Predictors of Five-Year Readmission to an Inpatient Service among Patients with Alcohol Use Disorders: Report from a Low-Middle Income Country.

BACKGROUND: Alcohol use disorder (AUD) is a relapsing-remitting disease that accounted for a sizable proportion of all-cause adult inpatient stays. OBJECTIVES: To determine the predictors of any and multiple readmissions to inpatient care for AUD within 5 years of the index admission. METHODS: This retrospective, register-based cohort study assessed consecutive patients with AUD admitted to a publicly-funded inpatient service between January 2007 and December 2014. Binary logistic regression was used to determine independent predictors for readmissions based on relevant demographic, clinical, and treatment variables that showed significant differences (p < 0.05) on univariate analysis. RESULTS: Among 938 patients (age 35.9 ± 10.3 years; duration of alcohol use 159.6 ± 104.5 months; dual diagnosis 19%; comorbidity of substance use disorder 49.3%; medical disorder 34.8%, 299 (31.9%) and 115 (12.3%) had any and multiple readmissions, respectively. Comorbid "severe mental illness" (Odds ratio [OR] 1.99, 95% confidence interval [CI] 1.11-3.57) and urban residence (OR 1.58, 95% CI 1.13-2.18) increased the odds of any readmission; "Improved" status at discharge (OR 0.51, 95% CI 0.35-0.72) during index hospitalization reduced odds of readmission. Additionally, any medical or psychiatric comorbidities increased (OR 2.23, 95% CI 1.26-3.97), and comorbid substance use disorder decreased (OR 0.41, 95% CI 0.19-0.89) risk of multiple readmissions. CONCLUSIONS: Clinicians could identify patients at-risk for any and multiple readmissions during the index hospitalization. A policy aimed at reducing the risk of rehospitalization, healthcare cost, and stigma should pay attention to the predictors of readmission. Such policy should further benefit resource-limited settings.

Predictors of Five-year Readmission to an Inpatient Service among Patients with Opioid Use Disorders.

Background Opioid use disorder (OUD), a relapsing-remitting chronic medical disease, accounts for a sizable proportion of all-cause adult inpatient stays. We evaluated the incidence and predictors of any and multiple readmissions to inpatient care for OUD. Methods This retrospective, register-based cohort study assessed consecutive patients with OUD admitted to a federally-funded inpatient service of an addiction treatment center in North India between January 2007 and December 2014. Binary logistic regression was used to determine independent readmission predictors based on demographic, clinical, and treatment variables that significantly differed in bivariate analysis. Results Among 908 patients, 306 (33.7%) and 106 (11.7%) had any and multiple readmissions, respectively. Injection drug use (Odds ratio [OR] 2.92, 95% confidence interval [CI] 1.90-4.49), comorbid severe mental illness (OR 2.80, 95% CI 1.42-5.55) and common mental disorder (OR 3.4 95% CI 1.65-6.95), antagonist treatment (OR 1.6 95% CI 1.14-2.27), and urban residence (OR 1.38 95% CI 1.01-1.90) increased odds of readmission. 'Improved' discharge status (OR 0.48 95% CI 0.34-0.70) in first admissions reduced odds of any readmission. Similar risk factors also influenced multiple readmissions with higher odds ratios. Conclusions Identification and adequate treatment of risk factors may reduce the chances of readmission.

Predictors of Treatment Completion in an Inpatient Substance Use Treatment Service in India.

INTRODUCTION: Treatment completion is associated with a better outcome in substance use disorders. We examined the rates of treatment completion and its predictors in patients admitted to specialized addiction treatment settings over a 13-year period. METHODS: Ours was a retrospective cohort study. We included consecutive 2850 patients admitted to the inpatient treatment between January 2007 and December 2019. We divided the patients into 2 groups: completed versus premature discontinuation of treatment. The predictor variables were based on previous research, clinical experience, and availability of the digital record. RESULTS: The number of patients who completed and discontinued treatments was 1873 (72.6%) and 707 (27.4%), respectively. The inpatient treatment discontinuation rate varied widely during the study period (18% in 2007 and 41% in 2012). The average rate of treatment discontinuation was 27%. The change-point analysis showed 5 statistically significant change points in the years 2008, 2010, 2012, 2014, and 2016. Patients who were prescribed medications for alcohol and opioid dependence and those who were on opioid agonist treatment had 4.7 and 6.3 higher odds of completing inpatient treatment than those who were not on medication. Patients with physical and psychiatric comorbidities had higher odds of treatment completion. Patients with a primary diagnosis of opioid dependence had lower odds of treatment completion than those with alcohol dependence. CONCLUSIONS: The rates of discontinuation may vary with concurrent changes in the treatment policies. Awareness of the risk factors and policy measures that may improve treatment completion must aid in informed decision making.