Alessio Pollice

Dose-dependent fetal complications of warfarin in pregnant women with mechanical heart valves

OBJECTIVES The purpose of this study was to assess the incidence of warfarin fetal complications and whether they are dose-dependent. BACKGROUND Gravid patients with mechanical heart valves require long-term anticoagulant therapy. Controversy exists concerning the appropriate treatment of these patients. METHODS Forty-three women on warfarin carrying out 58 pregnancies were studied. For each patient with full-term pregnancy a caesarian section was scheduled for the 38th week during brief warfarin discontinuation. Maternal and fetal complications were evaluated. Fetal complications were divided according to the warfarin dosage < or = 5 mg and > 5 mg necessary to keep an international normalized ratio (INR) of 2.5 to 3.5, and analyzed subsequently. RESULTS A total of 58 pregnancies were observed: 31 healthy babies (30 full term, 1 premature) and 27 fetal complications (22 spontaneous abortions, 2 warfarin embryopathies, 1 stillbirth, 1 ventricular septal defect, 1 growth retardation) were recorded. Two maternal valve thromboses occurred. No fetal or maternal bleeding was observed during caesarian sections or premature vaginal delivery. Patients whose warfarin doses during pregnancy were > 5 mg had 22 fetal complications, whereas those taking a dose < or = 5 mg had only five fetal complications (p = 0.0001). For an increase of the warfarin dose there was a substantially increased probability of fetal complications (p < 0.0001; p < 0.7316). CONCLUSIONS There is a close dependency between warfarin dosage and fetal complications. Patients on warfarin anticoagulation may be delivered by planned caesarian section at the 38th week while briefly interrupting anticoagulation.

Obstruction of Mechanical Mitral Prostheses: Analysis of Pathologic Findings

BACKGROUND The pathologic and echocardiographic findings observed in 87 patients with mitral valve obstruction were reviewed to ascertain the incidence of pannus formation versus that of thrombosis, the relationship between the two, and the time to the occurrence of pannus versus the time to thrombosis. METHODS Pannus morphology (concentric or eccentric), its location on the valve (atrial, ventricular, atrioventricular), and the presence and relationship of associated thrombi (atrial, ventricular, atrioventricular) were analyzed. The times between valve replacement and the occurrence of obstruction were also compared. RESULTS There were 10 caged-ball valves, 65 tilting-disc valves, and 12 bileaflet valves. Seventy-two patients underwent prosthetic replacement, and 15 underwent thrombolysis. Pannus alone was found in 27, pannus and thrombus in 39, and thrombus alone in 21. Primary thrombosis occurred earlier than pannus formation (p = 0.04); this was true for patients with bileaflet valves (p = 0.006) and those with tilting-disc valves (p = 0.04). Pannus was atrial in 19.7% (13/66), ventricular in 21.2% (14/66), and atrioventricular in 59.1% (39/66). Pannus morphology was concentric in 22.7% (15/66) and eccentric in 77.3% (51/66). Atrial secondary thrombi occur more often in patients with atrioventricular pannus (p = 0.04). Eight patients had reobstruction; this was caused by pannus formation in 5 and by thrombosis in 3. Five underwent reoperation, and 3 underwent thrombolysis. Reobstruction occurred earlier than the first event. CONCLUSIONS The frequency of pannus formation is much higher than that of thrombus formation, but thrombosis is of earlier onset than pannus formation. Thrombosis is due to the deposition of clots on the prosthesis, and a pannus occurs as the result of an inflammatory reaction developing on both valve surfaces.

