» Articles » PMID: 25537450

READMIT: a Clinical Risk Index to Predict 30-day Readmission After Discharge from Acute Psychiatric Units

Overview
Journal J Psychiatr Res
Specialty Psychiatry
Date 2014 Dec 25
PMID 25537450
Citations 42
Authors
Affiliations
Soon will be listed here.
Abstract

Our aim was to create a clinically useful risk index, administered prior to discharge, for determining the probability of psychiatric readmission within 30 days of hospital discharge for general psychiatric inpatients. We used population-level sociodemographic and health administrative data to develop a predictive model for 30-day readmission among adults discharged from an acute psychiatric unit in Ontario, Canada (2008-2011), and converted the final model into a risk index system. We derived the predictive model in one-half of the sample (n = 32,749) and validated it in the other half of the sample (n = 32,750). Variables independently associated with 30-day readmission (forming the mnemonic READMIT) were: (R) Repeat admissions; (E) Emergent admissions (i.e. harm to self/others); (D) Diagnoses (psychosis, bipolar and/or personality disorder), and unplanned Discharge; (M) Medical comorbidity; (I) prior service use Intensity; and (T) Time in hospital. Each 1-point increase in READMIT score (range 0-41) increased the odds of 30-day readmission by 11% (odds ratio 1.11, 95% CI 1.10-1.12). The index had moderate discriminative capacity in both derivation (C-statistic = 0.631) and validation (C-statistic = 0.630) datasets. Determining risk of psychiatric readmission for individual patients is a critical step in efforts to address the potentially avoidable high rate of this negative outcome. The READMIT index provides a framework for identifying patients at high risk of 30-day readmission prior to discharge, and for the development, evaluation and delivery of interventions that can assist with optimizing the transition to community care for patients following psychiatric discharge.

Citing Articles

Factors Associated with Hospital Readmission in a Population with a Diagnosis of Personality Disorder.

Carbonel-Aranda V, Gonzalez A, Garcia-Illanes Y, Traverso-Rodriguez M, Bordallo-Aragon A, Goodman-Casanova J Actas Esp Psiquiatr. 2024; 52(6):750-758.

PMID: 39665603 PMC: 11636539. DOI: 10.62641/aep.v52i6.1796.


Clinical Characteristics of Inpatients with Schizophrenia Spectrum Disorder Treated with Electroconvulsive Therapy: A Population-Level Cross-Sectional Study: Titre: Caractéristiques cliniques des patients hospitalisés présentant un trouble du....

Kaster T, Babujee A, Sharpe I, Rhee T, Gomes T, Kurdyak P Can J Psychiatry. 2024; 70(3):194-208.

PMID: 39529283 PMC: 11562932. DOI: 10.1177/07067437241290181.


The effect of legal representation on clinical measures in involuntarily admitted psychiatric patients: a retrospective study.

Cohen Y, Bendor A, Gilbar R, Cohen O, Khawaled R, Dienstag A Isr J Health Policy Res. 2024; 13(1):58.

PMID: 39363227 PMC: 11448244. DOI: 10.1186/s13584-024-00633-9.


A predictive model for readmission within 1-year post-discharge in patients with schizophrenia.

Hou M, Wu Y, Xue J, Chen Q, Zhang Y, Zhang R BMC Psychiatry. 2024; 24(1):573.

PMID: 39174919 PMC: 11340171. DOI: 10.1186/s12888-024-06024-3.


Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment.

Yoo D, Woo H, Nguyen V, Birnbaum M, Kruzan K, Kim J Proc SIGCHI Conf Hum Factor Comput Syst. 2024; 2024.

PMID: 38894725 PMC: 11184595. DOI: 10.1145/3613904.3642369.