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Objective: To validate the performance of a first-trimester simple risk score based on the ASPRE trial algorithm for pre-eclampsia.Design: Multicentre retrospective cohort analysis.Setting: Four Italian hospitals.Population: Unselected nulliparous women at 11-13 weeks of gestation from January 2014 through to January 2018.Methods: Model performance was evaluated based on discrimination and calibration.Main Outcome Measures: Delivery before 37 weeks of gestation with a diagnosis of pre-eclampsia.Results: Based on 73 preterm pre-eclampsia cases and 7546 controls (including 101 cases of late pre-eclampsia), the area under the receiver operating characteristics curve was 0.659 (95% CI 0.579-0.726). The sensitivity was 32.9% (95% CI 22.1-43.7) at a false-positive rate of 8.8%. The positive likelihood ratio was 3.74 (95% CI 2.67-5.23), the positive predictive value was 3.49% (95% CI 2.12-4.86%) and the negative predictive value was 99.3% (95% CI 99.1-99.5%). The sensitivity and positive likelihood ratio were approximately 40% lower than in the original study. The calibration analysis showed a good agreement between observed and expected risks (P = 0.037). Comparison with the Fetal Medicine Foundation (FMF) algorithm yielded a difference in the area under the curve of 0.084 (P = 0.007).Conclusions: In our Italian population, the simple risk score had a lower performance than expected for the prediction of preterm pre-eclampsia in nulliparous women. The FMF algorithm applied to the same data set resulted in a better prediction.Tweetable Abstract: Simple risk score predicts preterm pre-eclampsia in Italy. [ABSTRACT FROM AUTHOR]

Objectives: Biomedical applications, such as analysis and management of mass spectrometry proteomics experiments, involve heterogeneous platforms and knowledge, massive data sets, and complex algorithms. Main requirements of such applications are semantic modeling of the experiments and data analysis, as well as high performance computational platforms. In this paper we propose a software platform allowing to model and execute biomedical applications on the Grid.Methods: Computational Grids offer the required computational power, whereas ontologies and workflow help to face the heterogeneity of biomedical applications. In this paper we propose the use of domain ontologies and workflow techniques for modeling biomedical applications, whereas Grid middleware is responsible for high performance execution. As a case study, the modeling of a proteomics experiment is discussed.Results: The main result is the design and first use of PROTEUS, a Grid-based problem-solving environment for biomedical and bioinformatics applications.Conclusion: To manage the complexity of biomedical experiments, ontologies help to model applications and to identify appropriate data and algorithms, workflow techniques allow to combine the elements of such applications in a systematic way. Finally, translation of workflow into execution plans allows the exploitation of the computational power of Grids. Along this direction, in this paper we present PROTEUS discussing a real case study in the proteomics domain. [ABSTRACT FROM AUTHOR]

Aim: The aim of this multicentre, observational, transversal study was to evaluate pacemaker (PM) choice and setting in a large number of patients, in order to understand their relationship with the patients' clinical characteristics.Methods and Results: The study enrolled a total of 1858 patients (71 ± 14 years, 54% male), consecutively evaluated during scheduled PM follow-up visits in 7 Italian cardiac arrhythmia centres. To evaluate the appropriateness of PM choice in relation to the patients' clinical characteristics, we analysed their rhythm disorders at the time of device implantation and the characteristics of the devices implanted. To evaluate the appropriateness of device setting, current rhythm disorders and device setting at the time of enrolment were analysed. In the overall study population, 64.3% of the patients received a PM with all of the features required for their rhythm disorder [80.8% in persistent atrioventricular (AV) block, 76.5% in atrial fibrillation needing pacing, 71.0% in sinus node disease, 58.7% in non-persistent atrioventricular block (AVB), 52.7% in neuro-mediated syncope]. The most frequent cause of inappropriate PM choice was the lack of an algorithm to promote intrinsic AV conduction in non-persistent AVB patients (38.1%). In 76.2% of the patients with an appropriate PM (n = 1301), the PM was optimally set for their rhythm disorder.Conclusions: In the present 'real-world' registry, a large number of patients (35.7%) did not receive an optimal PM for their rhythm disorders. Moreover, one-fourth of appropriate PMs were not programmed according to the patients' clinical characteristics. [ABSTRACT FROM AUTHOR]

