Predictors of readmission and mortality in adults with diabetes or stress hyperglycemia after initial hospitalization for COVID-19.

INTRODUCTION: We previously reported predictors of mortality in 1786 adults with diabetes or stress hyperglycemia (glucose>180 mg/dL twice in 24 hours) admitted with COVID-19 from March 2020 to February 2021 to five university hospitals. Here, we examine predictors of readmission. RESEARCH DESIGN AND METHODS: Data were collected locally through retrospective reviews of electronic medical records from 1786 adults with diabetes or stress hyperglycemia who had a hemoglobin A1c (HbA1c) test on initial admission with COVID-19 infection or within 3 months prior to initial admission. Data were entered into a Research Electronic Data Capture (REDCap) web-based repository, and de-identified. Descriptive data are shown as mean±SD, per cent (%) or median (IQR). Student's t-test was used for comparing continuous variables with normal distribution and Mann-Whitney U test was used for data not normally distributed. X2 test was used for categorical variable. RESULTS: Of 1502 patients who were alive after initial hospitalization, 19.4% were readmitted; 90.3% within 30 days (median (IQR) 4 (0-14) days). Older age, lower estimated glomerular filtration rate (eGFR), comorbidities, intensive care unit (ICU) admission, mechanical ventilation, diabetic ketoacidosis (DKA), and longer length of stay (LOS) during the initial hospitalization were associated with readmission. Higher HbA1c, glycemic gap, or body mass index (BMI) were not associated with readmission. Mortality during readmission was 8.0% (n=23). Those who died were older than those who survived (74.9±9.5 vs 65.2±14.4 years, p=0.002) and more likely had DKA during the first hospitalization (p<0.001). Shorter LOS during the initial admission was associated with ICU stay during readmission, suggesting that a subset of patients may have been initially discharged prematurely. CONCLUSIONS: Understanding predictors of readmission after initial hospitalization for COVID-19, including older age, lower eGFR, comorbidities, ICU admission, mechanical ventilation, statin use and DKA but not HbA1c, glycemic gap or BMI, can help guide treatment approaches and future research in adults with diabetes.

Impact of Glucose-Lowering Medications on Health-Related Quality of Life in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE).

OBJECTIVE: Diabetes is associated with reduced health-related quality of life (HRQoL). Information on the relationship between HRQoL and glucose-lowering medications in recently diagnosed type 2 diabetes (T2D) is limited. We assessed changes in HRQoL in participants with T2D receiving metformin plus one of four glucose-lowering medications in Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE). RESEARCH DESIGN AND METHODS: A total of 5,047 participants, baseline mean age 57 years, with <10 years T2D duration and glycated hemoglobin level 6.8-8.5% and taking metformin monotherapy, were randomly assigned to glargine, glimepiride, liraglutide, or sitagliptin. HRQoL was evaluated at baseline for 4,885 participants, and at years 1, 2, and 3, with use of the self-administered version of the Quality of Well-being Scale (QWB-SA) and SF-36 physical (PCS) and mental (MCS) component summary scales. Linear models were used to analyze changes in HRQoL over time in intention-to-treat analyses. RESULTS: None of the medications worsened HRQoL. There were no differences in QWB-SA or MCS by treatment group at any time point. PCS scores improved with liraglutide versus other groups at year 1 only. Greater weight loss during year 1 explained half the improvement in PCS scores with liraglutide versus glargine and glimepiride. Liraglutide participants in the upper tertile of baseline BMI showed the greatest improvement in PCS scores at year 1. CONCLUSIONS: Adding liraglutide to metformin in participants within 10 years of T2D diagnosis showed improvement in the SF-36 PCS in comparisons with the other medications at 1 year, which was no longer significant at years 2 and 3. Improvement was related to weight loss and baseline BMI.

Predictors of lack of glycemic control in persons with type 2 diabetes.

BACKGROUND: Professional guidelines recommend an HbA1c < 7% for most people with diabetes and < 8.5% for those with relaxed glycemic goals. However, many people with type 2 diabetes mellitus (T2DM) are unable to achieve the desired HbA1c goal. This study evaluated factors associated with lack of improvement in HbA1c over 3 years. METHODS: All patients with T2DM treated within a major academic healthcare system during 2015-2020, who had at least one HbA1c value > 8.5% within 3 years from their last HbA1c were included in analysis. Patients were grouped as improved glycemic control (last HbA1c ≤ 8.5%) or lack of improvement (last HbA1c > 8.5%). Multivariate logistic regression analysis was performed to assess independent predictors of lack of improvement in glycemic control. RESULTS: Out of 2,232 patients who met the inclusion criteria, 1,383 had an improvement in HbA1c while 849 did not. In the fully adjusted model, independent predictors of lack of improvement included: younger age (odds ratio, 0.89 per 1-SD [12 years]; 95% CI, 0.79-1.00), female gender (1.30, 1.08-1.56), presence of hypertension (1.29, 1.08-1.55), belonging to Black race (1.32, 1.04-1.68, White as reference), living in low income area (1.86,1.28-2.68, high income area as reference), and insurance coverage other than Medicare (1.32, 1.05-1.66). Presence of current smoking was associated with a paradoxical improvement in HbA1c (0.69, 0.47-0.99). In a subgroup analysis, comparing those with all subsequent HbA1c values > 8.5% (N = 444) to those with all subsequent HbA1c values < 8.5% (N = 341), similar factors were associated with lack of improvement, but smoking was no longer significant. CONCLUSION: We conclude that socioeconomic factors like race, type of insurance coverage and living in low-income areas are associated with lack of improvement in HbA1c over a period of 3-years in people with T2DM. Intervention strategies focused on low-income neighborhoods need to be designed to improve diabetes management.

Medicaid insured persons with diabetes have increased proportion of missed appointments and high HbA1c.

Objective: This study was conducted to evaluate whether the type of insurance coverage is associated with missed appointments and to evaluate the effect of missed appointments on diabetes control. Methods: All patients with diabetes mellitus (DM) managed at a major academic medical center between Jan 2015 and Dec 2020 were included in analysis. Association between insurance coverage and the proportion of missed appointments was evaluated with adjustments for demographic variables and social determinants of health. The relationship between proportion of missed appointments and glycemic control was also evaluated. Results: The dataset included 30,633 patients, out of which 14,064 (46%) reported commercial insurance, 13,376 (44%) reported Medicare and 3,193 (10%) reported Medicaid coverage. Proportion of missed appointments was 18.1 ± 18.1% among Medicaid covered patients,12.1 ± 15.3% among commercially insured and 10.2 ± 14.1% among Medicare covered patients (p < 0.001). Type of insurance was found to be a significant predictor of proportion of missed appointments after adjusting for age, race, language, marital status, smoking, BMI, HbA1c and type of diabetes (p < 0.001) in series regression analysis. Proportion of missed appointments was associated with HbA1c with partial correlation coefficient +0.104 (p < 0.005) after adjusting for age, race, gender, type of insurance coverage, BMI and type of diabetes. Conclusions: Medicaid covered patients with diabetes have higher proportion of missed clinic appointments and higher HbA1c. More research is needed to evaluate the root causes of inability to keep appointments in this population so that strategies for improved healthcare delivery can be designed.