| 1. |
- Aad, G, et al.
(författare)
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- 2015
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swepub:Mat__t
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| 2. |
- Aaltonen, T., et al.
(författare)
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Combination of Tevatron Searches for the Standard Model Higgs Boson in the W+W- Decay Mode
- 2010
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Ingår i: Physical Review Letters. - 0031-9007 .- 1079-7114. ; 104:6, s. 061802-
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Tidskriftsartikel (refereegranskat)abstract
- We combine searches by the CDF and D0 Collaborations for a Higgs boson decaying to W+W-. The data correspond to an integrated total luminosity of 4.8 (CDF) and 5.4 (D0) fb(-1) of p (p) over bar collisions at root s = 1.96 TeV at the Fermilab Tevatron collider. No excess is observed above background expectation, and resulting limits on Higgs boson production exclude a standard model Higgs boson in the mass range 162-166 GeV at the 95% C.L.
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| 3. |
- Ruilope, LM, et al.
(författare)
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Design and Baseline Characteristics of the Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease Trial
- 2019
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Ingår i: American journal of nephrology. - : S. Karger AG. - 1421-9670 .- 0250-8095. ; 50:5, s. 345-356
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Tidskriftsartikel (refereegranskat)abstract
- <b><i>Background:</i></b> Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. <b><i>Patients and</i></b> <b><i>Methods:</i></b> The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate ≥25 mL/min/1.73 m<sup>2</sup> and albuminuria (urinary albumin-to-creatinine ratio ≥30 to ≤5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level α = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. <b><i>Conclusions:</i></b> FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049.
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| 4. |
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| 5. |
- Adler, Eric D., et al.
(författare)
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Improving risk prediction in heart failure using machine learning
- 2020
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Ingår i: European Journal of Heart Failure. - : Wiley. - 1388-9842 .- 1879-0844. ; 22:1, s. 139-147
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Tidskriftsartikel (refereegranskat)abstract
- Background: Predicting mortality is important in patients with heart failure (HF). However, current strategies for predicting risk are only modestly successful, likely because they are derived from statistical analysis methods that fail to capture prognostic information in large data sets containing multi-dimensional interactions. Methods and results: We used a machine learning algorithm to capture correlations between patient characteristics and mortality. A model was built by training a boosted decision tree algorithm to relate a subset of the patient data with a very high or very low mortality risk in a cohort of 5822 hospitalized and ambulatory patients with HF. From this model we derived a risk score that accurately discriminated between low and high-risk of death by identifying eight variables (diastolic blood pressure, creatinine, blood urea nitrogen, haemoglobin, white blood cell count, platelets, albumin, and red blood cell distribution width). This risk score had an area under the curve (AUC) of 0.88 and was predictive across the full spectrum of risk. External validation in two separate HF populations gave AUCs of 0.84 and 0.81, which were superior to those obtained with two available risk scores in these same populations. Conclusions: Using machine learning and readily available variables, we generated and validated a mortality risk score in patients with HF that was more accurate than other risk scores to which it was compared. These results support the use of this machine learning approach for the evaluation of patients with HF and in other settings where predicting risk has been challenging.
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