Prevention of catheter lumen occlusion with rT-PA versus heparin (Pre-CLOT): study protocol of a randomized trial [ISRCTN35253449]

BACKGROUND: Many patients with end-stage renal disease use a central venous catheter for hemodialysis access. A large majority of these catheters malfunction within one year of insertion, with up to two-thirds due to thrombosis. The optimal solution for locking the catheter between hemodialysis sessions, to decrease the risk of thrombosis and catheter malfunction, is unknown. The Prevention of Catheter Lumen Occlusion with rt-PA versus Heparin (PreCLOT) study will determine if use of weekly rt-PA, compared to regular heparin, as a catheter locking solution, will decrease the risk of catheter malfunction. METHODS/DESIGN: The study population will consist of patients requiring chronic hemodialysis thrice weekly who are dialyzed with a newly inserted permanent dual-lumen central venous catheter. Patients randomized to the treatment arm will receive rt-PA 1 mg per lumen once per week, with heparin 5,000 units per ml as a catheter locking solution for the remaining two sessions. Patients randomized to the control arm will receive heparin 5,000 units per ml as a catheter locking solution after each dialysis session. The study treatment period will be six months, with 340 patients to be recruited from 14 sites across Canada. The primary outcome will be catheter malfunction, based on mean blood flow parameters while on hemodialysis, with a secondary outcome of catheter-related bacteremia. A cost-effectiveness analysis will be undertaken to assess the cost of maintaining a catheter using rt-PA as a locking solution, compared to the use of heparin. DISCUSSION: Results from this study will determine if use of weekly rt-PA, compared to heparin, will decrease catheter malfunction, as well as assess the cost-effectiveness of these locking solutions

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation

Renal replacement therapy in Europe : A summary of the 2013 ERA-EDTA Registry Annual Report with a focus on diabetes mellitus

Publisher Copyright: © The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA.Background: This article provides a summary of the 2013 European Renal Association-European Dialysis and Transplant Association (ERA-EDTA) Registry Annual Report (available at http://www.era-edta-reg.org), with a focus on patients with diabetes mellitus (DM) as the cause of end-stage renal disease (ESRD). Methods: In 2015, the ERA-EDTA Registry received data on renal replacement therapy (RRT) for ESRD from 49 national or regional renal registries in 34 countries in Europe and bordering the Mediterranean Sea. Individual patient datawere provided by 31 registries, while 18 registries provided aggregated data. The total population covered by the participating registries comprised 650 million people. Results: In total, 72 933 patients started RRT for ESRD within the countries and regions reporting to the ERA-EDTA Registry, resulting in an overall incidence of 112 per million population (pmp). The overall prevalence on 31 December 2013was 738 pmp (n = 478 990). Patients with DM as the cause of ESRD comprised 24% of the incident RRT patients (26 pmp) and 17% of the prevalent RRT patients (122 pmp).Whencompared with the USA, the incidence of patients starting RRTpmpsecondary toDMin Europe was five times lower and the incidence of RRT due to other causes of ESRD was two times lower. Overall, 19 426 kidney transplants were performed (30 pmp). The 5-year adjusted survival for all RRT patients was 60.9% [95% confidence interval (CI) 60.5-61.3] and 50.6% (95% CI 49.9-51.2) for patients with DM as the cause of ESRD.publishersversionPeer reviewe

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

The epidemiology of renal replacement therapy in two different parts of the worldThe Latin American Dialysis and Transplant Registry versus the European Renal Association-European Dialysis and Transplant Association Registry

Publisher Copyright: © 2018 Pan American Health Organization. All rights reserved.Objective: To compare the epidemiology of renal replacement therapy (RRT) for end-stage renal disease (ESRD) in Latin America and Europe, as well as to study differences in macro-economic indicators, demographic and clinical patient characteristics, mortality rates, and causes of death between these two populations. Methods: We used data from 20 Latin American and 49 European national and subnational renal registries that had provided data to the Latin American Dialysis and Renal Transplant Registry (RLADTR) and the European Renal Association-European Dialysis and Transplant Association (ERA-EDTA) Registry, respectively. The incidence and prevalence of RRT in 2013 were calculated per million population (pmp), overall and by subcategories of age, sex, primary renal disease, and treatment modality. The correlation between gross domestic product and the prevalence of RRT was analyzed using linear regression. Trends in the prevalence of RRT between 2004 and 2013 were assessed using Joinpoint regression analysis. Results: In 2013, the overall incidence at day 91 after the onset of RRT was 181 pmp for Latin American countries and 130 pmp for European countries. The overall prevalence was 660 pmp for Latin America and 782 pmp for Europe. In the Latin American countries, the annual increase in the prevalence averaged 4.0% (95% confdence interval (CI): 2.5%-5.6%) from 2004 to 2013, while the European countries showed an average annual increase of 2.2% (95% CI: 2.0%-2.4%) for the same time period. The crude mortality rate was higher in Latin America than in Europe (112 versus 100 deaths per 1 000 patient-years), and cardiovascular disease was the main cause of death in both of those regions. Conclusions. There are considerable differences between Latin America and Europe in the epidemiology of RRT for ESRD. Further research is needed to explore the reasons for these differences.Peer reviewe