Marcello Chiodi

A prospective, randomized study of empirical antifungal therapy for the treatment of chemotherapy-induced febrile neutropenia in children

Given that the rationale for empirical antifungal therapy in neutropenic children is limited and based on adult patient data, we performed a prospective, randomized, controlled trial that evaluated 110 neutropenic children with persistent fever. Those at high risk for invasive fungal infections (IFI) received caspofungin (Arm C) or liposomal amphotericinB (Arm B); those with a lower risk were randomized to receive Arm B, C, or no antifungal treatment (Arm A). Complete response to empirical antifungal therapy was achieved in 90/104 patients (86·5%): 48/56 at high risk (85·7%) [88·0% in Arm B; 83·9% in Arm C (P = 0·72)], and 42/48 at low risk (87·5%) [87·5% in control Arm A, 80·0% Arm B, 94·1% Arm C; (P = 0·41)]. None of the variables tested by multiple logistic regression analysis showed a significant effect on the probability to achieve complete response. IFI was diagnosed in nine patients (8·2%, 95% confidence interval, 3·8–15·0). This randomized controlled study showed that empirical antifungal therapy was of no advantage in terms of survival without fever and IFI in patients aged <18 years and defined with low risk of IFI. Higher risk patients, including those with relapsed cancer, appear to be the target for empirical antifungal therapy during protracted febrile neutropenia.

Simultaneous seismic wave clustering and registration

In this paper we introduce a simple procedure to identify clusters of multivariate waveforms based on a simultaneous assignation and alignment procedure. This approach is aimed at the identification of clusters of earthquakes, assuming that similarities between seismic events with respect to hypocentral parameters and focal mechanism correspond to similarities between waveforms of events. Therefore we define a distance measure between seismic curves in R^dd>=1, in order to interpret and better understand the main features of the generating seismic process.

Erratum to: Alternated estimation in semi-parametric space-time branching-type point processes with application to seismic catalogs

An estimation approach for the semi-parametric intensity function of a class of space-time point processes is introduced. In particular we want to account for the estimation of parametric and nonparametric components simultaneously, applying a forward predictive likelihood to semi-parametric models. For each event, the probability of being a background event or an offspring is therefore estimated.

Nonparametric Clustering of Seismic Events

In this paper we propose a clustering technique, based on the maximization of the likelihood function defined from the generalization of a model for seismic activity (ETAS model, (Ogata (1988))), iteratively changing the partitioning of the events. In this context it is useful to apply models requiring the distinction between independent events (i.e. the background seismicity) and strongly correlated ones. This technique develops nonparametric estimation methods of the point process intensity function. To evaluate the goodness of fit of the model, from which the clustering method is implemented, residuals process analysis is used.

Kernel Intensity for Space-Time Point Processes with Application to Seismological Problems

Dealing with data coming from a space-time inhomogeneous process, there is often the need of semi-parametric estimates of the conditional intensity function; isotropic or anisotropic multivariate kernel estimates can be used, with windows sizes h. The properties of the intensities estimated with this choice of h are not always good for specific fields of application; we could try to choose h in order to have good predictive properties of the estimated intensity function. Since a direct ML approach cannot be followed, we propose an estimation procedure, computationally intensive, based on the subsequent increments of likelihood obtained adding an observation at time. The first results obtained are very encouraging. Some application in statistical seismology is presented.

Clustering and Registration of Multidimensional Functional Data

In order to find similarity between multidimensional curves, we consider the application of a procedure that provides a simultaneous assignation to clusters and alignment of such functions. In particular we look for clusters of multivariate seismic waveforms based on EM-type procedure and functional data analysis tools.

Gamma Kernel Intensity Estimation in Temporal Point Processes

In this article, we propose a nonparametric approach for estimating the intensity function of temporal point processes based on kernel estimators. In particular, we use asymmetric kernel estimators characterized by the gamma distribution, in order to describe features of observed point patterns adequately. Some characteristics of these estimators are analyzed and discussed both through simulated results and applications to real data from different seismic catalogs.

An Algorithm for Earthquakes Clustering Based on Maximum Likelihood

In this paper we propose a clustering technique set up to separate and find out the two main components of seismicity: the background seismicity and the triggered one. We suppose that a seismic catalogue is the realization of a non homogeneous space–time Poisson clustered process, with a different parametrization for the intensity function of the Poisson-type component and of the clustered (triggered) component. The method here proposed assigns each earthquake to the cluster of earthquakes, or to the set of independent events, according to the increment to the likelihood function, computed using the conditional intensity function estimated by maximum likelihood methods and iteratively changing the assignment of the events; after a change of partition, MLE of parameters are estimated again and the process is iterated until there is no more improvement in the likelihood.

Vascular Risk Factors, Vascular Diseases, and Imaging Findings in a Hospital- based Cohort of Mild Cognitive Impairment Types

BACKGROUND Mild Cognitive Impairment (MCI) is a transitional state between normal cognition and dementia. OBJECTIVE The aim of this study is to investigate the role of vascular risk factors, vascular diseases, cerebrovascular disease and brain atrophy in a large hospital-based cohort of MCI types including 471 amnestic MCI (a-MCI), 693 amnestic MCI multiple domain (a-MCImd), 322 single non-memory MCI (snm-MCI), and 202 non amnestic MCI multiple domain (na-MCImd). For comparison, 1,005 neurologically and cognitively healthy subjects were also evaluated. METHOD Several vascular risk factors and vascular diseases were assessed. All participants underwent neurological, neuropsychological and behavioural assessments as well as carotid ultrasonography and standard brain MRI. Multinomial logistic regression models on the MCI cohort with the NCH group and a-MCI type as reference categories were used to assess the effects of the variables evaluated on the estimated probability of one of the four MCI types. RESULTS This study demonstrates that cerebrovascular disease contributes substantially to the risk of non-memory MCI types and a-MCImd type, and that brain atrophy is present in all MCI types and is greater in multiple domain types particularly in the na-MCI type. CONCLUSION Improving detection and control of cerebrovascular disease in aging individuals should be mandatory. Since the incidence of MCI and dementia will be expected to rise because of the progressive life expectancy, a better management of cerebrovascular disease could indeed prevent or delay the onset of MCI, or could delay progression of MCI to dementia.

Space-time forecasting of seismic events in Chile

The aim of this work is to study the seismicity in Chile using the ETAS (epidemic type aftershock sequences) space‐time approach. The proposed ETAS model is estimated using a semi‐parametric technique taking into account the parametric and nonparametric components corresponding to the triggered and background seismicity, respectively. The model is then used to predict the temporal and spatial intensity of events for some areas of Chile where recent large earthquakes (with magnitude greater than 8.0 M) occurred.