Using Routine Hemoglobin A1c Testing to Determine the Glycemic Status in Psychiatric Inpatients

AIM: Using routine hemoglobin A1c (HbA1c) testing to describe the prevalence, characteristics, and length of stay (LOS) of psychiatry inpatients with type 2 diabetes compared to those with pre-diabetes and those without diabetes. METHODS: In this prospective observational study, all inpatients aged greater than 30 years admitted to the Austin Health Psychiatry Unit, a major tertiary hospital, affiliated with the University of Melbourne, between February 2014 and April 2015 had routine HbA1c testing as part of the Diabetes Discovery Initiative. Patients were divided into three groups: diabetes (HbA1c ≥ 6.5%, 48 mmol/mol), pre-diabetes (HbA1c 5.7-6.4%, 39-46 mmol/mol), or no diabetes (HbA1c ≤ 5.6%, 38 mmol/mol). Baseline characteristics, co-morbidities, psychiatric illnesses, and treatment were recorded. RESULTS: There were a total of 335 psychiatry inpatients (median age 41 years). The most prevalent diagnoses were schizophrenia, depression, and substance abuse. Of the 335 psychiatric inpatients, 14% (n = 46) had diabetes and 19% (n = 63) had pre-diabetes, a prevalence threefold greater than in the aged matched general population. Compared to inpatients with pre-diabetes and no diabetes, those with diabetes were older and were at least twice as likely to have hypertension, obesity, and hyperlipidemia (all p ≤ 0.002). In multivariable analyses, diabetes was associated with increasing age (p = 0.02), substance abuse (p = 0.04), dyslipidaemia (p = 0.03), and aripiprazole use (p = 0.01). Patients with diabetes also had a 70% longer expected LOS (95% CI: 20-130%; p = 0.001), compared to those with pre-diabetes and no diabetes. CONCLUSION: Despite relative youth, one-third of all psychiatric inpatients above the age of 30 have diabetes or pre-diabetes. Presence of diabetes in psychiatric inpatients is associated with older age, substance abuse, and longer LOS. Routine inpatient HbA1c testing provides an opportunity for early detection and optimization of diabetes care