Combined Evaluation of ST-segment Elevation in lead AVR and ST-segment Depression in other Leads Enhances Prediction of in-hospital Cardiovascular Death in Patients with Non ST-segment Elevation Acute Coronary Syndrome

Objective: To investigate the prognostic significance of ST-segment elevation (STE) in aVR associated with ST-segment depression (STD) in other leads in patients with non-STE acute coronary syndrome (NSTE-ACS). Background: In NSTE-ACS patients, STD has been extensively associated with severe coronary lesions and poor outcomes. The prognostic role of STE in aVR is uncertain. Methods: We enrolled 888 consecutive patients with NSTE-ACS. They were divided into two groups according to the presence or not on admission ECG of aVR STE≥ 1mm and STD (defined as high risk ECG pattern). The primary and secondary endpoints were: in-hospital cardiovascular (CV) death and the rate of culprit left main disease (LMD). Results: Patients with high risk ECG pattern (n=121) disclosed a worse clinical profile compared to patients (n=575) without [median GRACE (Global-Registry-of-Acute-Coronary-Events) risk score =142 vs. 182, respectively]. A total of 75% of patients underwent coronary angiography. The rate of in-hospital CV death was 3.9%. On multivariable analysis patients who had the high risk ECG pattern showed an increased risk of CV death (OR=2.88, 95%CI 1.05-7.88) and culprit LMD (OR=4.67,95%CI 1.86-11.74) compared to patients who had not. The prognostic significance of the high risk ECG pattern was maintained even after adjustment for the GRACE risk score (OR = 2.28, 95%CI:1.06-4.93 and OR = 4.13, 95%CI:2.13-8.01, for primary and secondary endpoint, respectively). Conclusions: STE in aVR associated with STD in other leads predicts in-hospital CV death and culprit LMD. This pattern may add prognostic information in patients with NSTE-ACS on top of recommended scoring system.

Interferometria SAR per lo studio di movimenti e generazione di modelli digitali del terreno in Antartide

Satellite SAR (Synthetic Aperture Radar) interferometry represents a valid technique for digital elevation models (DEM) generation, providing metric accuracy even without ancillary data of good quality. Depending on the situations the interferometric phase could be interpreted both as topography and as a displacement eventually occurred between the two acquisitions. Once that these two components have been separated it is possible to produce a DEM from the first one or a displacement map from the second one. InSAR DEM (Digital Elevation Model) generation in the cryosphere is not a straightforward operation because almost every interferometric pair contains also a displacement component, which, even if small, when interpreted as topography during the phase to height conversion step could introduce huge errors in the final product. Considering a glacier, assuming the linearity of its velocity flux, it is therefore necessary to differentiate at least two pairs in order to isolate the topographic residue only. In case of an ice shelf the displacement component in the interferometric phase is determined not only by the flux of the glacier but also by the different heights of the two tides. As a matter of fact even if the two scenes of the interferometric pair are acquired at the same time of the day only the main terms of the tide disappear in the interferogram, while the other ones, smaller, do not elide themselves completely and so correspond to displacement fringes. Allowing for the availability of tidal gauges (or as an alternative of an accurate tidal model) it is possible to calculate a tidal correction to be applied to the differential interferogram. It is important to be aware that the tidal correction is applicable only knowing the position of the grounding line, which is often a controversial matter. In this thesis it is described the methodology applied for the generation of the DEM of the Drygalski ice tongue in Northern Victoria Land, Antarctica. The displacement has been determined both in an interferometric way and considering the coregistration offsets of the two scenes. A particular attention has been devoted to investigate the importance of the role of some parameters, such as timing annotations and orbits reliability. Results have been validated in a GIS environment by comparison with GPS displacement vectors (displacement map and InSAR DEM) and ICEsat GLAS points (InSAR DEM).

