A predictor model of treatment resistance in schizophrenia using data from electronic health records

Autor: Giouliana Kadra-Scalzo, Daniela Fonseca de Freitas, Deborah Agbedjro, Emma Francis, Isobel Ridler, Megan Pritchard, Hitesh Shetty, Aviv Segev, Cecilia Casetta, Sophie E. Smart, Anna Morris, Johnny Downs, Søren Rahn Christensen, Nikolaj Bak, Bruce J. Kinon, Daniel Stahl, Richard D. Hayes, James H. MacCabe

Publikováno v: PLoS ONE, Vol 17, Iss 9 (2022)

Objectives To develop a prognostic tool of treatment resistant schizophrenia (TRS) in a large and diverse clinical cohort, with comprehensive coverage of patients using mental health services in four London boroughs. Methods We used the Least Absolut

Externí odkaz: https://doaj.org/article/33cab6cd2e474f839d332b6455850c37

Bone Status in Obese, Non-diabetic, Antipsychotic-Treated Patients, and Effects of the Glucagon-Like Peptide-1 Receptor Agonist Exenatide on Bone Turnover Markers and Bone Mineral Density

Autor: Robert Eriksson, Brian V. Broberg, Pelle L. Ishøy, Nikolaj Bak, Ulrik B. Andersen, Niklas R. Jørgensen, Filip K. Knop, Bjørn H. Ebdrup

Publikováno v: Frontiers in Psychiatry, Vol 9 (2019)

Background: Low bone mineral density (BMD) may constitute an underestimated comorbidity in schizophrenia patients undergoing long-term antipsychotic treatment. Glucagon-like peptide 1 (GLP-1) receptor agonists are antidiabetic drugs, which may also a

Externí odkaz: https://doaj.org/article/96668b960e5b4b0eb02e97e74946f9ca

Using a statistical learning approach to identify sociodemographic and clinical predictors of response to clozapine

Autor: Daniela Fonseca de Freitas, Giouliana Kadra-Scalzo, Deborah Agbedjro, Emma Francis, Isobel Ridler, Megan Pritchard, Hitesh Shetty, Aviv Segev, Cecilia Casetta, Sophie E Smart, Johnny Downs, Søren Rahn Christensen, Nikolaj Bak, Bruce J Kinon, Daniel Stahl, James H MacCabe, Richard D Hayes

Publikováno v: Journal of Psychopharmacology. 36:498-506

Background: A proportion of people with treatment-resistant schizophrenia fail to show improvement on clozapine treatment. Knowledge of the sociodemographic and clinical factors predicting clozapine response may be useful in developing personalised a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fe9615a728d6f7914f142306ef3b3db
https://doi.org/10.1177/02698811221078746

Clinical correlates of early onset antipsychotic treatment resistance

Autor: Daniela Fonseca de Freitas, Deborah Agbedjro, Giouliana Kadra-Scalzo, Emma Francis, Isobel Ridler, Megan Pritchard, Hitesh Shetty, Aviv Segev, Cecilia Casetta, Sophie E. Smart, Anna Morris, Johnny Downs, Søren Rahn Christensen, Nikolaj Bak, Bruce J. Kinon, Daniel Stahl, Richard D. Hayes, James H. MacCabe

Publikováno v: Fonseca de Freitas, D, Agbedjro, D, Kadra-Scalzo, G, Francis, E, Ridler, I, Pritchard, M, Shetty, H, Segev, A, Casetta, C, Smart, S, Morris, A, Downs, J, Christensen, S R, Bak, N, Kinon, B J, Stahl, D, Hayes, R D & Maccabe, J 2022, ' Clinical correlates of early onset antipsychotic treatment resistance ', Journal of Psychopharmacology .

Background: There is evidence of heterogeneity within treatment-resistant schizophrenia (TRS), with some people not responding to antipsychotic treatment from illness onset and others becoming treatment-resistant after an initial response period. The

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d0aebd527f5bc1a6aada72de23235cf
https://kclpure.kcl.ac.uk/portal/en/publications/clinical-correlates-of-early-onset-antipsychotic-treatment-resistance(27e6bfa2-2720-4c92-acbb-96c59d918005).html

A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data

Autor: Egill Rostrup, Martin W Skjerbæk, Birgitte Fagerlund, Christos Pantelis, Martin Christian Axelsen, Jonathan Foldager, Merete Osler, Louise Baruël Johansen, Mette Ødegaard Nielsen, Søren Christensen, Lars Arvastson, Lars Kai Hansen, Bjørn H Ebdrup, Bob Oranje, Jayachandra Mitta Raghava, Birte Glenthøj, Bruce J Kinon, Nikolaj Bak, Karen Sando Ambrosen

Publikováno v: Translational Psychiatry, Vol 10, Iss 1, Pp 1-13 (2020)
Translational Psychiatry
Ambrosen, K S, Skjerbæk, M W, Foldager, J, Axelsen, M C, Bak, N, Arvastson, L, Christensen, S R, Johansen, L B, Raghava, J M, Oranje, B, Rostrup, E, Nielsen, M, Osler, M, Fagerlund, B, Pantelis, C, Kinon, B J, Glenthøj, B Y, Hansen, L K & Ebdrup, B H 2020, ' A machine-learning framework for robust and reliable prediction of short-and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data ', Translational Psychiatry, vol. 10, no. 1, 276 . https://doi.org/10.1038/s41398-020-00962-8
Ambrosen, K S, Skjerbæk, M W, Foldager, J, Axelsen, M C, Bak, N, Arvastson, L, Christensen, S R, Johansen, L B, Raghava, J M, Oranje, B, Rostrup, E, Nielsen, M, Osler, M, Fagerlund, B, Pantelis, C, Kinon, B J, Glenthøj, B Y, Hansen, L K & Ebdrup, B H 2020, ' A machine-learning framework for robust and reliable prediction of short-and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data ', Translational Psychiatry, vol. 10, no. 1, 276, pp. S34–S35 . https://doi.org/10.1038/s41398-020-00962-8

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::703df2e61a0f9354019ed329458ed853
https://doi.org/10.1038/s41398-020-00962-8