Abstract
Background: Multimodal modeling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals who are at ultra-high risk. Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in gray matter volume in multiple brain regions identified by magnetic resonance imaging.
Methods: In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality—cognition, cortical structure information, and the neuroanatomical measure of brain age gap—to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well-characterized cohort of Australian youths who were identified as ultra-high risk of transitioning to psychosis using the Comprehensive Assessment of At Risk Mental States (CAARMS) and followed for up to 18 years; it contains clinical data (from N = 416 participants), cognitive data (n = 213), and magnetic resonance imaging cortical parameters extracted using FreeSurfer (n = 231).
Results: The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0%–12%, brain age gap 7%, cognition 0%–16%).
Conclusions: In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in the fit of the model for long-term prediction of transition to psychosis.
Methods: In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality—cognition, cortical structure information, and the neuroanatomical measure of brain age gap—to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well-characterized cohort of Australian youths who were identified as ultra-high risk of transitioning to psychosis using the Comprehensive Assessment of At Risk Mental States (CAARMS) and followed for up to 18 years; it contains clinical data (from N = 416 participants), cognitive data (n = 213), and magnetic resonance imaging cortical parameters extracted using FreeSurfer (n = 231).
Results: The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0%–12%, brain age gap 7%, cognition 0%–16%).
Conclusions: In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in the fit of the model for long-term prediction of transition to psychosis.
| Original language | English |
|---|---|
| Pages (from-to) | 417-428 |
| Number of pages | 12 |
| Journal | Biological Psychiatry: Cognitive Neuroscience and Neuroimaging |
| Volume | 9 |
| Issue number | 4 |
| Early online date | 3 Dec 2023 |
| DOIs | |
| Publication status | Published - Apr 2024 |
Bibliographical note
Acknowledgments and Disclosures:This work was supported by the Prediction of Early Mental Disorder and Preventive Treatment (http://www.pre-empt.org.au)—Centre of Research Excellence (National Health and Medical Research Council [NHMRC] Grant No. 1198304).
PDM received grants from the National Institute of Mental Health during the conduct of the study. In addition, PDM had the following patent issued: AU 2015203289; US 9884034; US 15/844444; and CA 2773031. PDM has received past unrestricted grant funding from Janssen-Cilag, Astra Zeneca, Eli Lilly, Novartis, and Pfizer and honoraria for consultancy and teaching from Janssen-Cilag, Eli Lilly, Pfizer, Astra Zeneca, Roche, Bristol Meyers Squibb, and Lundbeck. He has received grant funding from the Colonial Foundation, NMMRC, Australian Research Council, National Alliance for Research on Schizophrenia & Depression, Stanley Foundation, National Institutes of Health, Wellcome Trust, and Australian and Victorian governments. SRC received speaker/consultation fees from Janssen-Cilag, Lundbeck, Otsuka, and Servier and research funding from Janssen-Cilag, Lundbeck, Otsuka, and Gilead. BN was supported by NHMRC Senior Research Fellowship (Grant No. 1137687) and a University of Melbourne Dame Kate Campbell Fellowship, all of which were unrelated to this work. CP was supported by NHMRC L3 Investigator (Grant No. 1196508) outside the submitted work. KA was supported by NHMRC Career Development Fellowship (Grant No. 1141207) and a University of Melbourne Dame Kate Campbell Fellowship. KA has received funding from the NHMRC, Medical Research Future Fund, and Wellcome Trust, all unrelated to this work. AL was supported by NHMRC Emerging Leadership Fellowship (Grant No. 2010063). ARY was supported by NHMRC Principal Research Fellowship (Grant No. 1136829). GPA was supported by NHMRC Senior Research Fellowship (Grant No. 1080963). JTWW was funded by Netherlands Organization for Scientific Research Veni (Grant No. 016.156.019). All other authors report no biomedical financial interests or potential conflicts of interest.
Keywords
- Cox model
- Multimodal modeling
- PACE 400
- Prediction
- Schizophrenia
- UHR