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Abstract
Background: Negative symptoms contribute towards substantial disability in people with schizophrenia. However, less is known about their impact compared to other symptoms in first episode psychosis (FEP). We sought to investigate how psychosis symptom clusters are associated with clinical outcomes in people with FEP using a large electronic health record (EHR) dataset.
Methods: Data were obtained from pseudonymised EHRs of 1,835 people with FEP in South London. Five psychosis symptom clusters (positive, negative, disorganisation, mania and depression) were identified using CRIS‐CODE NLP software. Data on subsequent hospital admission and the number of unique antipsychotics prescribed in the 5 years following presentation were obtained. Their relationship with psychosis symptom clusters was analysed using multivariable negative binomial regression with age, gender and ethnicity as covariates.
Results: Mania (IRR 6.37, 95% CI 3.24 to 12.5) and positive symptoms (3.87, 1.98 to 7.58) were more strongly associated with increased hospital admission than disorganisation (1.68, 0.46 to 6.15), depression (1.55, 0.71 to 3.39) or negative symptoms (1.51, 0.51 to 4.48). Negative symptoms were more strongly associated with increased antipsychotic treatment failure (4.49, 2.35 to 8.59) than mania (3.65, 2.42 to 5.52), positive symptoms (3.35, 2.22 to 5.06), disorganisation (2.64, 1.23 to 5.68) or depression (2.18, 1.36 to 3.49).
Conclusion: Increased mania and positive symptom burden predict greater hospitalisation than negative symptoms. However, negative symptoms are more strongly associated with increased antipsychotic treatment failure. These findings illustrate that although mania and positive symptoms result in increased hospitalisation, negative symptoms are less responsive to antipsychotic treatment.
Methods: Data were obtained from pseudonymised EHRs of 1,835 people with FEP in South London. Five psychosis symptom clusters (positive, negative, disorganisation, mania and depression) were identified using CRIS‐CODE NLP software. Data on subsequent hospital admission and the number of unique antipsychotics prescribed in the 5 years following presentation were obtained. Their relationship with psychosis symptom clusters was analysed using multivariable negative binomial regression with age, gender and ethnicity as covariates.
Results: Mania (IRR 6.37, 95% CI 3.24 to 12.5) and positive symptoms (3.87, 1.98 to 7.58) were more strongly associated with increased hospital admission than disorganisation (1.68, 0.46 to 6.15), depression (1.55, 0.71 to 3.39) or negative symptoms (1.51, 0.51 to 4.48). Negative symptoms were more strongly associated with increased antipsychotic treatment failure (4.49, 2.35 to 8.59) than mania (3.65, 2.42 to 5.52), positive symptoms (3.35, 2.22 to 5.06), disorganisation (2.64, 1.23 to 5.68) or depression (2.18, 1.36 to 3.49).
Conclusion: Increased mania and positive symptom burden predict greater hospitalisation than negative symptoms. However, negative symptoms are more strongly associated with increased antipsychotic treatment failure. These findings illustrate that although mania and positive symptoms result in increased hospitalisation, negative symptoms are less responsive to antipsychotic treatment.
| Original language | English |
|---|---|
| Pages (from-to) | 120 |
| Number of pages | 1 |
| Journal | Early Intervention in Psychiatry |
| Volume | 12 |
| Issue number | S1 |
| Publication status | Published - 17 Sept 2018 |
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Dive into the research topics of 'Negative Symptoms in First Episode Psychosis (FEP) Predict Antipsychotic Treatment Failure: A Natural Language Processing (NLP) Electronic Health Record Study'. Together they form a unique fingerprint.Projects
- 2 Finished
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Linking electronic health records with passive smartphone activity data to predict outcomes in psychotic disorders
Patel, R. (Primary Investigator), McGuire, P. (Primary Investigator) & Curcin, V. (Primary Investigator)
14/02/2018 → 13/02/2021
Project: Research
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Symptom dimensions in first episode psychosis: predicting clinical outcomes using natural language processing
Patel, R. (Primary Investigator)
3/10/2016 → 2/10/2018
Project: Research