A novel map-matching procedure for low-sampling GPS data with applications to traffic flow analysis

An extensive sample (2%) of private vehicles in Italy are equipped with a GPS device that periodically measures their position and dynamical state for insurance purposes. Having access to this type of data allows to develop theoretical and practical applications of great interest: the real-time reconstruction of traffic state in a certain region, the development of accurate models of vehicle dynamics, the study of the cognitive dynamics of drivers. In order for these applications to be possible, we first need to develop the ability to reconstruct the paths taken by vehicles on the road network from the raw GPS data. In fact, these data are affected by positioning errors and they are often very distanced from each other (~2 Km). For these reasons, the task of path identification is not straightforward. This thesis describes the approach we followed to reliably identify vehicle paths from this kind of low-sampling data. The problem of matching data with roads is solved with a bayesian approach of maximum likelihood. While the identification of the path taken between two consecutive GPS measures is performed with a specifically developed optimal routing algorithm, based on A* algorithm. The procedure was applied on an off-line urban data sample and proved to be robust and accurate. Future developments will extend the procedure to real-time execution and nation-wide coverage.

The cardio-renal axis. Non ST-segment elevation myocardial infarction risk stratification according to renal dysfunction

Background: Chronic kidney disease (CKD) is one of the strongest risk factor for myocardial infarction (MI) and mortality. The aim of this study was to assess the association between renal dysfunction severity, short-term outcomes and the use of in-hospital evidence-based therapies among patients with non–ST-segment elevation myocardial infarction (NSTEMI). Methods: We examined data on 320 patients presenting with NSTEMI to Maggiore’s Emergency Department from 1st Jan 2010 to 31st December 2011. The study patients were classified into two groups according to their baseline glomerular filtration rate (GFR): renal dysfunction (RD) (GFR<60) and non-RD (GFR≥60 ml/min). Patients were then classified into four groups according to their CKD stage (GFR≥60, GFR 59-30, GFR 29-15, GFR <15). Results: Of the 320 patients, 155 (48,4%) had a GFR<60 ml/min at baseline. Compared with patients with a GFR≥60 ml/min, this group was, more likely to be female, to have hypertension, a previous myocardial infarction, stroke or TIA, had higher levels of uric acid and C-reactive protein. They were less likely to receive immediate (first 24 hours) evidence-based therapies. The GFR of RD patients treated appropriately increases on average by 5.5 ml/min/1.73 m2. The length of stay (mean, SD) increased with increasing CKD stage, respectively 5,3 (4,1), 7.0 (6.1), 7.8 (7.0), 9.2 (5.8) (global p <.0001). Females had on average a longer hospitalization than males, regardless of RD. In hospital mortality was higher in RD group (3,25%). Conclusions: The in-hospital mortality not was statically difference among the patients with a GFR value ≥60 ml/min, and patients with a GFR value <60 ml/min. The length of stay increased with increasing CKD stages. Despite patients with RD have more comorbidities then without RD less frequently receive guideline –recommended therapy. The GFR of RD patients treated appropriately improves during hospitalization, but not a level as we expected.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.

Quality assessment of a landslide inventory map and its application to land‐use planning. A case study in the Northern Apennines (Emilia‐Romagna region, Italy)

Landslide hazard and risk are growing as a consequence of climate change and demographic pressure. Land‐use planning represents a powerful tool to manage this socio‐economic problem and build sustainable and landslide resilient communities. Landslide inventory maps are a cornerstone of land‐use planning and, consequently, their quality assessment represents a burning issue. This work aimed to define the quality parameters of a landslide inventory and assess its spatial and temporal accuracy with regard to its possible applications to land‐use planning. In this sense, I proceeded according to a two‐steps approach. An overall assessment of the accuracy of data geographic positioning was performed on four case study sites located in the Italian Northern Apennines. The quantification of the overall spatial and temporal accuracy, instead, focused on the Dorgola Valley (Province of Reggio Emilia). The assessment of spatial accuracy involved a comparison between remotely sensed and field survey data, as well as an innovative fuzzylike analysis of a multi‐temporal landslide inventory map. Conversely, long‐ and short‐term landslide temporal persistence was appraised over a period of 60 years with the aid of 18 remotely sensed image sets. These results were eventually compared with the current Territorial Plan for Provincial Coordination (PTCP) of the Province of Reggio Emilia. The outcome of this work suggested that geomorphologically detected and mapped landslides are a significant approximation of a more complex reality. In order to convey to the end‐users this intrinsic uncertainty, a new form of cartographic representation is needed. In this sense, a fuzzy raster landslide map may be an option. With regard to land‐use planning, landslide inventory maps, if appropriately updated, confirmed to be essential decision‐support tools. This research, however, proved that their spatial and temporal uncertainty discourages any direct use as zoning maps, especially when zoning itself is associated to statutory or advisory regulations.