Abstract
Background: Prediction studies in subjects at Clinical High Risk (CHR) for psychosis are hampered by a high proportion of uncertain outcomes. We therefore investigated whether quantitative EEG (QEEG) parameters can contribute to an improved identification of CHR subjects with a later conversion to psychosis. Methods: This investigation was a project within the European Prediction of Psychosis Study (EPOS), a prospective multicenter, naturalistic field study with an 18-month follow-up period. QEEG spectral power and alpha peak frequencies (APF) were determined in 113 CHR subjects. The primary outcome measure was conversion to psychosis. Results: Cox regression yielded a model including frontal theta (HR = 1.82; [95% CI 1.00-3.32]) and delta (HR = 2.60; [95% CI 1.30-5.20]) power, and occipital-parietal APF (HR = .52; [95% CI .35-.80]) as predictors of conversion to psychosis. The resulting equation enabled the development of a prognostic index with three risk classes (hazard rate 0.057 to 0.81). Conclusions: Power in theta and delta ranges and APF contribute to the short-term prediction of psychosis and enable a further stratification of risk in CHR samples. Combined with (other) clinical ratings, EEG parameters may therefore be a useful tool for individualized risk estimation and, consequently, targeted prevention.
Original language | English |
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Pages (from-to) | 42-47 |
Number of pages | 6 |
Journal | Schizophrenia Research |
Volume | 153 |
Issue number | 1-3 |
DOIs | |
Publication status | Published - 1 Jan 2014 |
Funding
This study was supported by a grant for the Dutch Prediction of Psychosis Study from ZON-MW (ZorgOnderzoek Nederland/NWO-Medische Wetenschappen , project # 2630.0001 ) and a grant from the European Commission in Brussels, Belgium , for the European Prediction of Psychosis study (grant QLGU-CT-2001-01081 ). The funding sources had no influence on the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.
Keywords
- Clinical high risk
- Psychosis prediction
- QEEG