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Sökning: WFRF:(Ostergaard P. B.)

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  • Shepherd, L., et al. (författare)
  • Infection-related and -unrelated malignancies, HIV and the aging population
  • 2016
  • Ingår i: HIV Medicine. - : Wiley. - 1464-2662 .- 1468-1293. ; 17:8, s. 590-600
  • Tidskriftsartikel (refereegranskat)abstract
    • Objectives: HIV-positive people have increased risk of infection-related malignancies (IRMs) and infection-unrelated malignancies (IURMs). The aim of the study was to determine the impact of aging on future IRM and IURM incidence. Methods: People enrolled in EuroSIDA and followed from the latest of the first visit or 1 January 2001 until the last visit or death were included in the study. Poisson regression was used to investigate the impact of aging on the incidence of IRMs and IURMs, adjusting for demographic, clinical and laboratory confounders. Linear exponential smoothing models forecasted future incidence. Results: A total of 15 648 people contributed 95 033 person-years of follow-up, of whom 610 developed 643 malignancies [IRMs: 388 (60%); IURMs: 255 (40%)]. After adjustment, a higher IRM incidence was associated with a lower CD4 count [adjusted incidence rate ratio (aIRR) CD4 count < 200 cells/μL: 3.77; 95% confidence interval (CI) 2.59, 5.51; compared with ≥ 500 cells/μL], independent of age, while a CD4 count < 200 cells/μL was associated with IURMs in people aged < 50 years only (aIRR: 2.51; 95% CI 1.40–4.54). Smoking was associated with IURMs (aIRR: 1.75; 95% CI 1.23, 2.49) compared with never smokers in people aged ≥ 50 years only, and not with IRMs. The incidences of both IURMs and IRMs increased with older age. It was projected that the incidence of IRMs would decrease by 29% over a 5-year period from 3.1 (95% CI 1.5–5.9) per 1000 person-years in 2011, whereas the IURM incidence would increase by 44% from 4.1 (95% CI 2.2–7.2) per 1000 person-years over the same period. Conclusions: Demographic and HIV-related risk factors for IURMs (aging and smoking) and IRMs (immunodeficiency and ongoing viral replication) differ markedly and the contribution from IURMs relative to IRMs will continue to increase as a result of aging of the HIV-infected population, high smoking and lung cancer prevalence and a low prevalence of untreated HIV infection. These findings suggest the need for targeted preventive measures and evaluation of the cost−benefit of screening for IURMs in HIV-infected populations.
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  • Pelletier, F., et al. (författare)
  • Endocrine and Growth Abnormalities in 4H Leukodystrophy Caused by Variants in POLR3A, POLR3B, and POLR1C
  • 2021
  • Ingår i: Journal of Clinical Endocrinology & Metabolism. - : The Endocrine Society. - 0021-972X .- 1945-7197. ; 106:2
  • Tidskriftsartikel (refereegranskat)abstract
    • Context: 4H or POLR3-related leukodystrophy is an autosomal recessive disorder typically characterized by hypomyelination, hypodontia, and hypogonadotropic hypogonadism, caused by biallelic pathogenic variants in POLR3A, POLR3B, POLR1C, and POLR3K. The endocrine and growth abnormalities associated with this disorder have not been thoroughly investigated to date. Objective: To systematically characterize endocrine abnormalities of patients with 4H leukodystrophy. Design: An international cross-sectional study was performed on 150 patients with genetically confirmed 4H leukodystrophy between 2015 and 2016. Endocrine and growth abnormalities were evaluated, and neurological and other non-neurological features were reviewed. Potential genotype/phenotype associations were also investigated. Setting: This was a multicenter retrospective study using information collected from 3 predominant centers. Patients: A total of 150 patients with 4H leukodystrophy and pathogenic variants in POLR3A, POLR3B, or POLR1C were included. Main Outcome Measures: Variables used to evaluate endocrine and growth abnormalities included pubertal history, hormone levels (estradiol, testosterone, stimulated LH and FSH, stimulated GH, IGF-I, prolactin, ACTH, cortisol, TSH, and T4), and height and head circumference charts. Results: The most common endocrine abnormalities were delayed puberty (57/74; 77% overall, 64% in males, 89% in females) and short stature (57/93; 61%), when evaluated according to physician assessment. Abnormal thyroid function was reported in 22% (13/59) of patients. Conclusions: Our results confirm pubertal abnormalities and short stature are the most common endocrine features seen in 4H leukodystrophy. However, we noted that endocrine abnormalities are typically underinvestigated in this patient population. A prospective study is required to formulate evidence-based recommendations for management of the endocrine manifestations of this disorder.
