Abstract
BACKGROUND: Off-label antipsychotic use in people with intellectual disabilities and challenging behaviour is high. Antipsychotic withdrawal is recommended, but attempts are often unsuccessful. This study aimed to develop and internally validate a prediction model that provides insight into predicting factors for unsuccessful (i.e. incomplete) off-label antipsychotic withdrawal attempts in people with intellectual disabilities. METHODS: Data collected in two previous studies examining the withdrawal of off-label antipsychotics in people with intellectual disabilities and challenging behaviour living mostly in 24/7 care settings (98.6%) in the Netherlands were analysed. The dataset included 141 participants (64.5% male, median age 52). We selected candidate predictors (age, level of intellectual disability, defined daily dose, autism spectrum disorder and three subscales of the Aberrant Behavior Checklist [ABC], namely stereotypy, hyperactivity and lethargy) based on previous research and clinical relevance. A multivariable logistic regression analysis with backward selection procedures was conducted to identify significant predictors. The model was internally validated using bootstrapping procedures. RESULTS: The analysis revealed the level of intellectual disability (p = 0.030, OR = 2.374), defined daily dose (p = 0.063, OR = 2.833), and ABC stereotypy (p = 0.007, OR = 1.106) as key predictors for unsuccessful withdrawals. The variables explained 20% of the variance (Nagelkerke's R-square, R(2) = 0.200). The model calibrated well as the Hosmer and Lemeshow test was not significant. The discrimination of the model was fair to good; the Area Under the Curve (AUC) was 0.728. Internal validation procedures showed an optimism-corrected AUC of 0.706; the optimism-corrected Nagelkerke's R(2) was 0.157. CONCLUSIONS: The odds of unsuccessful withdrawal increase with a more severe level of intellectual disability, a higher antipsychotic defined daily dose and higher stereotypy scores. The results inform healthcare providers about the predictive factors enabling them to better anticipate and support future withdrawal attempts.