The Glycemia Risk Index Predicts Performance of Diabetes Self-Management Habits in Youth With Type 1 Diabetes Mellitus.

BACKGROUND: The Glycemia Risk Index (GRI) was developed in adults with diabetes and is a validated metric of quality of glycemia. Little is known about the relationship between GRI and type 1 diabetes (T1D) self-management habits, a validated assessment of youths' engagement in habits associated with glycemic outcomes. METHOD: We retrospectively examined the relationship between GRI and T1D self-management habits in youth with T1D who received care from a Midwest pediatric diabetes clinic network. The GRI was calculated using seven days of continuous glucose monitor (CGM) data, and T1D self-management habits were assessed ±seven days from the GRI score. A mixed-effects Poisson regression model was used to evaluate the total number of habits youth engaged in with GRI, glycated hemoglobin A1c (HbA1c), age, race, ethnicity, and insurance type as fixed effects and participant ID as a random effect to account for multiple clinic visits per individual. RESULTS: The cohort included 1182 youth aged 2.5 to 18.0 years (mean = 13.8, SD = 3.5) comprising 50.8% male, 84.6% non-Hispanic White, and 64.8% commercial insurance users across a total of 6029 clinic visits. Glycemia Risk Index scores decreased as total number of habits performed increased, suggesting youth who performed more self-management habits achieved a higher quality of glycemia. CONCLUSIONS: In youth using CGMs, GRI may serve as an easily obtainable metric to help identify youth with above target glycemia, and engagement/disengagement in the T1D self-management habits may inform clinicians with suitable interventions for improving glycemic outcomes.

Recognizing Complications in Youth With Diabetes Admitted With Diabetic Ketoacidosis Versus Hyperglycemic Hyperosmolar State.

INTRODUCTION: We compare in-hospital complications in youth with isolated diabetic ketoacidosis (DKA) to youth with hyperosmolarity. METHOD: We reviewed medical records of youth (1-20 years) admitted over two years with DKA, hyperglycemic hyperosmolar state (HHS), and hyperosmolar DKA. We evaluated outcomes, including hospital length of stay, altered mental status (AMS), and acute kidney injury (AKI). RESULTS: Of 369 admissions, 334 had isolated DKA, 32 had hyperosmolar DKA, and three had isolated HHS. Hyperosmolar youth had longer length of stay, larger initial fluid boluses, more frequent pediatric intensive care unit admissions, and increased risk of AKI and AMS. The odds of AKI were positively associated with serum osmolality and negatively associated with new-onset diabetes mellitus (DM) compared with established DM. CONCLUSIONS: In youth with DM, hyperosmolarity increases acute complications compared with isolated DKA. Larger-scale studies are needed to identify ways to prevent acute complications in youth experiencing hyperglycemic emergencies.

Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes.

BACKGROUND: The glycemia risk index (GRI) is a composite metric developed and used to estimate quality of glycemia in adults with diabetes who use continuous glucose monitor (CGM) devices. In a cohort of youth with type 1 diabetes (T1D), we examined the utility of the GRI for evaluating quality of glycemia between clinic visits by analyzing correlations between the GRI and longitudinal glycated hemoglobin A1c (HbA1c) measures. METHOD: Using electronic health records and CGM data, we conducted a retrospective cohort study to analyze the relationship between the GRI and longitudinal HbA1c measures in youth (T1D duration ≥1 year; ≥50% CGM wear time) receiving care from a Midwest pediatric diabetes clinic network (March 2016 to May 2022). Furthermore, we analyzed correlations between HbA1c and the GRI high and low components, which reflect time spent with high/very high and low/very low glucose, respectively. RESULTS: In this cohort of 719 youth (aged = 2.5-18.0 years [median = 13.4; interquartile range [IQR] = 5.2]; 50.5% male; 83.7% non-Hispanic White; 68.0% commercial insurance), baseline GRI scores positively correlated with HbA1c measures at baseline and 3, 6, 9, and 12 months later (r = 0.68, 0.65, 0.60, 0.57, and 0.52, respectively). At all time points, strong positive correlations existed between HbA1c and time spent in hyperglycemia. Substantially weaker, negative correlations existed between HbA1c and time spent in hypoglycemia. CONCLUSIONS: In youth with T1D, the GRI may be useful for evaluating quality of glycemia between scheduled clinic visits. Additional CGM-derived metrics are needed to quantify risk for hypoglycemia in this population.

Liver injury on admission linked to worse outcomes in COVID-19: an analysis of 14,138 patients.

