RETRACTED: Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Post-publication record
- Nature
- Retraction
- Reason
- Error in Analyses;Error in Results and/or Conclusions;Upgrade/Update of Prior Notice(s);
- Date
- 11/2/2023 0:00
- Flagged by OpenAlex?
- Yes
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement — it reports them as false, which reads as “fine”.
Abstract
Importance: Leveraging the dynamic nature of clinical variables in the clinical high risk for psychosis (CHR-P) population has the potential to significantly improve the performance of outcome prediction models. Objective: To improve performance of prediction models and elucidate dynamic clinical profiles using joint modeling to predict conversion to psychosis and symptom remission. Design, Setting, and Participants: Data were collected as part of the third wave of the North American Prodrome Longitudinal Study (NAPLS 3), which is a 9-site prospective longitudinal study. Participants were individuals aged 12 to 30 years who met criteria for a psychosis-risk syndrome. Clinical, neurocognitive, and demographic variables were collected at baseline and at multiple follow-up visits, beginning at 2 months and up to 24 months. An initial feature selection process identified longitudinal clinical variables that showed differential change for each outcome group across 2 months. With these variables, a joint modeling framework was used to estimate the likelihood of eventual outcomes. Models were developed and tested in a 10-fold cross-validation framework. Clinical data were collected between February 2015 and November 2018, and data were analyzed from February 2022 to December 2023. Main Outcomes and Measures: Prediction models were built to predict conversion to psychosis and symptom remission. Participants met criteria for conversion if their positive symptoms reached the fully psychotic range and for symptom remission if they were subprodromal on the Scale of Psychosis-Risk Symptoms for a duration of 6 months or more. Results: Of 488 included NAPLS 3 participants, 232 (47.5%) were female, and the mean (SD) age was 18.2 (3.4) years. Joint models achieved a high level of accuracy in predicting conversion (balanced accuracy [BAC], 0.91) and remission (BAC, 0.99) compared with baseline models (conversion: BAC, 0.65; remission: BAC, 0.60). Clinical variables that showed differential change between outcome groups across a 2-month span, including measures of symptom severity and aspects of functioning, were also identified. Further, intra-individual risks for each outcome were more negatively correlated when using joint models (r = -0.92; P < .001) compared with baseline models (r = -0.50; P < .001). Conclusions and Relevance: In this study, joint models significantly outperformed baseline models in predicting both conversion and remission, demonstrating that monitoring short-term clinical change may help to parse heterogeneous dynamic clinical trajectories in a CHR-P population. These findings could inform additional study of targeted treatment selection and could move the field closer to clinical implementation of prediction models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- JAMA Psychiatry
- Topic
- Schizophrenia research and treatment
- Field
- Medicine
- Canadian institutions
- Hotchkiss Brain InstituteUniversity of Calgary
- Funders
- National Center for Advancing Translational SciencesNational Institute of Mental Health
- Keywords
- ProdromeNeurocognitivePsychosisPopulationSchizophrenia (object-oriented programming)PsychologyLongitudinal studyMedicinePsychiatryClinical psychologyCognition
- Has abstract in OpenAlex
- yes