42 research outputs found

    Changes in renal function after nephroureterectomy for upper urinary tract carcinoma: analysis of a large multicenter cohort (Radical Nephroureterectomy Outcomes (RaNeO) Research Consortium)

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    Purpose To investigate prevalence and predictors of renal function variation in a multicenter cohort treated with radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC). Methods Patients from 17 tertiary centers were included. Renal function variation was evaluated at postoperative day (POD)-1, 6 and 12 months. Timepoints differences were Delta 1 = POD-1 eGFR - baseline eGFR; Delta 2 = 6 months eGFR - POD-1 eGFR; Delta 3 = 12 months eGFR - 6 months eGFR. We defined POD-1 acute kidney injury (AKI) as an increase in serum creatinine by >= 0.3 mg/dl or a 1.5 1.9-fold from baseline. Additionally, a cutoff of 60 ml/min in eGFR was considered to define renal function decline at 6 and 12 months. Logistic regression (LR) and linear mixed (LM) models were used to evaluate the association between clinical factors and eGFR decline and their interaction with follow-up. Results A total of 576 were included, of these 409(71.0%) and 403(70.0%) had an eGFR < 60 ml/min at 6 and 12 months, respectively, and 239(41.5%) developed POD-1 AKI. In multivariable LR analysis, age (Odds Ratio, OR 1.05, p < 0.001), male gender (OR 0.44, p = 0.003), POD-1 AKI (OR 2.88, p < 0.001) and preoperative eGFR < 60 ml/min (OR 7.58, p < 0.001) were predictors of renal function decline at 6 months. Age (OR 1.06, p < 0.001), coronary artery disease (OR 2.68, p = 0.007), POD-1 AKI (OR 1.83, p = 0.02), and preoperative eGFR < 60 ml/min (OR 7.80, p < 0.001) were predictors of renal function decline at 12 months. In LM models, age (p = 0.019), hydronephrosis (p < 0.001), POD-1 AKI (p < 0.001) and pT-stage (p = 0.001) influenced renal function variation (ss 9.2 +/- 0.7, p < 0.001) during follow-up. Conclusion Age, preoperative eGFR and POD-1 AKI are independent predictors of 6 and 12 months renal function decline after RNU for UTUC

    A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

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    Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis &lt; 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11&nbsp;years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982

    The Sex-Specific Detrimental Effect of Diabetes and Gender-Related Factors on Pre-admission Medication Adherence Among Patients Hospitalized for Ischemic Heart Disease: Insights From EVA Study

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    Background: Sex and gender-related factors have been under-investigated as relevant determinants of health outcomes across non-communicable chronic diseases. Poor medication adherence results in adverse clinical outcomes and sex differences have been reported among patients at high cardiovascular risk, such as diabetics. The effect of diabetes and gender-related factors on medication adherence among women and men at high risk for ischemic heart disease (IHD) has not yet been fully investigated.Aim: To explore the role of sex, gender-related factors, and diabetes in pre-admission medication adherence among patients hospitalized for IHD.Materials and Methods: Data were obtained from the Endocrine Vascular disease Approach (EVA) (ClinicalTrials.gov Identifier: NCT02737982), a prospective cohort of patients admitted for IHD. We selected patients with baseline information regarding the presence of diabetes, cardiovascular risk factors, and gender-related variables (i.e., gender identity, gender role, gender relations, institutionalized gender). Our primary outcome was the proportion of pre-admission medication adherence defined through a self-reported questionnaire. We performed a sex-stratified analysis of clinical and gender-related factors associated with pre-admission medication adherence.Results: Two-hundred eighty patients admitted for IHD (35% women, mean age 70), were included. Around one-fourth of the patients were low-adherent to therapy before hospitalization, regardless of sex. Low-adherent patients were more likely diabetic (40%) and employed (40%). Sex-stratified analysis showed that low-adherent men were more likely to be employed (58 vs. 33%) and not primary earners (73 vs. 54%), with more masculine traits of personality, as compared with medium-high adherent men. Interestingly, women reporting medication low-adherence were similar for clinical and gender-related factors to those with medium-high adherence, except for diabetes (42 vs. 20%, p = 0.004). In a multivariate adjusted model only employed status was associated with poor medication adherence (OR 0.55, 95%CI 0.31–0.97). However, in the sex-stratified analysis, diabetes was independently associated with medication adherence only in women (OR 0.36; 95%CI 0.13–0.96), whereas a higher masculine BSRI was the only factor associated with medication adherence in men (OR 0.59, 95%CI 0.35–0.99).Conclusion: Pre-admission medication adherence is common in patients hospitalized for IHD, regardless of sex. However, patient-related factors such as diabetes, employment, and personality traits are associated with adherence in a sex-specific manner

    A SAR-Based Measurement Method for Passive-Tag Positioning With a Flying UHF-RFID Reader

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    This paper presents and characterizes a measurement method for positioning of passive tags, by a drone equipped with a UHF-RFID reader. The method is based on a synthetic aperture radar approach and exploits the knowledge of the reader/drone trajectory, which is achieved with a differential Global Navigation Satellite System. Different sources of measurement uncertainty are analyzed by means of numerical simulations and experimental results. The method capabilities are discussed versus the length and shape of the reader trajectory. Finally, the proposed localization method is validated through an experimental analysis carried out with commercial RFID hardware and a microclass unmanned aerial vehicle

    Shielding of High Magnetic Fields

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    This paper revisits the problem of shielding low-frequency high magnetic fields. Such fields are encountered in the domain of electromechanical energy conversion particularly during railgun operation. Using finite-element analysis, we investigate, separately, the mechanism of redirection of the magnetic flux due to induced eddy currents and the magnetic flux shunting due to highly permeable materials. In a first phase, various shapes of highly conductive shields are analyzed in the frequency domain. In a second phase, we study the shunt effect of different shields being made of magnetic materials. In a magnetostatic model afterward, for a shield that combines both highly conductive and magnetically highly permeable materials, the case of a double-exponential-shaped magnetic field is treated in the time domain. Finally, we make some remarks on the concept of shielding effectiveness
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