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  • Georgiadis, S, et al. (författare)
  • CAN SINGLE IMPUTATION TECHNIQUES FOR BASDAI COMPONENTS RELIABLY CALCULATE THE COMPOSITE SCORE IN AXIAL SPONDYLOARTHRITIS PATIENTS?
  • 2022
  • Ingår i: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 81, s. 212-213
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • In axial spondyloarthritis (axSpA), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) is a key patient-reported outcome. However, one or more of its components may be missing when recorded in clinical practice.ObjectivesTo determine whether an individual patient’s BASDAI at a given timepoint can be reliably calculated with different single imputation techniques and to explore the impact of the number of missing components and/or differences between missingness of individual components.MethodsReal-life data from axSpA patients receiving tumour necrosis factor inhibitors (TNFi) from 13 countries in the European Spondyloarthritis (EuroSpA) Research Collaboration Network were utilized [1]. We studied missingness in BASDAI components based on simulations in a complete dataset, where we applied and expanded the approach of Ramiro et al. [2]. After introducing one or more missing components completely at random, BASDAI was calculated from the available components and with three different single imputation techniques: possible middle value (i.e. 50) of the component and mean and median of the available components. Differences between the observed (original) and calculated scores were assessed and correct classification of patients as having BASDAI<40 mm was additionally evaluated. For the setting with one missing component, differences arising between missing one of components 1-4 versus 5-6 were explored. Finally, the performance of imputations in relation to the values of the original score was investigated.ResultsA total of 19,894 axSpA patients with at least one complete BASDAI registration at any timepoint were included. 59,126 complete BASDAI registrations were utilized for the analyses with a mean BASDAI of 38.5 (standard deviation 25.9). Calculating BASDAI from the available components and imputing with mean or median showed similar levels of agreement (Table 1). When allowing one missing component, >90% had a difference of ≤6.9 mm between the original and calculated scores and >95% were correctly classified as BASDAI<40 (Table 1). However, separate analyses of components 1-4 and 5-6 as a function of the BASDAI score suggested that imputing any one of the first four BASDAI components resulted in a level of agreement <90% for specific BASDAI values while imputing one of the stiffness components 5-6 always reached a level of agreement >90% (Figure 1, upper panels). As expected, it was observed that regardless of the BASDAI component set to missing and the imputation technique used, correct classification of patients as BASDAI<40 was less than 95% for values around the cutoff (Figure 1, lower panels).Table 1.Level of agreement between the original and calculated BASDAI and correct classification for BASDAI<40 mmLevel of agreement with Dif≤6.9 mm* (%)Correct classification for BASDAI<40 mm** (%)1 missing componentAvailable93.996.9Value 5073.996.3Mean94.296.8Median93.196.82 missing componentsAvailable83.794.8Value 5040.792.8Mean83.594.8Median82.894.73 missing componentsAvailable71.992.6Value 5028.187.3Mean72.292.6Median69.792.2* The levels of agreement with a difference (Dif) of ≤6.9 mm between the original and calculated scores were based on the half of the smallest detectable change. Agreement of >90% was considered as acceptable. ** Correct classification of >95% was considered as acceptable.Figure 1.Level of agreement between the original and calculated BASDAI and correct classification for BASDAI<40 mm as a function of the original scoreConclusionBASDAI calculation with available components gave similar results to single imputation of missing components with mean or median. Only when missing one of BASDAI components 5 or 6, single imputation techniques can reliably calculate individual BASDAI scores. However, missing any single component value results in misclassification of patients with original BASDAI scores close to 40.References[1]Ørnbjerg et al. (2019). Ann Rheum Dis, 78(11), 1536-1544.