Modeling the effects of treatment resistance and anticholinergic burden on cognitive function domains in patients with schizophrenia.

The contribution of anticholinergic burden to cognitive function in patients with treatment resistant schizophrenia (TRS) is uncertain. This case-control study aims to comprehensively examine the association between treatment resistance and cognitive functions and the contribution of anticholinergic burden in patients with schizophrenia. Anticholinergic burden of all patients was calculated using the Anticholinergic Cognitive Burden scale. Exploratory Factor Analysis of 11 cognitive assessments identified four cognitive domains: verbal memory, attention and general cognitive functions, visual memory and processing speed, and executive function. Two structural equation models (SEM) examined the relationship of TRS and these cognitive functions with, and without considering anticholinergic burden. A total of 288 participants were included (TRS N=111, non-TRS N=177). Patients with TRS performed poorer than the non-TRS group only in the executive function domain. Anticholinergic burden contributed significantly to the attention and general cognitive functions, visual memory and processing speed, and executive function. The impact of TRS on executive function was no longer significant after adding anticholinergic burden to the SEM. Results suggested that anticholinergic burden contributes to a wide range of cognitive function impairment in patients with schizophrenia and is likely to be part of the apparent differences of cognitive function between TRS and non-TRS.

Development of an individualized risk calculator of treatment resistance in patients with first-episode psychosis (TRipCal) using automated machine learning: a 12-year follow-up study with clozapine prescription as a proxy indicator.

About 15-40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of development of TR early in the course of illness is important to provide personalized intervention. A total of 1400 patients with FEP enrolled in the early intervention for psychosis service or receiving the standard psychiatric service between July 1, 1998, and June 30, 2003, for the first time were included. Clozapine prescriptions until June 2015, as a proxy of TR, were obtained. Premorbid information, baseline characteristics, and monthly clinical information were retrieved systematically from the electronic clinical management system (CMS). Training and testing samples were established with random subsampling. An automated machine learning (autoML) approach was used to optimize the ML algorithm and hyperparameters selection to establish four probabilistic classification models (baseline, 12-month, 24-month, and 36-month information) of TR development. This study found 191 FEP patients (13.7%) who had ever been prescribed clozapine over the follow-up periods. The ML pipelines identified with autoML had an area under the receiver operating characteristic curve ranging from 0.676 (baseline information) to 0.774 (36-month information) in predicting future TR. Features of baseline information, including schizophrenia diagnosis and age of onset, and longitudinal clinical information including symptoms variability, relapse, and use of antipsychotics and anticholinergic medications were important predictors and were included in the risk calculator. The risk calculator for future TR development in FEP patients (TRipCal) developed in this study could support the continuous development of data-driven clinical tools to assist personalized interventions to prevent or postpone TR development in the early course of illness and reduce delay in clozapine initiation.