Predicting onset of early- and late-treatment resistance in first-episode schizophrenia patients using advanced shrinkage statistical methods in a small sample. (December 2020)

• Record Type: Journal Article
• Authors: Ajnakina, Olesya; Agbedjro, Deborah; Lally, John; Forti, Marta Di; Trotta, Antonella; Mondelli, Valeria; Pariante, Carmine; Dazzan, Paola; Gaughran, Fiona; Fisher, Helen L.; David, Anthony; Murray, Robin M.; Stahl, Daniel
• Is Part Of: Psychiatry research. Volume 294(2020)
• Publication Date: 2020-12
• Subjects: Statistical learning -- prognosis -- Schizophrenia -- Treatment resistance -- Treatment response -- Prediction; Psychiatry -- Periodicals; Psychiatry -- periodicals; Psychiatrie -- Périodiques; 616.89
• DOI: 10.1016/j.psychres.2020.113527
• Languages: English
• ISSNs: 0165-1781

Abstract

Highlights: Evidence suggests there are two subtypes of treatment-resistant schizophrenia (i.e. early treatment resistant (E-TR) and late-treatment resistant (L-TR) schizophrenia). Using the known risk factors for poor schizophrenia outcomes and employing advanced statistical shrinkage methods, our results showed it was possible to predict with sufficient accuracy who is a greater vs lesser risk to meet criteria for E-TR and L-TR during the 5-year follow-up after the first contact with mental health services for schizophrenia. However, sensitivity for these prediction models was low implying that further work is necessary to explore way of improving these prediction models for such rare but important outcomes before they can be used for a more in-depth risk assessment, follow-up monitoring and individually tailored prevention strategies. Abstract: Evidence suggests there are two treatment-resistant schizophrenia subtypes (i.e. early treatment resistant (E-TR) and late-treatment resistant (L-TR)). We aimed to develop prediction models for estimating individual risk for these outcomes by employing advanced statistical shrinkage methods. 239 first-episode schizophrenia (FES) patients were followed-up for approximately 5 years after first presentation to psychiatric services; of these, n =56 (25.2%) were defined as E-TR and n =24 (12.6%) were defined as L-TR. Using known risk factors for poor schizophrenia outcomes, we developed prediction models for E-TR and L-TR using LASSO and RIDGE logistic regression models. Models' internal validation was performed employing Harrell's optimism-correction with repeated cross-validation; their predictive accuracy was assessed through discrimination and calibration. Both LASSO and RIDGE models had high discrimination, good calibration. While LASSO had moderate sensitivity for estimating an individual risk for E-TR and L-TR, sensitivity estimated for RIDGE model for these outcomes was extremely low, which was due to having a very large estimated optimism. Although it was possible to discriminate with sufficient accuracy who would meet criteria for E-TR and L-TR during the 5-year follow-up after first contact with mental health services for schizophrenia, further work is necessary to improve sensitivity for these models.