Abstract
Background
There is little evidence on the accuracy of psychosis relapse prediction models.
Aim
To conduct a systematic review of psychosis relapse prediction models
.
Method
We conducted a literature search including studies that developed and/or validated psychosis relapse prediction models. Study quality was assessed and narrative synthesis was used to combine studies.
Results
There were two eligible studies. One developed a model using prodromal symptoms and illness-related variables, which explained 14% of relapse variance but was at high risk of bias. The second developed a model using administrative data which was moderately discriminative (C=0.631) and associated with relapse (OR 1.11 95% CI 1.10, 1.12) and achieved moderately discriminative capacity when validated (C=0.630). The risk of bias was low.
Conclusions
It is unclear whether prodromal symptoms are useful for predicting relapse. The use of routine data to develop prediction models may be a more promising approach.
There is little evidence on the accuracy of psychosis relapse prediction models.
Aim
To conduct a systematic review of psychosis relapse prediction models
.
Method
We conducted a literature search including studies that developed and/or validated psychosis relapse prediction models. Study quality was assessed and narrative synthesis was used to combine studies.
Results
There were two eligible studies. One developed a model using prodromal symptoms and illness-related variables, which explained 14% of relapse variance but was at high risk of bias. The second developed a model using administrative data which was moderately discriminative (C=0.631) and associated with relapse (OR 1.11 95% CI 1.10, 1.12) and achieved moderately discriminative capacity when validated (C=0.630). The risk of bias was low.
Conclusions
It is unclear whether prodromal symptoms are useful for predicting relapse. The use of routine data to develop prediction models may be a more promising approach.
| Original language | English |
|---|---|
| Article number | e0183998 |
| Number of pages | 12 |
| Journal | PLoS ONE |
| Volume | 12 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 21 Sep 2017 |