Health technology assessment of pathogen reduction technologies applied to plasma for clinical use

Although existing clinical evidence shows that the transfusion of blood components is becoming increasingly safe, the risk of transmission of known and unknown pathogens, new pathogens or re-emerging pathogens still persists. Pathogen reduction technologies may offer a new approach to increase blood safety. The study is the output of collaboration between the Italian National Blood Centre and the Post-Graduate School of Health Economics and Management, Catholic University of the Sacred Heart, Rome, Italy. A large, multidisciplinary team was created and divided into six groups, each of which addressed one or more HTA domains.Plasma treated with amotosalen + UV light, riboflavin + UV light, methylene blue or a solvent/detergent process was compared to fresh-frozen plasma with regards to current use, technical features, effectiveness, safety, economic and organisational impact, and ethical, social and legal implications. The available evidence is not sufficient to state which of the techniques compared is superior in terms of efficacy, safety and cost-effectiveness. Evidence on efficacy is only available for the solvent/detergent method, which proved to be non-inferior to untreated fresh-frozen plasma in the treatment of a wide range of congenital and acquired bleeding disorders. With regards to safety, the solvent/detergent technique apparently has the most favourable risk-benefit profile. Further research is needed to provide a comprehensive overview of the cost-effectiveness profile of the different pathogen-reduction techniques. The wide heterogeneity of results and the lack of comparative evidence are reasons why more comparative studies need to be performed

Epilepsy in Neurodegenerative Dementias: A Clinical, Epidemiological, and EEG Study

BACKGROUND: Seizures are common in patients with dementia but precise epidemiologic data of epilepsy in neurodegenerative dementia is lacking. OBJECTIVE: The first aim of the study was to investigate prevalence and clinical characteristics of epilepsy in a large cohort of patients with neurodegenerative dementias. Subsequently, we explored clinical, neuropsychological, and quantitative electroencephalogram (qEEG) data of Alzheimer's disease (AD) patients with epilepsy (AD-EPI) as compared to AD patients without epilepsy (AD-CTR). METHODS: We retrospectively evaluated consecutive patients with a diagnosis of a neurodegenerative dementia and a clinically diagnosed epilepsy that required antiepileptic drugs (AED). All patients underwent baseline comprehensive neuropsychological assessment. A follow-up of at least one year was requested to confirm the dementia diagnosis. In AD patients, qEEG power band analysis was performed. AD-CTR and AD-EPI patients were matched for age, Mini-Mental State Examination score, and gender. RESULTS: Thirty-eight out of 2,054 neurodegenerative dementia patients had epilepsy requiring AED. The prevalence of epilepsy was 1.82% for AD, 1.28% for the behavioral variant of frontotemporal dementia (bvFTD), 2.47% for dementia with Lewy bodies (DLB), and 12% for primary progressive aphasia. Epilepsy were more drug-responsive in AD than in non-AD dementias. Finally, no significant differences were found in neuropsychological and qEEG data between AD-EPI and AD-CTR patients. CONCLUSION: In our cohort, AD, FTD, and DLB dementias have similar prevalence of epilepsy, even if AD patients were more responsive to AED. Moreover, AD-EPI patients did not have significant clinical, neuropsychological qEEG differences compared with AD-CTR patients

Actigraphic Sensors Describe Stroke Severity in the Acute Phase: Implementing Multi-Parametric Monitoring in Stroke Unit

: Actigraphy is a tool used to describe limb motor activity. Some actigraphic parameters, namely Motor Activity (MA) and Asymmetry Index (AR), correlate with stroke severity. However, a long-lasting actigraphic monitoring was never performed previously. We hypothesized that MA and AR can describe different clinical conditions during the evolution of the acute phase of stroke. We conducted a multicenter study and enrolled 69 stroke patients. NIHSS was assessed every hour and upper limbs' motor activity was continuously recorded. We calculated MA and AR in the first hour after admission, after a significant clinical change (NIHSS ± 4) or at discharge. In a control group of 17 subjects, we calculated MA and AR normative values. We defined the best model to predict clinical status with multiple linear regression and identified actigraphic cut-off values to discriminate minor from major stroke (NIHSS ≥ 5) and NIHSS 5-9 from NIHSS ≥ 10. The AR cut-off value to discriminate between minor and major stroke (namely NIHSS ≥ 5) is 27% (sensitivity = 83%, specificity = 76% (AUC 0.86 p < 0.001), PPV = 89%, NPV = 42%). However, the combination of AR and MA of the non-paretic arm is the best model to predict NIHSS score (R2: 0.482, F: 54.13), discriminating minor from major stroke (sensitivity = 89%, specificity = 82%, PPV = 92%, NPV = 75%). The AR cut-off value of 53% identifies very severe stroke patients (NIHSS ≥ 10) (sensitivity = 82%, specificity = 74% (AUC 0.86 p < 0.001), PPV = 73%, NPV = 82%). Actigraphic parameters can reliably describe the overall severity of stroke patients with motor symptoms, supporting the addition of a wearable actigraphic system to the multi-parametric monitoring in stroke units

Bayesian estimation of agent-based models

We consider Bayesian inference techniques for agent-based (AB) models, as an alternative to simulated minimum distance (SMD). Three computationally heavy steps are involved: (i) simulating the model, (ii) estimating the likelihood and (iii) sampling from the posterior distribution of the parameters. Computational complexity of AB models implies that efficient techniques have to be used with respect to points (ii) and (iii), possibly involving approximations. We first discuss non-parametric (kernel density) estimation of the likelihood, coupled with Markov chain Monte Carlo sampling schemes. We then turn to parametric approximations of the likelihood, which can be derived by observing the distribution of the simulation outcomes around the statistical equilibria, or by assuming a specific form for the distribution of external deviations in the data. Finally, we introduce Approximate Bayesian Computation techniques for likelihood-free estimation. These allow embedding SMD methods in a Bayesian framework, and are particularly suited when robust estimation is needed. These techniques are first tested in a simple price discovery model with one parameter, and then employed to estimate the behavioural macroeconomic model of De Grauwe (2012), with nine unknown parameters

Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases

Alzheimer's disease (AD) and Parkinson's disease (PD) are two common, progressive neurodegenerative brain disorders. Their diagnosis is very challenging at an early disease stage, if based on clinical symptoms only. Brain imaging techniques such as [18F]-fluoro-deoxyglucose positron emission tomography (FDG-PET) can provide important additional information with respect to changes in the cerebral glucose metabolism. In this study, we use machine learning techniques to perform an automated classification of FDG-PET data. The approach is based on the extraction of features by applying the scaled subprofile model with principal component analysis (SSM/PCA) in order to extract characteristics patterns of glucose metabolism. These features are then used for discriminating healthy controls, PD and AD patients by means of two machine learning frameworks: Generalized Matrix Learning Vector Quantization (GMLVQ) with local and global relevance matrices, and Support Vector Machines (SVMs) with a linear kernel. Datasets from different neuroimaging centers are considered. Results obtained for the individual centers, show that reliable classification is possible. We demonstrate, however, that cross-center classification can be problematic due to potential center-specific characteristics of the available FDG-PET data

The Future of Agent-Based Modeling

In this paper, I elaborate on the role of agent-based (AB) modeling for macroeconomic research. My main tenet is that the full potential of the AB approach has not been realized yet. This potential lies in the modular nature of the models, which is bought by abandoning the straitjacket of rational expectations and embracing an evolutionary perspective. I envisage the foundation of a Modular Macroeconomic Science, where new models with heterogeneous interacting agents, endowed with partial information and limited computational ability, can be created by recombining and extending existing models in a unified computational framework