Background: Infection with the SARS-CoV-2 virus, which can result in hepatic inflammation and injury that varies from mild to severe and potentially acute fulminant liver injury, may be associated with poor outcomes. Our aims were to: (I) assess baseline clinical and demographic characteristics in patients with coronavirus disease 2019 (COVID-19) who did and did not have abnormalities in liver chemistries [alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total bilirubin (Tbili)] and (II) evaluate associations between abnormalities in liver chemistries and the primary outcomes of in-hospital death, intubation, and hospital length of stay (LOS). Methods: In this nationwide retrospective cohort study of 14,138 patients, we analyzed associations between abnormalities in liver chemistries (ALT, AST, ALP, and Tbili) and mortality, intubation, and prolonged hospital LOS in patients with laboratory-confirmed COVID-19. We used Pearson's chi-squared tests to detect significant differences in categorical variables for patients with and without abnormal liver chemistries. Welch's two-sample t-tests were used to make comparisons of liver chemistry (ALT, AST, ALP, Tbili) and serum albumin results. All other continuous variables were analyzed using independent samples t-tests. A P value of <0.05 was considered significant. Results: Propensity score matching demonstrated that abnormalities in liver chemistries at admission are significantly associated with increased risk for mortality (RR 1.70) and intubation (RR 1.44) in patients with COVID-19. Elevated AST is the liver chemistry abnormality associated with the highest risk for mortality (RR 2.27), intubation (RR 2.12), and prolonged hospitalization (RR 1.19). Male gender, pre-existing liver disease, and decreased serum albumin are also significantly associated with severe outcomes and death in COVID-19. Conclusions: Routine liver chemistry testing should be implemented and used for risk stratification at the time of COVID-19 diagnosis.

Diabetes status and other factors as correlates of risk for thrombotic and thromboembolic events during SARS-CoV-2 infection: A nationwide retrospective case-control study using Cerner Real-World Data™.

OBJECTIVES: We sought to examine in individuals with SARS-CoV-2 infection whether risk for thrombotic and thromboembolic events (TTE) is modified by presence of a diabetes diagnosis. Furthermore, we analysed whether differential risk for TTEs exists in type 1 diabetes mellitus (T1DM) versus type 2 diabetes mellitus (T2DM). DESIGN: Retrospective case-control study. SETTING: The December 2020 version of the Cerner Real-World Data COVID-19 database is a deidentified, nationwide database containing electronic medical record (EMR) data from 87 US-based health systems. PARTICIPANTS: We analysed EMR data for 322 482 patients >17 years old with suspected or confirmed SARS-CoV-2 infection who received care between December 2019 and mid-September 2020. Of these, 2750 had T1DM; 57 811 had T2DM; and 261 921 did not have diabetes. OUTCOME: TTE, defined as presence of a diagnosis code for myocardial infarction, thrombotic stroke, pulmonary embolism, deep vein thrombosis or other TTE. RESULTS: Odds of TTE were substantially higher in patients with T1DM (adjusted OR (AOR) 2.23 (1.93-2.59)) and T2DM (AOR 1.52 (1.46-1.58)) versus no diabetes. Among patients with diabetes, odds of TTE were lower in T2DM versus T1DM (AOR 0.84 (0.72-0.98)). CONCLUSIONS: Risk of TTE during COVID-19 illness is substantially higher in patients with diabetes. Further, risk for TTEs is higher in those with T1DM versus T2DM. Confirmation of increased diabetes-associated clotting risk in future studies may warrant incorporation of diabetes status into SARS-CoV-2 infection treatment algorithms.

An "All-Data-on-Hand" Deep Learning Model to Predict Hospitalization for Diabetic Ketoacidosis in Youth With Type 1 Diabetes: Development and Validation Study.

