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The field of additive manufacturing, particularly 3D printing, has ushered in a significant transformation in the realm of joint arthritis treatment through prosthetic surgery. This innovative technology allows for the creation of bespoke prosthetic devices that are tailored to meet the specific needs of individual patients. These devices are constructed using high-performance materials, including titanium and cobalt-chrome alloys. Nevertheless, the routine physical activities of patients, such as walking, sitting, and running, can induce wear and tear on the materials comprising these prosthetic devices, subsequently diminishing their functionality and durability. In response to this challenge, this research has endeavored to leverage novel techniques. The primary focus of this study lies in the development of an algorithm designed to optimize hip replacement procedures via the mechanical design of the prosthesis. This optimization process exploits the capabilities of machine learning algorithms, multi-body dynamics, and finite element method (FEM) simulations. The paramount innovation in this methodology is the capacity to design a prosthetic system that intricately adapts to the distinctive characteristics of each patient (weight, height, gait cycle). The primary objective of this research is to enhance the performance and longevity of prosthetic devices by improving their fatigue strength. The evaluation of load distribution on the prosthetic device, facilitated by FEM simulations, anticipates a substantial augmentation in the useful life of the prosthetic system. This research holds promise as a notable advancement in prosthetic technology, offering a more efficacious treatment option for patients suffering from joint arthritis. The aim of this research is to make meaningful contributions to the enhancement of patient quality of life and the long-term performance of prosthetic devices. [ABSTRACT FROM AUTHOR]

Background The proportion of reported causes of death (CoDs) that are not underlying causes can be relevant even in high-income countries and seriously affect health planning. The Global Burden of Disease (GBD) study identifies these 'garbage codes' (GCs) and redistributes them to underlying causes using evidence-based algorithms. Planners relying on vital registration data will find discrepancies with GBD estimates. We analyse these discrepancies, through the analysis of GCs and their redistribution. Methods We explored the case of Italy, at national and regional level, and compared it to nine other Western European countries with similar population sizes. We analysed differences between official data and GBD 2019 estimates, for the period 1990–2017 for which we had vital registration data for most select countries. Results In Italy, in 2017, 33 000 deaths were attributed to unspecified type of stroke and 15 000 to unspecified type of diabetes, these making a fourth of the overall garbage. Significant heterogeneity exists on the overall proportion of GCs, type (unspecified or impossible underlying causes), and size of specific GCs among regions in Italy, and among the select countries. We found no pattern between level of garbage and relevance of specific GCs. Even locations performing below average show interesting lower levels for certain GCs if compared to better performing countries. Conclusions This systematic analysis suggests the heterogeneity in GC levels and causes, paired with a more detailed analysis of local practices, strengths and weaknesses, could be a positive element in a strategy for the reduction of GCs in Italy. [ABSTRACT FROM AUTHOR]

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]

Background: Dependency in older ages is increasing. Many older persons receive care while living in the community. We aimed to identify the predictive value of four clinical measurements to predict home care intensity in older patients following discharge from hospital to home care over 90 days. Methods: We included 425 inpatients from the "Frailty Department–Local Palliative Care Network" of the local social health authority (ASST) Lecco, Italy (mean age 75.4 years, SD 14.5; female 75.5%). Changes in Health, End-stage disease, and Signs and Symptoms, light version (CHESS-Lite), activities of daily living (ADL), frailty, and the Service Urgency Algorithm. Receiver operative curves were used to calculate the area under the curve (AUC) for predicting Home Care Intensity coefficient (ratio of the number of days when any home care was provided 90 days post-discharge). The interRAI Contact Assessment Instrument was used to calculate these measures. Results: Analysis was stratified using six different home care intensity score cut-offs. CHESS-Lite had a higher AUC for predicting home care intensity at all cut-off levels but was best for predicting the highest level of home care intensity (≥ 0.8) where the AUC was 0.71 (0.64–0.79). The frailty index also had an acceptable AUC. ADL had the lowest AUC. Conclusions: Health instability measured with CHESS-Lite has a high predictive value for identifying home care intensity in geriatric patients after discharge from hospital to home, especially in persons with higher home care intensity scores. Geriatric patients with high health instability should be focused on at discharge to prioritize assessment and initiate timely services for home care support. [ABSTRACT FROM AUTHOR]

Urban mapping techniques using high spectral and spatial resolution (HSSR) data were investigated. To this aim, this paper proposes a novel mean shift (MS)-based multiscale method, and different spatial approaches are compared, including differential morphological profiles (DMPs), pixel shape index (PSI), the fractal net evolution approach (FNEA), and the proposed MS method. These spatial features were computed based on a dimensionally reduced representation that was obtained using the non-negative matrix factorization (NMF) transform. The support vector machine (SVM) was then used for classification. These algorithms were evaluated using two HSSR datasets that were obtained by using the Reflective Optics System Imaging Spectrometer (ROSIS) sensor over the urban area of Pavia, northern Italy. The results show that the spatial approaches can effectively complement the spectral features for urban mapping, and the proposed MS-based multiscale algorithm can give comparable or even better results than the FNEA, DMPs and other traditional algorithms. [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]

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]