Spatiotemporal analysis of the PM10 concentration over the Taranto area

In this paper, an analysis of air quality data is provided for the municipal area of Taranto (southern Italy) characterized by high environmental risks as formally decreed by the Italian government in the 1990s with two administrative measures. This is due to the massive presence of industrial sites with elevated environmental impact activities along the NW boundary of the city conurbation. The aforementioned activities have effects on the environment and on public health, as a number of epidemiological researches concerning this area reconfirm. The present study is focused on particulate matter as measured by PM10 concentrations at 13 monitoring stations, equipped with analogous instruments based on the Beta absorption technology, either reporting hourly, two-hourly, or daily measurements. Daily estimates of the PM10 concentration surfaces are obtained in order to identify areas of higher concentration (hot spots), possibly related to specific anthropic activities. Preliminary analysis involved addressing several data problems: (1) due to the use of two different validation techniques, a calibration procedure was devised to allow for data comparability; (2) imputation techniques were considered to cope with the large number of missing data, due to both different working periods and occasional malfunctions of PM10 sensors; and (3) reliable weather covariates (wind speed and direction, pressure, temperature, etc.) were obtained and considered within the analysis. Spatiotemporal modelling was addressed by a Bayesian kriging-based model proposed by Le and Zidek (2006) characterized by the use of time varying covariates and a semiparametric covariance structure. Advantages and disadvantages of the model are highlighted and assessed in terms of fit and performance. Estimated daily PM10 concentration surfaces are suitable for the interpretation of time trends and for identifying concentration peaks within the urban area.

Using an Automatic Resistivity Profiler Soil Sensor On-The-Go in Precision Viticulture

Spatial information on vineyard soil properties can be useful in precision viticulture. In this paper a combination of high resolution soil spatial information of soil electrical resistivity (ER) and ancillary topographic attributes, such as elevation and slope, were integrated to assess the spatial variability patterns of vegetative growth and yield of a commercial vineyard (Vitis vinifera L. cv. Tempranillo) located in the wine-producing region of La Rioja, Spain. High resolution continuous geoelectrical mapping was accomplished by an Automatic Resistivity Profiler (ARP) on-the-go sensor with an on-board GPS system; rolling electrodes enabled ER to be measured for a depth of investigation approximately up to 0.5, 1 and 2 m. Regression analysis and cluster analysis algorithm were used to jointly process soil resistivity data, landscape attributes and grapevine variables. ER showed a structured variability that matched well with trunk circumference spatial pattern and yield. Based on resistivity and a simple terrain attribute uniform management units were delineated. Once a spatial relationship to target variables is found, the integration of point measurement with continuous soil resistivity mapping is a useful technique to identify within-plots areas of vineyard with similar status.

A Generalization of the Polychoric Correlation Coefficient

The polychoric correlation coefficient is a measure of association between two ordinal variables. It is based on the assumption that two latent bivariate normally distributed random variables generate couples of ordinal scores. Categories of the two ordinal variables correspond to intervals of the corresponding continuous variables. Thus, measuring the association between ordinal variables means estimating the product moment correlation between the underlying normal variables (Olsonn. 1979). When the hypothesis of latent bivariate normality is empirically or theoretically implausible, other distributional assumptions can be made. In this paper a new and more flexible polychoric correlation coefficient is proposed assuming that the underlying variables are skewnormally distributed (Roscino. 2005). The skew normal (Azzalini and Dalla Valle. 1996) is a family of distributions which includes the normal distribution as a special case, but with an extra parameter to regulate the skewness. As for the original polychoric correlation coefficient, the new coefficient was estimated by the maximization of the log-likelihood function with respect to the thresholds of the continuous variables, the skewness and the correlation parameters. The new coefficient was then tested on samples from simulated populations differing in the number of ordinal categories and the distribution of the underlying variables. The results were compared with those of the original polychoric correlation coefficient.

Bayesian analysis of three indices for lagoons ecological status evaluation

The Water Framework Directive (WFD) recognizes benthic macroinvertebrates as a good biological quality element for transitional waters as they are the most exposed to natural variability patterns characteristic of these ecosystems, due to their life cycles and space-use behavior. In this paper we consider the performance of three multimetric indices (namely M-AMBI, BITS and ISS) based on benthic macroinvertebrates abundances, aiming at assessing the ecological status of lagoons and likely to respond differently to different sources of stress and natural variability. In order to investigate the possible contrasting behavior of the three multimetric indices, we propose a Bayesian hierarchical model in which they are jointly modeled as functions of abiotic covariates, external anthropogenic pressure indicators and lagoon effects. The proposed model is applied to data from three lagoons in Apulia and assessed using multiple diagnostic tools. The joint sensitivity of lagoon quality evaluations to available covariates is thus investigated.

