conclusion: Anosognosia is a frequent symptom of EOD, occurring in 94.5% of all-cause EOD, and it is associated with higher risk of developing neuropsychiatric symptoms during disease progression. Recognising anosognosia may be helpful for clinicians and families to reduce diagnostic delay and improve disease managment

Epidemiology of early onset dementia and its clinical presentations in the province of Modena, Italy

Introduction: Patients with early onset dementia (EOD), defined as dementia with symptom onset at age <65, frequently present with atypical syndromes. However, the epidemiology of different EOD presentations, including variants of Alzheimer's disease (AD) and frontotemporal dementia (FTD), has never been investigated all together in a population-based study. Epidemiologic data of all-cause EOD are also scarce. Methods: We investigated EOD epidemiology by identifying patients with EOD seen in the extended network of dementia services of the Modena province, Northern Italy ( 48700,000 inhabitants) from 2006 to 2019. Results: In the population age 30 to 64, incidence was 13.2 per 100,000/year, based on 160 new cases from January 2016 to June 2019, and prevalence 74.3 per 100,000 on June 30, 2019. The most frequent phenotypes were the amnestic variant of AD and behavioral variant of FTD. Discussion: EOD affects a significant number of people. Amnestic AD is the most frequent clinical presentation in this understudied segment of the dementia population

Prevalenza ed impatto sociale delle demenze ad esordio precoce (Early onset dementia-EOD) nella provincia di Modena

Introduzione. Le demenze in cui l’esordio dei sintomi si verifica prima dei 65 anni (Early Onset Dementia-EOD), hanno caratteristiche cliniche ed impatto socio-sanitario diversi rispetto alle demenze ad esordio tardivo. I dati epidemiologici disponibili sono scarsi, con stime di prevalenza variabili tra 15 e 150/100.000 soggetti appartenenti alla fascia di età 45-64 anni. Non sono attualmente disponibili dati di prevalenza in Italia. Metodi. Abbiamo ricercato i casi di EOD in pazienti residenti in Provincia di Modena, esaminando i pazienti valutati presso i Centri per i Disturbi Cognitive e Demenze (CDCD) della provincia (2 neurologici, 8 geriatrici dislocati in tutto il territorio provinciale) tra il 1/1/2006 e il 31/12/2018. I casi con esordio antecedente al 1/1/2017 sono stati identificati retrospettivamente, mentre i casi con esordio successivo sono stati accertati prospetticamente, mediante invio da parte dei Medici di Medicina Generale e dai Geriatri ai due CDCD neurologici della provincia. La diagnosi di EOD è stata posta da un Neurologo esperto in disturbi cognitivi. Abbiamo incluso nello studio i pazienti con diagnosi di EOD al 31/12/2018. Abbiamo esaminato diagnosi, età di insorgenza, età e gravità dei disturbi cognitivi alla diagnosi. Per un sottogruppo di pazienti abbiamo inoltre raccolto variabili socio-demografiche quali la composizione del nucleo familiare e la condizione occupazionale del paziente e del caregiver principale. Risultati. Abbiamo identificato 248 pazienti con EOD al 31/12/2018, con una prevalenza di 116,5/100.000 soggetti a rischio nella fascia di età 45-64 anni e 71,8/100.000 nella fascia di età 30-64 anni. Il 41% dei pazienti ha ricevuto una diagnosi di malattia di Alzheimer (31% non amnesici), il 26% di demenza fronto-temporale, il 10% di demenza vascolare, il 7% demenza in parkinsonismo. L’età media all’esordio è stata di 58,9 anni (range 39-64), l’età media alla diagnosi è stata di 61,9 (range 40-72) con un significativo ritardo diagnostico. Il MMSE medio alla diagnosi è risultato pari a 22,2/30 (range 10-28). Il 22% dei pazienti svolgeva attività lavorativa alla diagnosi, mentre il 24% è stato costretto a lasciare il lavoro a causa della malattia. Il 3,4% dei pazienti aveva figli minori al momento della diagnosi. I caregivers, aventi età media di 59 anni, per il 70% svolgevano attività lavorativa, sperimentando una perdita media di 2 giornate lavorative al mese a causa della malattia del familiare. Il 7,4% e 20,5% dei pazienti ha usufruito rispettivamente di un centro diurno o struttura protetta, che tuttavia in nessun caso era specificamente dedicata a pazienti con EOD. Conclusioni. I dati raccolti permettono per la prima volta di valutare il numero e le caratteristiche dei pazienti con EOD nella Provincia di Modena. I risultati ottenuti saranno essenziali per organizzare servizi appropriati, dal punto di vista sanitario e socioassistenziale, per pazienti con EOD ed i loro familiari

Trivariate Bernoulli distribution with application to software fault tolerance

The widespread reliance on software for mission and life critical applications makes the reliability of these systems essential. Techniques such as fault tolerance have been proposed to achieve the highest levels of software reliability. However, the fault tolerance paradigm suffers from the risk of correlated failures, where a majority of the software versions fail on the same input leading to system failure. This paper derives a trivariate Bernoulli distribution to quantify the negative impact of correlated failures on the reliability of fault tolerant software composed of highly reliable versions. An experiment based on early empirical research demonstrates the capacity of the distribution to conduct reliability assessment for many combinations of the version reliabilities and correlations. The results indicate that correlated failures detract from system reliability, but that this reliability is often higher than a system composed of the single most reliable version