BACKGROUND: Although prior research has identified multiple risk factors for diabetic ketoacidosis (DKA), clinicians continue to lack clinic-ready models to predict dangerous and costly episodes of DKA. We asked whether we could apply deep learning, specifically the use of a long short-term memory (LSTM) model, to accurately predict the 180-day risk of DKA-related hospitalization for youth with type 1 diabetes (T1D). OBJECTIVE: We aimed to describe the development of an LSTM model to predict the 180-day risk of DKA-related hospitalization for youth with T1D. METHODS: We used 17 consecutive calendar quarters of clinical data (January 10, 2016, to March 18, 2020) for 1745 youths aged 8 to 18 years with T1D from a pediatric diabetes clinic network in the Midwestern United States. The input data included demographics, discrete clinical observations (laboratory results, vital signs, anthropometric measures, diagnosis, and procedure codes), medications, visit counts by type of encounter, number of historic DKA episodes, number of days since last DKA admission, patient-reported outcomes (answers to clinic intake questions), and data features derived from diabetes- and nondiabetes-related clinical notes via natural language processing. We trained the model using input data from quarters 1 to 7 (n=1377), validated it using input from quarters 3 to 9 in a partial out-of-sample (OOS-P; n=1505) cohort, and further validated it in a full out-of-sample (OOS-F; n=354) cohort with input from quarters 10 to 15. RESULTS: DKA admissions occurred at a rate of 5% per 180-days in both out-of-sample cohorts. In the OOS-P and OOS-F cohorts, the median age was 13.7 (IQR 11.3-15.8) years and 13.1 (IQR 10.7-15.5) years; median glycated hemoglobin levels at enrollment were 8.6% (IQR 7.6%-9.8%) and 8.1% (IQR 6.9%-9.5%); recall was 33% (26/80) and 50% (9/18) for the top-ranked 5% of youth with T1D; and 14.15% (213/1505) and 12.7% (45/354) had prior DKA admissions (after the T1D diagnosis), respectively. For lists rank ordered by the probability of hospitalization, precision increased from 33% to 56% to 100% for positions 1 to 80, 1 to 25, and 1 to 10 in the OOS-P cohort and from 50% to 60% to 80% for positions 1 to 18, 1 to 10, and 1 to 5 in the OOS-F cohort, respectively. CONCLUSIONS: The proposed LSTM model for predicting 180-day DKA-related hospitalization was valid in this sample. Future research should evaluate model validity in multiple populations and settings to account for health inequities that may be present in different segments of the population (eg, racially or socioeconomically diverse cohorts). Rank ordering youth by probability of DKA-related hospitalization will allow clinics to identify the most at-risk youth. The clinical implication of this is that clinics may then create and evaluate novel preventive interventions based on available resources.

Impact of diabetes status and related factors on COVID-19-associated hospitalization: A nationwide retrospective cohort study of 116,370 adults with SARS-CoV-2 infection.

AIMS: We examined diabetes status (no diabetes; type 1 diabetes [T1D]; type 2 diabetes [T2D]) and other demographic and clinical factors as correlates of coronavirus disease 2019 (COVID-19)-related hospitalization. Further, we evaluated predictors of COVID-19-related hospitalization in T1D and T2D. METHODS: We analyzed electronic health record data from the de-identified COVID-19 database (December 2019 through mid-September 2020; 87 US health systems). Logistic mixed models were used to examine predictors of hospitalization at index encounters associated with confirmed SARS-CoV-2 infection. RESULTS: In 116,370 adults (>=18 years old) with COVID-19 (93,098 no diabetes; 802 T1D; 22,470 T2D), factors that independently increased risk for hospitalization included diabetes, male sex, public health insurance, decreased body mass index (BMI; <25.0-29.9 kg/m2), increased BMI (>25.0-29.9 kg/m2), vitamin D deficiency/insufficiency, and Elixhauser comorbidity score. After further adjustment for concurrent hyperglycemia and acidosis in those with diabetes, hospitalization risk was substantially higher in T1D than T2D and in those with low vitamin D and elevated hemoglobin A1c (HbA1c). CONCLUSIONS: The higher hospitalization risk in T1D versus T2D warrants further investigation. Modifiable risk factors such as vitamin D deficiency/insufficiency, BMI, and elevated HbA1c may serve as prognostic indicators for COVID-19-related hospitalization in adults with diabetes.

Lay Advisor Interventions in Rural Populations: A Systematic Review and Meta-analysis.

CONTEXT: Age-adjusted death rates for heart disease are higher in rural areas than in urban areas. Lay advisors could potentially facilitate improvement in cardiovascular health outcomes. The aim of this systematic review and meta-analysis is to estimate lay advisor intervention effects on cardiovascular health metrics in rural populations. EVIDENCE ACQUISITION: Searches of databases including MEDLINE, CINAHL, and Scopus from 1975 through October 2017 retrieved 323 citations, of which 272 abstracts were reviewed. Two authors independently abstracted data from eligible studies. Analysis was conducted in March 2018. EVIDENCE SYNTHESIS: Of 21 articles included in the systematic review, eight were RCTs and 13 were pre- and post-intervention studies. Of the RCTs, three took place in the U.S. Only two studies had low risk of bias. Using a random effects model, meta-analysis of six RCTs (1,641 participants) showed that lay advisor interventions in rural residents were associated with improvement in HbA1c of 0.4% (95% CI=0.13, 0.66, p=0.004, I2=60.65%). From four RCTs (873 participants), lay advisor interventions significantly improved BMI with pooled effect of 2.18 (95% CI=1.13, 3.24, p<0.001, I2=0.00%). Most studies had normal baseline blood pressure and cholesterol levels before intervention, and no significant effects were noted for these outcomes. Diverse types of measures used for diet, physical activity, and smoking precluded statistical synthesis. CONCLUSIONS: Lay advisor interventions had significant positive effects on glycemic control and BMI for rural residents; however, further rigorous studies are needed in U.S. rural populations, and elements of effective lay advisor interventions require further investigation.