Major PM10 source location by a spatial multivariate receptor model

We present a multivariate receptor model for identifying the spatial location of major PM10 pollution sources through the concentrations at multiple monitoring stations. We build on a mixed multiplicative log-normal factor model adjusting the source contributions for meteorological covariates and for temporal correlation and considering source profiles as compositional Gaussian random fields, to account for the variability induced by the spatial distribution of the monitoring sites. Taking a Bayesian approach to estimation, the proposed hierarchical model is implemented and used to analyze average daily PM10 concentration measurements from 13 monitoring sites in Taranto, Italy, for the period April–December 2005. Three major sources of pollution are identified and characterized in terms of their spatial and temporal behavior and in relation to meteorological data.

Bayesian spatiotemporal analysis of zero-inflated biological population density data by a delta-normal spatiotemporal additive model

We evaluate the spatiotemporal changes in the density of a particular species of crustacean known as deep-water rose shrimp, Parapenaeus longirostris, based on biological sample data collected during trawl surveys carried out from 1995 to 2006 as part of the international project MEDITS (MEDiterranean International Trawl Surveys). As is the case for many biological variables, density data are continuous and characterized by unusually large amounts of zeros, accompanied by a skewed distribution of the remaining values. Here we analyze the normalized density data by a Bayesian delta-normal semiparametric additive model including the effects of covariates, using penalized regression with low-rank thin-plate splines for nonlinear spatial and temporal effects. Modeling the zero and nonzero values by two joint processes, as we propose in this work, allows to obtain great flexibility and easily handling of complex likelihood functions, avoiding inaccurate statistical inferences due to misclassification of the high proportion of exact zeros in the model. Bayesian model estimation is obtained by Markov chain Monte Carlo simulations, suitably specifying the complex likelihood function of the zero-inflated density data. The study highlights relevant nonlinear spatial and temporal effects and the influence of the annual Mediterranean oscillations index and of the sea surface temperature on the distribution of the deep-water rose shrimp density.

Source apportionment of polycyclic aromatic hydrocarbons (PAHs) and carcinogenic risk estimate in Taranto, Italy

Objectives In last 2 years the limit value of benzo(a)pyrene (BaP) in ambient air of 1.0 ng/m3 has been exceeded in the urban district of Taranto near the industrial area. In these cases Italian law prescribes the adoption of corrective measures on emission sources. The objectives were: to identify emission sources and quantify relative contribution to the PAHs levels; to estimate health impact associated to PAHs exposure in general population. Methods For the source apportionment multivariate receptor models have been used. Data of monthly average concentration of PAHs measured in several location have been analysed. US-EPA UNMIX software has been used to estimate profiles of unknown sources and uses diagnostic tools to identify the respective number and which chemical species are significant for analysis. To compute the lung cancer risk the WHO unit risk estimate for BaP (8.7 x 10-5 ng/m3) has been adopted. Results Preliminary results show that the models employed identify 3 to 4 emission sources. Profile and temporal trends of each one have been described. Estimated profiles have been compared with measured ones. Based on the average annual BaP level measured (1.3 ng/m3 in 2009, 1.8 in 2010), 2 excess cancer cases are estimated to result from a life-time exposure based on 2009 data, and 3 cases are expected according to 2010 values. Conclusions Among different emissive sources, the analysis identifies theoretical sources whose profiles, compared with observed data, allow to identify dominant contributions to PAHs pollution and to design corrective actions to reduce environmental and health impact.

A Spatial Clustering Hierarchical Model for Disease Mapping

In this paper a finite mixture model with a specific weights for each observation is introduced. The logistic transformation of these weights is modelled through a markovian field, with space autocorrelations of Gaussian type. This specification is particularly useful for desease mapping issues: some implementation difficulties are shortly discussed, together with the problem of the choice of the mixture’s components number.