[Individual and contextual factors associated with violent behaviours during psychiatric hospitalizations]

Outcomes among hospitalized patients with dementia and behavioral disturbances when physical restraints are introduced

BACKGROUND

Physical restraint use among patients hospitalized with dementia and behavioral disturbances has not been studied nationally in the United States.

METHODS

National Inpatient Sample database years 2016 through 2020 were used to compare physically restrained and unrestrained patients with dementia and behavioral disturbances. Multivariable regression analyses were used to assess patient outcomes.

RESULTS

There were 991,605 patients coded for dementia with behavioral disturbances. Among these, physical restraints were used with 64,390 (6.5%) and not with 927,215 (93.5%). Patients in the restrained group were younger (mean age ± standard error: 78.7 ± 0.25 vs. 79.9 ± 0.34 ; p < 0.01) and more often male (59.0% vs. 45.8%; p < 0.01) compared to the unrestrained group. A higher proportion of Black patients were in the restrained group (15.2% vs. 11.8%; p < 0.01). Larger hospitals also made up a more significant proportion of restrained versus unrestrained patients (53.3% vs. 45.1%; p < 0.01). Those with physical restraints had longer lengths of stays (adjusted mean difference [aMD] = 2.6 days CI [2.2-3.0]; p < 0.01) and higher total hospital charges (aMD = $13,150 CI [10,827-15,472]; p < 0.01). There were similar adjusted odds for in-hospital mortality (adjusted odds ratio [aOR] = 1.0 [CI 0.95-1.1]; p = 0.28) and lower odds of being discharged to home after hospitalization (aOR = 0.74 [0.70-0.79]; <0.01) for patients with physical restraints compared to those without.

CONCLUSION

Among patients hospitalized with dementia and behavioral disturbances, those with physical restraints had greater hospital resource utilization outcomes. Attempts to limit physical restraint use whenever possible may improve outcomes in this vulnerable population.

Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD

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