[2]Ramiro et al. (2014). Rheumatology, 53(2), 374-376.AcknowledgementsNovartis Pharma AG and IQVIA for supporting the EuroSpA collaboration.Disclosure of InterestsStylianos Georgiadis Grant/research support from: Novartis, Myriam Riek Grant/research support from: Novartis, Christos Polysopoulos Grant/research support from: Novartis, Almut Scherer Grant/research support from: Novartis, Daniela Di Giuseppe: None declared, Gareth T. Jones Speakers bureau: Janssen, Grant/research support from: AbbVie, Pfizer, UCB, Amgen, GSK, Merete Lund Hetland Grant/research support from: Abbvie, Biogen, BMS, Celltrion, Eli Lilly, Janssen Biologics B.V, Lundbeck Fonden, MSD, Medac, Pfizer, Roche, Samsung Biopies, Sandoz, Novartis, Mikkel Østergaard Speakers bureau: Abbvie, BMS, Boehringer-Ingelheim, Celgene, Eli-Lilly, Hospira, Janssen, Merck, Novartis, Novo, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, UCB, Consultant of: Abbvie, BMS, Boehringer-Ingelheim, Celgene, Eli-Lilly, Hospira, Janssen, Merck, Novartis, Novo, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, UCB, Grant/research support from: Abbvie, BMS, Merck, Celgene, Novartis, Simon Horskjær Rasmussen Grant/research support from: Novartis, Johan K Wallman Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Novartis, Bente Glintborg Grant/research support from: Pfizer, Abbvie, BMS, Anne Gitte Loft Speakers bureau: AbbVie, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, UCB, Consultant of: AbbVie, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, UCB, Karel Pavelka Speakers bureau: Pfizer, MSD, BMS, UCB, Amgen, Egis, Roche, AbbVie, Consultant of: Pfizer, MSD, BMS, UCB, Amgen, Egis, Roche, AbbVie, Jakub Zavada Speakers bureau: Abbvie, Elli-Lilly, Sandoz, Novartis, Egis, UCB, Consultant of: Abbvie, Elli-Lilly, Sandoz, Novartis, Egis, UCB, Merih Birlik: None declared, Ayten Yazici Grant/research support from: Roche, Brigitte Michelsen Grant/research support from: Novartis, Eirik kristianslund: None declared, Adrian Ciurea Speakers bureau: AbbVie, Eli Lilly, Merck Sharp & Dohme, Novartis, Pfizer, Consultant of: AbbVie, Eli Lilly, Merck Sharp & Dohme, Novartis, Pfizer, Michael J. Nissen Speakers bureau: AbbVie, Eli Lilly, Janssens, Novartis, Pfizer, Consultant of: AbbVie, Eli Lilly, Janssens, Novartis, Pfizer, Ana Maria Rodrigues Speakers bureau: Abbvie, Amgen, Consultant of: Abbvie, Amgen, Grant/research support from: Novartis, Pfizer, Amgen, Maria Jose Santos Speakers bureau: Abbvie, AstraZeneca, Lilly, Novartis, Pfizer, Gary Macfarlane Grant/research support from: GSK, Anna-Mari Hokkanen Grant/research support from: MSD, Heikki Relas Speakers bureau: Abbvie, Celgene, Pfizer, UCB, Viatris, Consultant of: Abbvie, Celgene, Pfizer, UCB, Viatris, Catalin Codreanu Speakers bureau: AbbVie, Amgen, Boehringer Ingelheim, Ewopharma, Lilly, Novartis, Pfizer, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Ewopharma, Lilly, Novartis, Pfizer, Corina Mogosan: None declared, Ziga Rotar Speakers bureau: Abbvie, Novartis, MSD, Medis, Biogen, Eli Lilly, Pfizer, Sanofi, Lek, Janssen, Consultant of: Abbvie, Novartis, MSD, Medis, Biogen, Eli Lilly, Pfizer, Sanofi, Lek, Janssen, Matija Tomsic Speakers bureau: Abbvie, Amgen, Biogen, Eli Lilly, Janssen, Medis, MSD, Novartis, Pfizer, Sanofi, Sandoz-Lek, Consultant of: Abbvie, Amgen, Biogen, Eli Lilly, Janssen, Medis, MSD, Novartis, Pfizer, Sanofi, Sandoz-Lek, Björn Gudbjornsson Speakers bureau: Amgen, Novartis, Consultant of: Amgen, Novartis, Arni Jon Geirsson: None declared, Pasoon Hellamand Grant/research support from: Novartis, Marleen G.H. van de Sande Speakers bureau: Eli Lilly, Novartis, UCB, Janssen, Abbvie, Consultant of: Eli Lilly, Novartis, UCB, Janssen, Abbvie, Grant/research support from: Eli Lilly, Novartis, UCB, Janssen, Abbvie, Isabel Castrejon: None declared, Manuel Pombo-Suarez Consultant of: Abbvie, MSD, Roche, Bruno Frediani: None declared, Florenzo Iannone Speakers bureau: Abbvie, Amgen, AstraZeneca, BMS, Galapagos, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, UCB, Consultant of: Abbvie, Amgen, AstraZeneca, BMS, Galapagos, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, UCB, Lykke Midtbøll Ørnbjerg Grant/research support from: Novartis
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  • Resultat 1-10 av 29

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