Background: In health applications, and elsewhere, 3D data sets are increasingly accessed through the Internet. To reduce the transfer time while maintaining an unaltered 3D model, adequate compression and decompression techniques are needed. Recently, Grid technologies have been integrated with Web Services technologies to provide a framework for interoperable application-to-application interaction.Objectives: The paper describes an implementation of the Edgebreaker compression technique exploiting web services technology and presents a novel approach for using such services in a Grid Portal. The Grid portal, developed at the CACT/ISUFI of the University of Lecce, allows the processing and delivery of biomedical images (CT--computerized tomography--and MRI--magnetic resonance images) in a distributed environment, using the power and security of computational Grids.Methods: The Edgebreaker Compression Web Service has been deployed on a Grid portal and allows compressing and decompressing 3D data sets using the Globus toolkit GSI (Globus Security Infrastructure) protocol. Moreover, the classical algorithm has been modified extending the compression to files containing more than one object.Results and Conclusions: An implementation of the Edgebreaker compression technique and related experimental results are presented. A novel approach for using the compression web service in a Grid portal allowing storing and preprocessing of huge 3D data sets, and subsequent efficient transmission of results for remote visualization is also described. [ABSTRACT FROM AUTHOR]

Background: Healthcare utilization data are increasingly used for chronic disease surveillance. Nevertheless, no standard criteria for estimating prevalence of high-impact diseases, such as chronic obstructive pulmonary disease (COPD) and asthma, are available. In this study an algorithm for recognizing COPD/asthma cases from HCU data is developed and implemented in the HCU databases of the Italian Lombardy Region (about 10 million residents). The impact of diagnostic misclassification for reliably estimating prevalence was also assessed.Methods: Disease-specificdrug codes, hospital discharges together with co-payment exemptions when available, and a combination of them according with patient's age, were used to create the proposed algorithm. Identified cases were considered for prevalence estimation. An external validation study was also performed in order to evaluate systematic uncertainty of prevalence estimates.Results: Raw prevalence of COPD and asthma in 2010 was 3.6 and 3.3% respectively. According to external validation, sensitivity values were 53% for COPD and 39% for asthma. Adjusted prevalence estimates were respectively 6.8 and 8.5% for COPD (among person aged 40 years or older) and asthma (among person aged 40 years or younger).Conclusions: COPD and asthma prevalence may be estimated from HCU data, albeit with high systematic uncertainty. Validation is recommended in this setting. [ABSTRACT FROM AUTHOR]

Objective. The aim of this study was to develop and validate an algorithm to assist the attribution of neuropsychiatric (NP) events to underlying disease in SLE patients. Methods. Phase 1 identified and categorized candidate items to be included in the algorithm for the attribution of an NP event to SLE and their relative weights through a literature-informed consensus-driven process. Using a retrospective training cohort of SLE, phase 2 validated items selected in phase 1 and refined weights through a data-driven process, fitting items as independent variables and expert evaluation (clinical judgement) as reference standard in logistic models. Phase 3 consisted of a validation process using an external multicentre retrospective SLE cohort. Results. Phase 1 identified four different items: timing of the NP event, type of event, confounding factors and favouring factors. The training and validating cohorts included 228 and 221 patients, respectively. Each patient experienced at least one NP event characterized using the ACR case definition. In these samples, items selected in phase 1 showed good performance in discriminating patients with NPSLE: the area under the receiver operating characteristic curve using dichotomous outcomes was 0.87 in the training set and 0.82 in the validating set. Relevant cut-offs of the validated score identify events with a positive predictive value of 100% (95% CI 93.2, 100) and 86.3% (95% CI 76.2, 93.2) in the training and validating cohorts, respectively. Conclusion. A new algorithm based on a probability score was developed and validated to determine the relationship between NP events and SLE. [ABSTRACT FROM AUTHOR]

Most patients with advanced or metastatic cancer experience pain and despite several guidelines, undertreatment is well documented. A multicenter, open-label, prospective, non-randomised study was launched in Italy in 2006 to evaluate the epidemiology, patterns and quality of pain care of cancer patients. To assess the adequacy of analgesic care, we used a standardised measure, the pain management index (PMI), that compares the most potent analgesic prescribed for a patient with the reported level of the worst pain of that patient together with a selected list of clinical indicators. A total of 110 centres recruited 1801 valid cases. 61% of cases were received a WHO-level III opioid; 25.3% were classified as potentially undertreated, with wide variation (9.8-55.3%) according to the variables describing patients, centres and pattern of care. After adjustment with a multivariable logistic regression model, type of recruiting centre, receiving adjuvant therapy or not and type of patient recruited (new or already on follow-up) had a significant association with undertreatment. Non-compliance with the predefined set of clinical indicators was generally high, ranging from 41 to 76%. Despite intrinsic limitations of the PMI that may be considered as an indicator of the poor quality of cancer pain care, results suggest that the recourse to WHO third-level drugs still seems delayed in a substantial percentage of patients. This delay is probably related to several factors affecting practice in participating centres and suggests that the quality of cancer pain management in Italy deserves specific attention and interventions aimed at improving patients' outcomes. [ABSTRACT FROM AUTHOR]