A Novel Way to Quantify Schizophrenia Symptoms in Clinical Trials

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aut.relation.articlenumbere13398en_NZ
aut.relation.issue3en_NZ
aut.relation.journalEuropean Journal of Clinical Investigationen_NZ
aut.relation.volume51en_NZ
aut.researcherSandham, Margaret
dc.contributor.authorMedvedev, ONen_NZ
dc.contributor.authorBerk, Men_NZ
dc.contributor.authorDean, OMen_NZ
dc.contributor.authorBrown, Een_NZ
dc.contributor.authorSandham, MHen_NZ
dc.contributor.authorDipnall, JFen_NZ
dc.contributor.authorMcNamara, RKen_NZ
dc.contributor.authorSumich, Aen_NZ
dc.contributor.authorKrägeloh, CUen_NZ
dc.contributor.authorNarayanan, Aen_NZ
dc.contributor.authorSiegert, RJen_NZ
dc.date.accessioned2021-06-08T01:36:30Z
dc.date.available2021-06-08T01:36:30Z
dc.date.copyright2021en_NZ
dc.date.issued2021en_NZ
dc.description.abstractBACKGROUND: A major problem in quantifying symptoms of schizophrenia is establishing a reliable distinction between enduring and dynamic aspects of psychopathology. This is critical for accurate diagnosis, monitoring and evaluating treatment effects in both clinical practice and trials. MATERIALS AND METHODS: We applied Generalisability Theory, a robust novel method to distinguish between dynamic and stable aspects of schizophrenia symptoms in the widely used Positive and Negative Symptom Scale (PANSS) using a longitudinal measurement design. The sample included 107 patients with chronic schizophrenia assessed using the PANSS at five time points over a 24-week period during a multi-site clinical trial of N-Acetylcysteine as an add-on to maintenance medication for the treatment of chronic schizophrenia. RESULTS: The original PANSS and its three subscales demonstrated good reliability and generalisability of scores (G=0.77-0.93) across sample population and occasions making them suitable for assessment of psychosis risks and long-lasting change following a treatment, while subscales of the five factor models appeared less reliable. The most enduring symptoms represented by the PANSS were poor attention, delusions, blunted affect, and poor rapport. More dynamic symptoms with 40-50% of variance explained by patient transient state including grandiosity, preoccupation, somatic concerns, guilt feeling and hallucinatory behaviour. CONCLUSIONS: Identified dynamic symptoms are more amendable to change and should be the primary target of interventions aiming at effectively treating schizophrenia. Separating out the dynamic symptoms would increase assay sensitivity in trials, reduce the signal to noise ratio and increase the potential to detect the effects of novel therapies in clinical trials.en_NZ
dc.identifier.citationEuropean Journal of Clinical Investigation, 51(3), e13398.
dc.identifier.doi10.1111/eci.13398en_NZ
dc.identifier.issn0014-2972en_NZ
dc.identifier.issn1365-2362en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14249
dc.languageengen_NZ
dc.publisherWileyen_NZ
dc.relation.urihttps://onlinelibrary.wiley.com/doi/10.1111/eci.13398
dc.rights© 2020 The Authors. European Journal of Clinical Investigation published by John Wiley & Sons Ltd on behalf of Stichting European Society for Clinical Investigation Journal Foundation This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectClinical Trialen_NZ
dc.subjectGeneralisability Theoryen_NZ
dc.subjectMeasurementen_NZ
dc.subjectPositive and Negative Syndrome Scaleen_NZ
dc.subjectSchizophreniaen_NZ
dc.titleA Novel Way to Quantify Schizophrenia Symptoms in Clinical Trialsen_NZ
dc.typeJournal Article
pubs.elements-id391762
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences/Centre for Artificial Intelligence Research
pubs.organisational-data/AUT/Faculty of Health & Environmental Science
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Clinical Sciences
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Clinical Sciences/Nursing Department
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Clinical Sciences/Psychology & Neuroscience Department
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HH Clinical Sciences 2018 PBRF
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HY Public Health & Psychosocial Studies 2018 PBRF
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