Abstract / Description of output
Importance
Early illness course correlates with long-term outcome in psychosis. Accurate prediction would allow more focused intervention. Earlier intervention corresponds to better symptomatic and functional outcomes.
Objective
To use routinely collected baseline demographic and psychometric data to predict employment, education or training (EET) status, and symptom remission in patients with first episode psychosis (FEP) at 1 year.
Design, Setting, and Participants
83 FEP patients were recruited from National Health Service (NHS) Glasgow between 2011 and 2014 to a 24-month prospective cohort study with regular assessment of demographic and psychometric measures. An external independent cohort of 79 FEP patients were recruited from NHS Glasgow and Edinburgh during a 12-month study between 2006 and 2009.
Main Outcomes and Measures
Elastic net regularised logistic regression models were built to predict binary EET status, period and point remission outcomes at 1 year on 83 Glasgow patients (training dataset). Models were externally validated on an independent dataset of 79 patients from Glasgow and Edinburgh (validation dataset). Only baseline predictors shared across both cohorts were made available for model training and validation.
Results
After excluding participants with missing outcomes, models were built on the training dataset for EET status, period and point remission outcomes and externally validated on the validation dataset. Models predicted EET status, period and point remission with ROC area under curve (AUC) performances of 0.876 (95%CI: 0.864, 0.887), 0.630 (95%CI: 0.612, 0.647) and 0.652 (95%CI: 0.635, 0.670) respectively. Positive predictors of EET included baseline EET and living with spouse/children. Negative predictors included higher PANSS suspiciousness, hostility and delusions scores. Positive predictors for symptom remission included living with spouse/children, and affective symptoms on the Positive and Negative Syndrome Scale (PANSS). Negative predictors of remission included passive social withdrawal symptoms on PANSS.
Conclusions and Relevance
Using advanced statistical machine learning techniques, we provide the first externally validated evidence for the ability to predict 1-year EET status and symptom remission in FEP patients.
Early illness course correlates with long-term outcome in psychosis. Accurate prediction would allow more focused intervention. Earlier intervention corresponds to better symptomatic and functional outcomes.
Objective
To use routinely collected baseline demographic and psychometric data to predict employment, education or training (EET) status, and symptom remission in patients with first episode psychosis (FEP) at 1 year.
Design, Setting, and Participants
83 FEP patients were recruited from National Health Service (NHS) Glasgow between 2011 and 2014 to a 24-month prospective cohort study with regular assessment of demographic and psychometric measures. An external independent cohort of 79 FEP patients were recruited from NHS Glasgow and Edinburgh during a 12-month study between 2006 and 2009.
Main Outcomes and Measures
Elastic net regularised logistic regression models were built to predict binary EET status, period and point remission outcomes at 1 year on 83 Glasgow patients (training dataset). Models were externally validated on an independent dataset of 79 patients from Glasgow and Edinburgh (validation dataset). Only baseline predictors shared across both cohorts were made available for model training and validation.
Results
After excluding participants with missing outcomes, models were built on the training dataset for EET status, period and point remission outcomes and externally validated on the validation dataset. Models predicted EET status, period and point remission with ROC area under curve (AUC) performances of 0.876 (95%CI: 0.864, 0.887), 0.630 (95%CI: 0.612, 0.647) and 0.652 (95%CI: 0.635, 0.670) respectively. Positive predictors of EET included baseline EET and living with spouse/children. Negative predictors included higher PANSS suspiciousness, hostility and delusions scores. Positive predictors for symptom remission included living with spouse/children, and affective symptoms on the Positive and Negative Syndrome Scale (PANSS). Negative predictors of remission included passive social withdrawal symptoms on PANSS.
Conclusions and Relevance
Using advanced statistical machine learning techniques, we provide the first externally validated evidence for the ability to predict 1-year EET status and symptom remission in FEP patients.
| Original language | English |
|---|---|
| Article number | e0212846 |
| Journal | PLoS ONE |
| Volume | 14 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 7 Mar 2019 |