Modifiable risk factors for inpatient violence in psychiatric hospital: Prospective study and prediction model
Fazel S., Toynbee M., Ryland H., Vazquez-Montes M., Al-Taiar H., Wolf A., Aziz O., Khosla V., Gulati G., Fanshawe T.
Background Violence perpetrated by psychiatric inpatients is associated with modifiable factors. Current structured approaches to assess inpatient violence risk lack predictive validity and linkage to interventions. Methods Adult psychiatric inpatients on forensic and general wards in three psychiatric hospitals were recruited and followed up prospectively for 6 months. Information on modifiable (dynamic) risk factors were collected every 1-4 weeks, and baseline background factors. Data were transferred to a web-based monitoring system (FOxWeb) to calculate a total dynamic risk score. Outcomes were extracted from an incident-reporting system recording aggression and interpersonal violence. The association between total dynamic score and violent incidents was assessed by multilevel logistic regression and compared with dynamic score excluded. Results We recruited 89 patients and conducted 624 separate assessments (median 5/patient). Mean age was 39 (s.d. 12.5) years with 20% (n = 18) female. Common diagnoses were schizophrenia-spectrum disorders (70%, n = 62) and personality disorders (20%, n = 18). There were 93 violent incidents. Factors contributing to violence risk were a total dynamic score of 3/41 (OR 3.39, 95% CI 1.25-9.20), 10-year increase in age (OR 0.67, 0.47-0.96), and female sex (OR 2.78, 1.04-7.40). Non-significant associations with schizophrenia-spectrum disorder were found (OR 0.50, 0.20-1.21). In a fixed-effect model using all covariates, AUC was 0.77 (0.72-0.82) and 0.75 (0.70-0.80) when the dynamic score was excluded. Conclusions In predicting violence risk in individuals with psychiatric disorders, modifiable factors added little incremental value beyond static ones in a psychiatric inpatient setting. Future work should make a clear distinction between risk factors that assist in prediction and those linked to needs.