Recovery block fault tolerance considering correlated failures

Software fault tolerance methods have been proposed to achieve high reliability. However, these methods suffer from the possibility of correlated failures, where the failure of multiple components leads to system failure. Furthermore, previous methods to assess the impact of correlated failure on software fault tolerance require extensive testing data and are therefore less suitable to conduct reliability analysis in the early design stages. This paper presents an approach to quantify the reliability of the recovery block method to software fault tolerance in terms of the reliability of the components and the correlations between the failures of these components. The approach is demonstrated through a series of examples. Our results indicate that the approach can quantify the negative impact of correlation on the recovery block. Thus, the approach can be used to identify correlations that may impede system reliability

Reliability of systems with identically distributed correlated components

The vast majority of reliability engineers and many researchers continue to base their models on the assumption that components fail in a statistically independent manner. This assumption is generally untenable because correlated failures can lower the reliability of a fault tolerant system. A simple method that quantifies the impact of correlation among the components of a system is needed to eliminate this widespread practice. Previous approaches to model correlated component failures have not gained popularity because they rely on excessively sophisticated mathematical approaches, making them cumbersome to apply in practice. This paper proposes a method to model the reliability of systems with correlated identical components, where the components exhibit the same average reliability and also possess a common failure correlation parameter. The method is applicable to the class of coherent systems commonly modelled by reliability block diagrams and fault trees. The utility of the approach is demonstrated through a series of examples, including the derivation of several commonly used structures such as the k-out-of-n and parallel systems. The examples illustrate how the method facilitates the assessment of component correlation on system reliability

Discrete software reliability growth model based on maximum entropy principle with higher order polynominial moment contraints

Software managers depend on software reliability growth models (SRGM) during the testing phase in order to gauge when an application will be ready for release. However, many SRGM introduce an unnecessarily large number of parameters to fit the observed failure data more precisely, but in doing so compromise the ability of these models to accurately predict future failures. This paper presents a nonparametric software reliability growth model based on the Maximum Entropy Principle (MEP) with moment constraints, which provides an unbiased method to fit the observed data in a manner that is maximally noncommittal with regard to missing information. The MEP model is applied to a widely studied software failure data set from the historical literature. The results indicate that the MEP SRGM imposing first and second moment constraints achieves greater predictive accuracy than an earlier model based only on the first moment constraint. It is also shown that more complex MEP models utilizing third and fourth moment constraints actually produce worse predictions than the second order model. Thus, the proposed approach can avoid model over fitting by identifying the order of the MEP model that most accurately predicts future failures for a given data set

An adaptive em algorithm for the maximum likelihood estimation of non-homogeneous poisson process software reliability growth models

Non-homogeneous Poisson process (NHPP) software reliability growth models (SRGMa) enable quantitative metrics to guide decisions during the software engineering life cycle, including test resource allocation and release planning. However, many SRGM possess complex mathematical forms that make them difficult to apply. Specifically, traditional procedures solve a system of nonlinear equations to identify the numerical parameters that best characterize failure data. Recently, researchers have developed expectation-maximization (EM) algorithms for NHPP SRGM that exhibit better convergence properties and can therefore find maximum likelihood estimates with greater ease. This paper presents an adaptive EM (AEM) algorithm, which combines an earlier EM algorithm for NHPP SRGM with unconstrained search of the model parameter space. Our performance analysis shows that the AEM outperforms state-of-the-art EM algorithms for NHPP SRGM with very strong statistical significance, which is as much as hundreds of times faster on some data sets. Thus, the approach can fit SRGM very quickly. We also incorporate this high performance adaptive EM algorithm into a heuristic nested model selection procedure to objectively select a model of least complexity that best characterizes the failure data. Results indicate this heuristic approach often identifies the model possessing the best model selection criteria

Performance optimized expectation conditional maximization algorithms for nonhomogeneous poisson process software reliability models

Nonhomogeneous Poisson process (NHPP) and software reliability growth models (SRGM) are a popular approach to estimate useful metrics such as the number of faults remaining, failure rate, and reliability, which is defined as the probability of failure free operation in a specified environment for a specified period of time. We propose performance-optimized expectation conditional maximization (ECM) algorithms for NHPP SRGM. In contrast to the expectation maximization (EM) algorithm, the ECM algorithm reduces the maximum-likelihood estimation process to multiple simpler conditional maximization (CM)-steps. The advantage of these CM-steps is that they only need to consider one variable at a time, enabling implicit solutions to update rules when a closed form equation is not available for a model parameter. We compare the performance of our ECM algorithms to previous EM and ECM algorithms on many datasets from the research literature. Our results indicate that our ECM algorithms achieve two orders of magnitude speed up over the EM and ECM algorithms of [1] when their experimental methodology is considered and three orders of magnitude when knowle dge of the maximum-likelihood estimation is removed, whereas our approach is as much as 60 times faster than the EM algorithms of [2] . We subsequently propose a two-stage algorithm to further accelerate performance