Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning
Abstract Clinical course after first episode psychosis (FEP) is heterogeneous. Subgrouping and predicting longitudinal symptom trajectories after FEP may help develop personalized treatment approaches. We utilized k-means clustering to identify clusters of 411 FEP patients based on longitudinal posi...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | npj Mental Health Research |
| Online Access: | https://doi.org/10.1038/s44184-025-00129-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849310307757850624 |
|---|---|
| author | Yanan Liu Sara Jalali Ridha Joober Martin Lepage Srividya Iyer Jai Shah David Benrimoh |
| author_facet | Yanan Liu Sara Jalali Ridha Joober Martin Lepage Srividya Iyer Jai Shah David Benrimoh |
| author_sort | Yanan Liu |
| collection | DOAJ |
| description | Abstract Clinical course after first episode psychosis (FEP) is heterogeneous. Subgrouping and predicting longitudinal symptom trajectories after FEP may help develop personalized treatment approaches. We utilized k-means clustering to identify clusters of 411 FEP patients based on longitudinal positive and negative symptoms. Three clusters were identified. Cluster 1 exhibits lower positive and negative symptoms (LS), lower antipsychotic dose, and relatively higher affective psychosis; Cluster 2 shows lower positive symptoms, persistent negative symptoms (LPPN), and intermediate antipsychotic doses; Cluster 3 presents persistently high levels of both positive and negative symptoms (PPNS), and higher antipsychotic doses. We predicted cluster membership (AUC of 0.74) using ridge logistic regression on baseline data. Key predictors included lower levels of apathy, affective flattening, and anhedonia/asociality in the LS cluster, compared to the LPPN cluster. Hallucination severity, positive thought disorder and manic hostility predicted PPNS. These results help parse the FEP trajectory heterogeneity and may facilitate the development of personalized treatments. |
| format | Article |
| id | doaj-art-c5bcc232d991457aab9f88bc431ecbd9 |
| institution | Kabale University |
| issn | 2731-4251 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Mental Health Research |
| spelling | doaj-art-c5bcc232d991457aab9f88bc431ecbd92025-08-20T03:53:46ZengNature Portfolionpj Mental Health Research2731-42512025-05-014111010.1038/s44184-025-00129-7Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learningYanan Liu0Sara Jalali1Ridha Joober2Martin Lepage3Srividya Iyer4Jai Shah5David Benrimoh6Montreal Neurological Institute, McGill UniversityDouglas Research CentreDouglas Research CentreDouglas Research CentreDouglas Research CentreDouglas Research CentreDouglas Research CentreAbstract Clinical course after first episode psychosis (FEP) is heterogeneous. Subgrouping and predicting longitudinal symptom trajectories after FEP may help develop personalized treatment approaches. We utilized k-means clustering to identify clusters of 411 FEP patients based on longitudinal positive and negative symptoms. Three clusters were identified. Cluster 1 exhibits lower positive and negative symptoms (LS), lower antipsychotic dose, and relatively higher affective psychosis; Cluster 2 shows lower positive symptoms, persistent negative symptoms (LPPN), and intermediate antipsychotic doses; Cluster 3 presents persistently high levels of both positive and negative symptoms (PPNS), and higher antipsychotic doses. We predicted cluster membership (AUC of 0.74) using ridge logistic regression on baseline data. Key predictors included lower levels of apathy, affective flattening, and anhedonia/asociality in the LS cluster, compared to the LPPN cluster. Hallucination severity, positive thought disorder and manic hostility predicted PPNS. These results help parse the FEP trajectory heterogeneity and may facilitate the development of personalized treatments.https://doi.org/10.1038/s44184-025-00129-7 |
| spellingShingle | Yanan Liu Sara Jalali Ridha Joober Martin Lepage Srividya Iyer Jai Shah David Benrimoh Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning npj Mental Health Research |
| title | Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning |
| title_full | Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning |
| title_fullStr | Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning |
| title_full_unstemmed | Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning |
| title_short | Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning |
| title_sort | subtyping first episode psychosis based on longitudinal symptom trajectories using machine learning |
| url | https://doi.org/10.1038/s44184-025-00129-7 |
| work_keys_str_mv | AT yananliu subtypingfirstepisodepsychosisbasedonlongitudinalsymptomtrajectoriesusingmachinelearning AT sarajalali subtypingfirstepisodepsychosisbasedonlongitudinalsymptomtrajectoriesusingmachinelearning AT ridhajoober subtypingfirstepisodepsychosisbasedonlongitudinalsymptomtrajectoriesusingmachinelearning AT martinlepage subtypingfirstepisodepsychosisbasedonlongitudinalsymptomtrajectoriesusingmachinelearning AT srividyaiyer subtypingfirstepisodepsychosisbasedonlongitudinalsymptomtrajectoriesusingmachinelearning AT jaishah subtypingfirstepisodepsychosisbasedonlongitudinalsymptomtrajectoriesusingmachinelearning AT davidbenrimoh subtypingfirstepisodepsychosisbasedonlongitudinalsymptomtrajectoriesusingmachinelearning |