ArticleBMJ open2024
Nomogram model for predicting medication adherence in patients with various mental disorders based on the Dryad database.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Comparison of Three Adherence Assessment Tools in Omani Patients with Type 2 Diabetes Mellitus: A Single-Center Cross-Sectional Study of Agreement and Association with HbA1c.Patient preference and adherence · 2026Article
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- Development and validation of a high-performance clinical predictive model for early identification of non-alcoholic fatty liver disease.Frontiers in physiology · 2026Article
- Machine learning approaches to predicting medication nonadherence: a scoping review.International journal of medical informatics · 2025Article
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5 authors.
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Abstract
objectiveTreatment 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.
designClinical 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.
setting35 Italian Community Psychiatric Services.
participants451 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.
results432 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<0.01). The concordance statistic of the nomogram was 0.709 (95% CI 0.652 to 0.766), the Brier index was 0.215 and the area under the ROC curve was 0.716 (95% CI 0.669 to 0.763); decision curve analysis showed that applying this model between the threshold probabilities of 44% and 63% improved the net clinical benefit.
conclusionA low DAI-10 score, a high BPRS score, multiple hospitalisations in 1 year and the previous use of long-acting injectable drugs were independent risk factors for medication noncompliance in patients with psychotic disorders. Our nomogram for predicting treatment adherence behaviour in psychiatric patients exhibited good sensitivity and specificity.
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