1,720,963 research outputs found
Supplementary data for: Development of a new model of insulin sensitivity in patients with type 2 diabetes and association with mortality
No full textComparing medication persistence among patients with type 2 diabetes using sodium-glucose cotransporter 2 inhibitors or glucagon-like peptide-1 receptor agonists in real-world setting
Full text linkAim: To assess and compare the persistence with drug therapy between patients treated with glucagon-like peptide-1 receptor agonists (GLP1-RA) and sodium-glucose cotransporter-2 inhibitors (SGLT2-I) therapy. Methods: The 126,493 residents of the Lombardy Region (Italy) aged > 40 years newly treated with metformin during 2007-2015 were followed until 2017 to identify those who started therapy with GLP1-RA or SGLT2-I. To make GLP1-RA and SGLT2-I users more comparable, a 1:1 matched cohort design was adopted. Matching variables were sex, age, and adherence to the first-line therapy with metformin. Log-binomial regression models were fitted to estimate the propensity to 1-year treatment persistence in relation to the therapeutic strategy. Results: The final matched cohort was composed by 1,276 GLP1-RASGLT2-I pairs. About 24% and 29% of cohort members respectively on GLP1-RA and SGLT2-I discontinued the drug treatment. Compared with patients starting SGLT2-I, those on GLP1-RA had 15% (95% confidence interval, 3-25%) lower risk of discontinuation of the treatments of interest and 45% (28-57%) lower risk of discontinuing any antidiabetic drug therapy. Persistence was better among GLP1-RA users who received a once-weekly administration. Conclusions: In a real-life setting, patients who were prescribed a GLP1-RA exhibited more frequently better persistence to treatment than those prescribed a SGLT2-I therapy. CO 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort
No full textBackground
The progress of digital transformation in clinical practice opens the door to transforming the current clinical line for liver disease diagnosis from a late-stage diagnosis approach to an early-stage based one. Early diagnosis of liver fibrosis can prevent the progression of the disease and decrease liver-related morbidity and mortality. We developed here a machine learning (ML) algorithm containing standard parameters that can identify liver fibrosis in the general US population.
Materials and methods
Starting from a public database (National Health and Nutrition Examination Survey, NHANES), representative of the American population with 7265 eligible subjects (control population n = 6828, with Fibroscan values E < 9.7 KPa; target population n = 437 with Fibroscan values E ≥ 9.7 KPa), we set up an SVM algorithm able to discriminate for individuals with liver fibrosis among the general US population. The algorithm set up involved the removal of missing data and a sampling optimization step to managing the data imbalance (only ∼ 5 % of the dataset is the target population).
Results
For the feature selection, we performed an unbiased analysis, starting from 33 clinical, anthropometric, and biochemical parameters regardless of their previous application as biomarkers of liver diseases. Through PCA analysis, we identified the 26 more significant features and then used them to set up a sampling method on an SVM algorithm. The best sampling technique to manage the data imbalance was found to be oversampling through the SMOTE-NC. For final model validation, we utilized a subset of 300 individuals (150 with liver fibrosis and 150 controls), subtracted from the main dataset prior to sampling. Performances were evaluated on multiple independent runs.
Conclusions
We provide proof of concept of an ML clinical decision support tool for liver fibrosis diagnosis in the general US population. Though the presented ML model represents at this stage only a prototype, in the future, it might be implemented and potentially applied to program broad screenings for liver fibrosis
Hepatitis C virus infection and diabetes: a complex bidirectional relationship
No full text: Chronic hepatitis C (CHC) and diabetes represent two severe chronic conditions responsible for a considerable number of deaths worldwide. They have a complex, bidirectional relationship. On the one hand, several cohort studies have shown that chronic HCV infection increases both the risk of developing diabetes in non-diabetic subjects (by inducing insulin resistance and promoting β-cell dysfunction) as well as the risk of developing macro and microvascular complications in patients with known diabetes; on the other hand, diabetes is an independent risk factor for liver-related events among patients with CHC, including a higher incidence of hepatocellular carcinoma, liver-related death and transplantation. Importantly, sustained virological response, which can be obtained in the vast majority of patients with the use of direct antiviral agents, does not only lead to a lower rate of liver-related outcomes, but also to improvements of glycemic control and reduction in the rate of complications among patients with diabetes. The aim of this review is to summarize available clinical evidence on the association among CHC, diabetes and related clinical outcomes. We will also briefly discuss the biological mechanisms underpinning the association between CHC and diabetes, as well as the implications this relationship should have on everyday clinical practice
Prolonged Use of Proton Pump Inhibitors and Risk of Type 2 Diabetes: Results From a Large Population-Based Nested Case-Control Study
Full text linkCONTEXT: It is still debated whether prolonged use of proton pump inhibitors (PPIs) might affect metabolic health. OBJECTIVE: To investigate the relationship between prolonged use of PPIs and the risk of developing diabetes. METHODS: We performed a case-control study nested into a cohort of 777 420 patients newly treated with PPIs between 2010 and 2015 in Lombardy, Italy. A total of 50 535 people diagnosed with diabetes until 2020 were matched with an equal number of controls that were randomly selected from the cohort members according to age, sex, and clinical status. Exposure to treatment with PPIs was assessed in case-control pairs based on time of therapy. A conditional logistic regression model was fitted to estimate the odds ratios and 95% CIs for the exposure-outcome association, after adjusting for several covariates. Sensitivity analyses were performed to evaluate the robustness of our findings. RESULTS: Compared with patients who used PPIs for 2 years, respectively. The results were consistent when analyses were stratified according to age, sex, and clinical profile, with higher odds ratios being found in younger patients and those with worse clinical complexity. Sensitivity analyses revealed that the association was consistent and robust. CONCLUSIONS: Regular and prolonged use of PPIs is associated with a higher risk of diabetes. Physicians should therefore avoid unnecessary prescription of this class of drugs, particularly for long-term use
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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