Early intervention in psychiatry
Par Farooq S, Fonseka N, Wellappuli N et 13 autres.
Contenu en anglais
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In high-income countries, early intervention in psychosis (EIP) services provide treatment for first episode psychosis (FEP) as early as possible, proven to effectively improve health outcomes. These services rarely exist in low- and lower-middle-income countries (LMICs). The TRANSLATE study aims to evaluate the implementation of the EIP services using the selected indicators and to develop a clinical tool to predict the risk of Treatment-Resistant Schizophrenia (TRS) in patients with FEP.
This is an observational implementation research study conducted across five sites in Pakistan and one in Sri Lanka, recruiting 670 FEP patients. The evaluation of the implementation of the service will include key clinical and process outcomes such as disengagement from services, rates of remission, the level of functioning, quality of life, psychopathology and physical health. Required data will be collected over a 1-year period at baseline and at 1, 3, 6 and 12-month intervals. A descriptive analysis of baseline data and service utilisation data will be conducted, stratified by site. Probabilities of disengagement, remission and process evaluation outcomes will be modelled using logistic regression models adjusting for duration of untreated psychosis, baseline symptom severity and geographical location. Selected key clinical outcomes will also be analysed and presented descriptively. To develop a clinical model prediction model for TRS, a penalised multivariable logistic regression model will be fitted to estimate the one-year risk of TRS following an FEP diagnosis, incorporating up to 15 predictor variables identified within the study cohort. Model performance will be assessed using calibration plots and the C-statistics for discrimination. Internal validation will be carried out by bootstrapping and the optimism-adjusted calibration slope will be used as a global shrinkage factor to account for overfitting, with the model intercept re-estimated to ensure calibration-in-the-large.
This will be the first large study that will evaluate the implementation of EIP service in a LMIC country setting. The findings of this study will inform the adoption and scaling-up of EIP services in routine clinical practice. The clinical prediction model for the early detection of TRS will help initiate effective treatments for TRS at the earliest possible stage and potentially reduce the significant burden of disease associated with TRS.
ClinicalTrials.gov identifier: ISRCTN16495294.
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