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 ⩾1 (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.