Prediction of REsPonse to Antipsychotics using
electRoEncephalography

Rationale:
Psychosis is a disorder of disturbed contact with reality, characterized by hallucinations, delusions, and disorganization of speech and behavior. Recovery from psychosis is highly variable: some patients may experience a psychotic episode briefly and only once, while others suffer from recurrent episodes or chronic symptoms. The time between onset of a first psychotic episode (FEP) and start of treatment is an important and modifiable predictor of outcome.
Antipsychotics are the first-choice treatment for patients with FEP. However, in up to 40% of patients, remission is not achieved in response to the first prescribed antipsychotic drug. Non-response only becomes clear after ten weeks of treatment and is unpredictable. Clozapine is an antipsychotic with superior response rates, but is only considered after non-response to two other antipsychotics because of potential severe side-effects. Clozapine could be prescribed up to six years earlier if non-response to other antipsychotics could be predicted. Development of biomarkers for response to antipsychotic treatment is therefore a crucial step towards personalized care for patients with FEP, as it will improve long-term outcomes, speed up remission of psychosis and prevent unnecessary side-effects in non-responders.
Recent advances in artificial intelligence in combination with suitable biomarkers now allow a personalized approach to diagnosis and treatment in psychosis patients. Electroencephalography (EEG) is a feasible and cost-effective candidate biomarker that directly measures electrophysiological brain activity with high temporal resolution. A pilot study showed that clozapine treatment response in otherwise treatment-resistant schizophrenia patients could be predicted with 85% accuracy. We have also shown the merit of machine learning of quantitative EEG characteristics in previous studies in other disorders including dementia and delirium, and found diagnostic accuracies based on this information up to 95%. To improve prediction and identification of the underlying biological processes we will make use of the ability to investigate changes in the regulation of gene transcription by investigating changes in DNA methylation in whole blood as done previously in treatment studies by our group (PMID: 31645664).

Objectives: The primary objective of this study is to develop prediction markers of antipsychotic treatment outcome in patients with FEP, using machine learning to analyze the wealth of readily available clinical data in our center, combined with EEG recordings and changes in genome-wide methylation levels..

Study design: The study will be a mono-center observational cohort study, in collaboration with referring clinical centers in the Utrecht area, Amsterdam UMC, and UMC Groningen. Detailed patient information will be obtained at baseline, before patients start with antipsychotic treatment. An EEG recording and blood sample for DNA analysis will also be obtained at baseline. Follow-up will be scheduled at clinical evaluation points after (1) 4-6 weeks and (2) after 8-13 weeks of treatment-as-usual. Retrospectively, patients will be divided in responders and non-responders based on these assessment. An additional EEG recording and blood sample will be obtained at the second follow-up visit.

Study population:
Patients between 16 and 40 years with a first psychotic episode who are to receive treatment with antipsychotic medication (N=100) will be included. Patients will be recruited in the UMCU, and referring regional mental health centers (Altrecht) and national expertise centers (UMC Groningen, Amsterdam UMC) where appropriate. Study activities will be performed by UMC Utrecht researchers, making this a mono-center study with collaborative centers for referral of participants.

Main study parameters/endpoints:
For the primary outcome, patients will be divided into a responder and a non-responder group based on the Positive and Negative Symptoms Scale (PANSS) scores and remission criteria as defined by Andreasen and others. Secondary outcomes include improvement on questionnaires for the quality of life, general and physical health and functional outcome. Tertiary outcome includes continuous changes in psychosis symptom levels as measured by the PANSS.

Nature and extent of the burden and risks associated with participation, benefit and group relatedness: Most measurements included in this The study consists mostare of non-invasive measurements, except for small blood samples we will drawn, and will not require additional patient visits. The visits for the standard of care require additional time investment for questionnaires, EEG -data recording and blood sampling. The burden and risks are low to very low. Patients will receive 20 euro of compensation per visit for the participation in the study. Also, patients who participate receive a detailed report of the measurements ( questionnaires, EEG). This can lead to a better understanding of their complaints, functioning and if applicable, of their diagnosis. The main objective is to develop prediction markers so future patients may have benefit in their treatment success.

We expect that EEG characteristics can serve as predicite biomarker for treatment response in patients with a first psychotic episode.

Visit 0: baseline visit
Visit 1: 4-6 weeks after visit 0
Visit 2: 8- 13 weeks after visit 1

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