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

PSYCHIATRY RESEARCH

卷:294

Article

Ajnakina, Olesya^1,2  Agbedjro, Deborah¹  Lally, John^3,4,5  Di Forti, Marta⁶  Trotta, Antonella^6,7  Mondelli, Valeria⁸  Pariante, Carmine⁸  Dazzan, Paola³  Gaughran, Fiona^3,9  Fisher, Helen L.⁶  David, Anthony¹⁰  Murray, Robin M.^3,11  Stahl, Daniel¹ 

[1] Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Biostat & Hlth Informat, London, England

[2] UCL, Inst Epidemiol & Hlth Care, Dept Behav Sci & Hlth, London, England

[3] Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Psychosis Studies, London, England

[4] Royal Coll Surgeons Ireland, Dept Psychiat, Dublin, Ireland

[5] St Vincents Hosp Fairview, Dept Psychiat, Dublin, Ireland

[6] Kings Coll London, Inst Psychiat Psychol & Neurosci, Social Genet & Dev Psychiat Ctr, London, England

[7] South London & Maudsley NHS Fdn Trust, Tony Hillis Unit, London, England

[8] Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Psychol Med, London, England

[9] South London & Maudsley NHS Fdn Trust, Natl Psychosis Serv, London, England

[10] UCL, Inst Mental Hlth, London, England

[11] Univ Palermo, Dept Psychiat Expt Biomed & Clin Neurosci, Palermo, Italy

关键词: Statistical learning;    prognosis;    Schizophrenia;    Treatment resistance;    Treatment response;    Prediction;   

DOI  :  10.1016/j.psychres.2020.113527

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free