High Risk, High Dose?—Pharmacotherapeutic Prescription Patterns of Offender and Non-Offender Patients with Schizophrenia Spectrum Disorder

Abstract

Compared to acute or community settings, forensic psychiatric settings, in general, have been reported to make greater use of antipsychotic polypharmacy and/or high dose pharmacotherapy, including overdosing. However, there is a scarcity of research specifically on offender patients with schizophrenia spectrum disorders (SSD), although they make up a large proportion of forensic psychiatric patients. Our study, therefore, aimed at evaluating prescription patterns in offender patients compared to non-offender patients with SSD. After initial statistical analysis with null-hypothesis significance testing, we evaluated the interplay of the significant variables and ranked them in accordance with their predictive power through application of supervised machine learning algorithms. While offender patients received higher doses of antipsychotics, non-offender patients were more likely to receive polypharmacologic treatment as well as additional antidepressants and benzodiazepines. To the authors’ knowledge, this is the first study to evaluate a homogenous group of offender patients with SSD in comparison to non-offender controls regarding patterns of antipsychotic and other psychopharmacologic prescription patterns.

Suicidal Offenders and Non-Offenders with Schizophrenia Spectrum Disorders: A Retrospective Evaluation of Distinguishing Factors Using Machine Learning

“…They were more likely to have a prescription for antidepressants during the referenced hospitalisation. This observation is not only found when comparing merely the patients with a suicidal history: a higher rate of antidepressant pharmacotherapy in NOP with SSD than OP with SSD has been observed in general [33]. While an adjunctive antidepressant can be beneficial to patients with SSD, especially regarding negative symptoms, it seems to be a less common prescription in OP, possibly due to fear of re-exacerbation of positive psychotic symptoms and an increased risk of side effects [34,35].…”

Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review

“…Among these studies, four employed ML models to predict VB during the current admission ( 35 , 41 , 47 , 51 ). Additionally, nine studies categorized patients based on the occurrence of VB prior to their current admission ( 38 – 40 , 43 , 44 , 46 , 48 – 50 ), while another four classified patients into violent and non-violent groups by retrospectively reviewing their medical records since their disease onset ( 36 , 37 , 42 , 45 ). Moreover, eight studies were part of a larger project investigating the relationship between SSD and offending and used the same dataset of offender patients as their sample population ( 39 , 41 , 42 , 45 , 46 , 48 – 50 ).…”

“…(2022, 2023) in two separate studies evaluated the differences between offender and non-offender SSD patients regarding psychiatric pr e scription patterns and illness-related factors ( 46 , 49 ). Also, ten studies analyzed the relationship between different rating tools scores and VB in patients with SSD ( 36 , 38 , 39 , 41 , 43 , 45 , 46 , 48 – 50 ), including the Brief Psychiatric Rating Scale (BPRS) ( 38 , 43 , 52 ), the Psychopathy Checklist: Screening Version (PCL-SV), the Historical, Clinical and Risk management (HCR-20) scale ( 38 , 53 ), The Barratt Impulsiveness Scale version 11 (BIS-11) ( 38 , 54 ), the Positive And Negative Symptom Scale (PANSS) ( 36 , 39 , 41 , 43 , 45 , 46 , 48 – 50 , 55 ), the Social Disability Screening Schedule (SDSS) ( 43 ), Insight and Treatment Attitude Questionnaire (ITAQ) ( 47 , 56 ), Family Adaptation, Partnership, Growth, Affection and Resolve (APGAR) ( 47 , 57 ), Social Support Rating Scale (SSRS) ( 47 , 58 ), and Family Burden Scale of Disease (FBS) ( 47 , 59 ). Furthermore, two studies evaluated neuroimaging data of patients as VB predictors, along with sociodemographic features.…”

“…(2022) included physical VB regardless of the aim ( 41 ). On the other hand, six studies used a shared database to distinguish between violent and non-violent offenses ( 39 , 42 , 46 , 48 – 50 ). In a seventh study, they attempted to predict the risk of homicide among other offenses ( 45 ).…”

“…Seven studies employed data-driven feature selection by ML before model training to control overfitting: one utilized LASSO ( 38 ), three used RF ( 39 , 41 , 45 ), one applied boosted tree ( 42 ), one utilized both LASSO and LR ( 43 ), and one selected features after calculation of variable importance for each employed model separately ( 51 ). Sixteen studies used cross-validation, with two using 10-fold cross-validation ( 43 , 44 ), one using 7-fold ( 36 ), nine using 5-fold ( 37 , 39 , 41 , 42 , 45 , 46 , 48 – 50 ), one using 4-fold ( 47 ), and one using 3-fold ( 34 ). Two studies did not use cross-validation ( 35 , 40 ).…”