Clinical risk model to predict 28-day unplanned readmission via the accident and emergency department after discharge from acute psychiatric units for patients with psychotic spectrum disorders

Abstract

Background Unplanned readmissions rates are an important indicator of the quality of care provided in a psychiatric unit. However, there is no validated risk model to predict this outcome in patients with psychotic spectrum disorders. Aims This paper aims to establish a clinical risk prediction model to predict 28-day unplanned readmission via the accident and emergency department after discharge from acute psychiatric units for patients with psychotic spectrum disorders. Method Adult patients with psychotic spectrum disorders discharged within a 5-year period from all psychiatric units in Hong Kong were included in this study. Information on the socioeconomic background, past medical and psychiatric history, current discharge episode and Health of the Nation Outcome Scales (HoNOS) scores were used in a logistic regression to derive the risk model and the predictive variables. The sample was randomly split into two to derive (n = 10 219) and validate (n = 10 643) the model. Results The rate of unplanned readmission was 7.09%. The risk factors for unplanned readmission include higher number of previous admissions, comorbid substance misuse, history of violence and a score of one or more in the discharge HoNOS overactivity or aggression item. Protective factors include older age, prescribing clozapine, living with family and relatives after discharge and imposition of conditional discharge. The model had moderate discriminative power with a c-statistic of 0.705 and 0.684 on the derivation and validation data-set. Conclusions The risk of readmission for each patient can be identified and adjustments in the treatment for those with a high risk may be implemented to prevent this undesirable outcome.

Development and Validation of a Nomogram for Predicting Risk of Emergency Department Revisits in Chinese Older Patients

“…Although previous evidence reached consensus that older adults encountered more ED returns when compared with younger adults, the increasing age, within the spectrum of the older population, has yet demonstrated contradictory results. Some studies coincided with our finding that the oldest old were less prone to re-visit, [33][34][35] whereas others concluded that the likelihood of returning to EDs increased concurrently with advancing age. 5,11,13,28 Such inconsistencies in the age effect highlight the importance of considering factors that can more accurately reflect biological or functional age (eg, frailty) rather than simply chronological age in the geriatric population evaluation.…”

Risk factors associated with 30‐day unplanned hospital readmission for patients with mental illness

“…Patients with a history of violence were also significantly associated with UHRs (OR = 1.30; 95% CI 1.09–1.55) (Hariman et al . 2020).…”

“…The items are ‘Overactive, aggressive, disruptive or agitated’ (Hariman et al . 2020), ‘Grandiosity’ and ‘Suspiciousness’ (Hamilton et al . 2015).…”

“…Patients were less likely to be readmitted when they were discharged to family/relatives or institutionalized (Hariman et al . 2020), especially in psychiatric nursing homes (Del Favero et al . 2020).…”

Current Trends in Readmission Prediction: An Overview of Approaches

“…Even with the emergence of the ML algorithm, 29 out of 36 articles adopted traditional statistical methods. Among these studies, ~ 90% used LR either as a baseline [ 56 , 58 , 60 , 62 – 64 , 68 , 73 , 74 , 76 – 78 , 83 , 85 – 87 ] or the main model in prediction [ 60 , 69 , 71 , 82 , 88 – 90 ], and 3 studies derived their own risk scores on the basis of LR variable coefficients [ 61 , 66 , 84 ]. In the remaining 3 papers, the prognosis of readmission was carried out with Cox regression survival analysis.…”