Nomogram model for predicting medication adherence in patients with various mental disorders based on the Dryad database

Objective Treatment compliance among psychiatric patients is related to disease outcomes. How to assess patient compliance remains a concern. Here, we established a predictive model for medication compliance in patients with psychotic disorders to provide a reference for early intervention in treatment non-compliance behaviour.Design Clinical information for 451 patients with psychotic disorders was downloaded from the Dryad database. The Least Absolute Shrinkage and Selection Operator regression and logistic regression were used to establish the model. Bootstrap resampling (1000 iterations) was used for internal validation and a nomogram was drawn to predict medication compliance. The consistency index, Brier score, receiver operating characteristic curve and decision curve were used for model evaluation.Setting 35 Italian Community Psychiatric Services.Participants 451 patients prescribed with any long-acting intramuscular (LAI) antipsychotic were consecutively recruited, and assessed after 6 months and 12 months, from December 2015 to May 2017.Results 432 patients with psychotic disorders were included for model construction; among these, the compliance rate was 61.3%. The Drug Attitude Inventory-10 (DAI-10) and Brief Psychiatric Rating Scale (BPRS) scores, multiple hospitalisations in 1 year and a history of long-acting injectables were found to be independent risk factors for treatment noncompliance (all p