Abstract
Background
A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication.
Aims
To develop and evaluate a model that could predict the risk of TRS in routine clinical practice.
Method
We used data from two UK-based FEP cohorts (GAP and AESOP1-0) to develop and internally validate a prognostic
model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic
and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression,
with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping,
obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians
were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the
model.
Result
We included seven factors in the prediction model that [Q1]are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic
was 0.727 (95% CI 0.723–0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism.
Conclusion
We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and
carers[Q4] were involved in the development process. External validation of the tool is needed followed by co-design
methodology to support implementation in early intervention services.
Keywords:
First-episode schizophrenia
treatment resistant
prognostic model
decision analysis
mixed methods[Q5]
Treatment-resistant schizophrenia (TRS) is defined as the presence of persistent symptoms of at least moderate severity
and functional impairment despite treatment with at least two different antipsychotics used in adequate dose, each for 4–6
weeks’ minimum duration. Results from a recent meta-analysis showed that almost a quarter of people with first-episode
psychosis (FEP) or schizophrenia develop TRS in the early stages of treatment, increasing to one-third when the estimates
included those who relapse despite initial response and long-term follow-up. In England, 25–50% of the National Health
Service's (NHS) £11.8 billion mental health budget is allocated to schizophrenia care. Considering that about one-third of
people with schizophrenia develop TRS, treatment resistance accounts for a large proportion of these costs.
A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication.
Aims
To develop and evaluate a model that could predict the risk of TRS in routine clinical practice.
Method
We used data from two UK-based FEP cohorts (GAP and AESOP1-0) to develop and internally validate a prognostic
model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic
and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression,
with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping,
obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians
were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the
model.
Result
We included seven factors in the prediction model that [Q1]are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic
was 0.727 (95% CI 0.723–0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism.
Conclusion
We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and
carers[Q4] were involved in the development process. External validation of the tool is needed followed by co-design
methodology to support implementation in early intervention services.
Keywords:
First-episode schizophrenia
treatment resistant
prognostic model
decision analysis
mixed methods[Q5]
Treatment-resistant schizophrenia (TRS) is defined as the presence of persistent symptoms of at least moderate severity
and functional impairment despite treatment with at least two different antipsychotics used in adequate dose, each for 4–6
weeks’ minimum duration. Results from a recent meta-analysis showed that almost a quarter of people with first-episode
psychosis (FEP) or schizophrenia develop TRS in the early stages of treatment, increasing to one-third when the estimates
included those who relapse despite initial response and long-term follow-up. In England, 25–50% of the National Health
Service's (NHS) £11.8 billion mental health budget is allocated to schizophrenia care. Considering that about one-third of
people with schizophrenia develop TRS, treatment resistance accounts for a large proportion of these costs.
| Original language | English |
|---|---|
| Journal | BJPsych Internatioanl |
| Publication status | Accepted/In press - 31 May 2024 |