Association of socioeconomic status and DKA readmission in adults with type 1 diabetes: analysis of the US National Readmission Database.

Objective: To identify patient and hospital predictors of recurrent diabetic ketoacidosis (DKA) admissions in adults in the USA with type 1 diabetes, focusing on socioeconomic indicators. Research design and methods: This cross-sectional study used the National Readmission Database to identify adult patients with type 1 diabetes admitted for DKA between 2010 and 2015. The index DKA admission was defined as the first admission within the calendar year and the primary outcome was recurrent DKA admission(s) within the same calendar year. Multivariable logistic regression analysis was performed using covariates of patient and hospital factors at the index admission to determine the odds of DKA readmission(s). Results: Among 181 284 index DKA admissions, 39 693 (22%) had at least one readmission within the calendar year, of which 33 931 (86%) and 5762 (14%) had 1-3 and ≥4 DKA readmissions, respectively. When compared with the highest income quartile, patients in the first and second income quartiles had 46% (95% CI 30% to 64%) and 34% (95% CI 19% to 51%) higher odds of four or more DKA readmissions, respectively. Medicaid and Medicare insurance were both associated with a 3.3-fold adjusted risk (95% CI 3.0 to 3.7) for ≥4 readmissions compared with private insurance, respectively. Younger age, female sex, and discharge against medical advice were also predictive. Conclusions: Lower socioeconomic status and Medicaid insurance are strong predictors of DKA readmissions in adults with type 1 diabetes in the USA. Further studies are needed to understand the mediators of this association to inform multilevel interventions for this high-risk population. Significance of the study: The association of socioeconomic status (SES) and hospital admission for DKA has been studied in pediatrics with type 1 diabetes, but the data in adults are limited, and studies evaluating recurrent DKA admissions are scarcer. To our knowledge, this is the first study to describe predictors of recurrent DKA admissions in adults with type 1 diabetes on a national level in the USA. We found that those at highest risk of recurrent DKA are young women with low SES who had Medicaid or Medicare insurance. These findings should prompt further studies to explore the mediators of these disparities in patients with type 1 diabetes, as recurrent DKA results in high healthcare utilization and increased risk of long-term complications.

Retrospective study of inpatient diabetes management service, length of stay and 30-day readmission rate of patients with diabetes at a community hospital.

Background: Hospitalized patients with diabetes are at risk of complications and longer length of stay (LOS). Inpatient Diabetes Management Services (IDMS) are known to be beneficial; however, their impact on patient care measures in community, non-teaching hospitals, is unknown. Objectives: To evaluate whether co-managing patients with diabetes by the IDMS team reduces LOS and 30-day readmission rate (30DR). Methods: This retrospective quality improvement cohort study analyzed LOS and 30DR among patients with diabetes admitted to a community hospital. The IDMS medical team consisted of an endocrinologist, nurse practitioner, and diabetes educator. The comparison group consisted of hospitalized patients with diabetes under standard care of attending physicians (mostly internal medicine-trained hospitalists). The relationship between study groups and outcome variables was assessed using Generalized Estimating Equation models. Results: 4,654 patients with diabetes (70.8 ± 0.2 years old) were admitted between January 2016 and May 2017. The IDMS team co-managed 18.3% of patients, mostly with higher severity of illness scores (p < 0.0001). Mean LOS in patients co-managed by the IDMS team decreased by 27%. Median LOS decreased over time in the IDMS group (p = 0.046), while no significant decrease was seen in the comparison group. Mean 30DR in patients co-managed by the IDMS decreased by 10.71%. Median 30DR decreased among patients co-managed by the IDMS (p = 0.048). Conclusions: In a community hospital setting, LOS and 30DR significantly decreased in patients co-managed by a specialized diabetes team. These changes may be translated into considerable cost savings.

Development and validation of a prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults.

OBJECTIVE: To develop and validate a multivariable prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults. RESEARCH DESIGN AND METHODS: We collected pharmacologic, demographic, laboratory, and diagnostic data from 128 657 inpatient days in which at least 1 unit of subcutaneous insulin was administered in the absence of intravenous insulin, total parenteral nutrition, or insulin pump use (index days). These data were used to develop multivariable prediction models for biochemical and clinically significant hypoglycemia (blood glucose (BG) of ≤70 mg/dL and <54 mg/dL, respectively) occurring within 24 hours of the index day. Split-sample internal validation was performed, with 70% and 30% of index days used for model development and validation, respectively. RESULTS: Using predictors of age, weight, admitting service, insulin doses, mean BG, nadir BG, BG coefficient of variation (CVBG), diet status, type 1 diabetes, type 2 diabetes, acute kidney injury, chronic kidney disease (CKD), liver disease, and digestive disease, our model achieved a c-statistic of 0.77 (95% CI 0.75 to 0.78), positive likelihood ratio (+LR) of 3.5 (95% CI 3.4 to 3.6) and negative likelihood ratio (-LR) of 0.32 (95% CI 0.30 to 0.35) for prediction of biochemical hypoglycemia. Using predictors of sex, weight, insulin doses, mean BG, nadir BG, CVBG, diet status, type 1 diabetes, type 2 diabetes, CKD stage, and steroid use, our model achieved a c-statistic of 0.80 (95% CI 0.78 to 0.82), +LR of 3.8 (95% CI 3.7 to 4.0) and -LR of 0.2 (95% CI 0.2 to 0.3) for prediction of clinically significant hypoglycemia. CONCLUSIONS: Hospitalized patients at risk of insulin-associated hypoglycemia can be identified using validated prediction models, which may support the development of real-time preventive interventions.