Institution
University Medical Center Utrecht
Healthcare•Utrecht, Utrecht, Netherlands•
About: University Medical Center Utrecht is a healthcare organization based out in Utrecht, Utrecht, Netherlands. It is known for research contribution in the topics: Population & Medicine. The organization has 7781 authors who have published 9831 publications receiving 425279 citations. The organization is also known as: UMCU & UMC Utrecht.
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Katholieke Universiteit Leuven1, Gdańsk Medical University2, University of Valencia3, Zamorano4, Ghent University5, Charles University in Prague6, University of Glasgow7, University of Naples Federico II8, University Medical Center Utrecht9, Linköping University10, University of Birmingham11, University of Oslo12, Lund University13, Complutense University of Madrid14, University of Erlangen-Nuremberg15, John Radcliffe Hospital16, Tallinn University of Technology17, University of Lausanne18
TL;DR: 2007 Guidelines for the Management of Arterial Hypertension : The Task Force for the management of Arterspertension of the European Society ofhypertension (ESH) and of theEuropean Society of Cardiology (ESC).
Abstract: Because of new evidence on several diagnostic and therapeutic aspects of hypertension, the present guidelines differ in many respects from the previous ones. Some of the most important differences are listed below:
1. Epidemiological data on hypertension and BP control in Europe.
2. Strengthening of the prognostic value of home blood pressure monitoring (HBPM) and of its role for diagnosis and management of hypertension, next to ambulatory blood pressure monitoring (ABPM).
3. Update of the prognostic significance of night-time BP, white-coat hypertension and masked hypertension.
4. Re-emphasis on integration of BP, cardiovascular (CV) risk factors, asymptomatic organ damage (OD) and clinical complications for total CV risk assessment.
5. Update of the prognostic significance of asymptomatic OD, including heart, blood vessels, kidney, eye and brain.
6. Reconsideration of the risk of overweight and target body mass index (BMI) in hypertension.
7. Hypertension in young people.
8. Initiation of antihypertensive treatment. More evidence-based criteria and no drug treatment of high normal BP.
9. Target BP for treatment. More evidence-based criteria and unified target systolic blood pressure (SBP) (<140 mmHg) in both higher and lower CV risk patients.
10. Liberal approach to initial monotherapy, without any all-ranking purpose.
11. Revised schema for priorital two-drug combinations.
12. New therapeutic algorithms for achieving target BP.
13. Extended section on therapeutic strategies in special conditions.
14. Revised recommendations on treatment of hypertension in the elderly.
15. Drug treatment of octogenarians.
16. Special attention to resistant hypertension and new treatment approaches.
17. Increased attention to OD-guided therapy.
18. New approaches to chronic management of hypertensive disease
7,018 citations
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University Hospital of Lausanne1, University of Toronto2, Erasmus University Rotterdam3, University Medical Center Utrecht4, Queen's University5, European Organisation for Research and Treatment of Cancer6, University of Western Ontario7, Medical University of Vienna8, French Institute of Health and Medical Research9, National Institutes of Health10, University of Tübingen11, University of Calgary12
TL;DR: Benefits of adjuvant temozolomide with radiotherapy lasted throughout 5 years of follow-up, and a benefit of combined therapy was recorded in all clinical prognostic subgroups, including patients aged 60-70 years.
Abstract: BACKGROUND: In 2004, a randomised phase III trial by the European Organisation for Research and Treatment of Cancer (EORTC) and National Cancer Institute of Canada Clinical Trials Group (NCIC) reported improved median and 2-year survival for patients with glioblastoma treated with concomitant and adjuvant temozolomide and radiotherapy. We report the final results with a median follow-up of more than 5 years. METHODS: Adult patients with newly diagnosed glioblastoma were randomly assigned to receive either standard radiotherapy or identical radiotherapy with concomitant temozolomide followed by up to six cycles of adjuvant temozolomide. The methylation status of the methyl-guanine methyl transferase gene, MGMT, was determined retrospectively from the tumour tissue of 206 patients. The primary endpoint was overall survival. Analyses were by intention to treat. This trial is registered with Clinicaltrials.gov, number NCT00006353. FINDINGS: Between Aug 17, 2000, and March 22, 2002, 573 patients were assigned to treatment. 278 (97%) of 286 patients in the radiotherapy alone group and 254 (89%) of 287 in the combined-treatment group died during 5 years of follow-up. Overall survival was 27.2% (95% CI 22.2-32.5) at 2 years, 16.0% (12.0-20.6) at 3 years, 12.1% (8.5-16.4) at 4 years, and 9.8% (6.4-14.0) at 5 years with temozolomide, versus 10.9% (7.6-14.8), 4.4% (2.4-7.2), 3.0% (1.4-5.7), and 1.9% (0.6-4.4) with radiotherapy alone (hazard ratio 0.6, 95% CI 0.5-0.7; p<0.0001). A benefit of combined therapy was recorded in all clinical prognostic subgroups, including patients aged 60-70 years. Methylation of the MGMT promoter was the strongest predictor for outcome and benefit from temozolomide chemotherapy. INTERPRETATION: Benefits of adjuvant temozolomide with radiotherapy lasted throughout 5 years of follow-up. A few patients in favourable prognostic categories survive longer than 5 years. MGMT methylation status identifies patients most likely to benefit from the addition of temozolomide. FUNDING: EORTC, NCIC, Nelia and Amadeo Barletta Foundation, Schering-Plough.
6,161 citations
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TL;DR: A technology that can be used to study infected, inflammatory, or neoplastic tissues from the human gastrointestinal tract is developed that might have applications in regenerative biology through ex vivo expansion of the intestinal epithelia.
2,726 citations
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TL;DR: Type 2 diabetes mellitus has a strong genetic component, but only a handful of genes have been identified so far: genes for calpain 10, potassium inward-rectifier 6.2, peroxisome proliferator-activated receptor gamma, insulin receptor substrate-1, and others.
2,363 citations
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Katholieke Universiteit Leuven1, Public Health Research Institute2, Leiden University3, John Radcliffe Hospital4, University of Oxford5, Keele University6, Medical University of Vienna7, University Medical Center Utrecht8, University College Cork9, University of Pennsylvania10, University of Cologne11, Manchester Academic Health Science Centre12, University of Aberdeen13, RMIT University14, University of Manchester15, University of Amsterdam16, Imperial College London17, University of Ioannina18, Maastricht University Medical Centre19, Humboldt University of Berlin20
TL;DR: Proposed models for covid-19 are poorly reported, at high risk of bias, and their reported performance is probably optimistic, according to a review of published and preprint reports.
Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
2,183 citations
Authors
Showing all 7882 results
Name | H-index | Papers | Citations |
---|---|---|---|
Hans Clevers | 199 | 793 | 169673 |
Diederick E. Grobbee | 155 | 1051 | 122748 |
David Price | 138 | 1687 | 93535 |
Cisca Wijmenga | 136 | 668 | 86572 |
Patrick M.M. Bossuyt | 136 | 832 | 85153 |
René S. Kahn | 134 | 945 | 76611 |
John F. Thompson | 132 | 1420 | 95894 |
Chris J.L.M. Meijer | 128 | 733 | 78705 |
Bert Brunekreef | 124 | 806 | 81938 |
Lambertus A. Kiemeney | 123 | 719 | 73631 |
Jan K. Buitelaar | 123 | 1004 | 61880 |
Frits R. Rosendaal | 122 | 763 | 69043 |
Petra H.M. Peeters | 119 | 720 | 63681 |
Jim van Os | 118 | 852 | 87111 |
Geoffrey A. Donnan | 115 | 758 | 58971 |