Disparities in Shared Decision-Making Research and Practice: The Case for Black American Patients.

OBJECTIVE: The extent of shared decision making (SDM) use in the care of Black patients is limited. We explored preferences, needs, and challenges of Black patients to enhance SDM offerings. METHODS: We performed interviews with 32 Black patients receiving type 2 diabetes care in safety-net primary care practices caring predominantly for Black people. RESULTS: The following 4 themes emerged: preference for humanistic communication, need to account for the role of family in decision making, need for medical information sharing, and mistrust of clinicians. CONCLUSION: Given the dearth of research on SDM among ethnic and racial minorities, this study offers patient-perspective recommendations to improve SDM offerings for Black patients in primary care settings. To enhance SDM with Black patients, acknowledgment of the importance of storytelling as a strategy, to place medical information in a context that makes it meaningful and memorable, is recommended. Triadic SDM, in which family members are centrally involved in decision making, is preferred over classical dyadic SDM. There is a need to reconsider the universalism assumption underlying contemporary SDM models and the relevancy of current SDM practices that were developed mostly without the feedback of participants of ethnic, racial, and cultural minorities.Annals "Online First" article.

Predicting and Preventing Acute Care Re-Utilization by Patients with Diabetes.

PURPOSE OF REVIEW: Acute care re-utilization, i.e., hospital readmission and post-discharge Emergency Department (ED) use, is a significant driver of healthcare costs and a marker for healthcare quality. Diabetes is a major contributor to acute care re-utilization and associated costs. The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization. RECENT FINDINGS: Hospital readmissions and ED visits by diabetes patients are common and costly. Major risk factors for readmission include sociodemographics, comorbidities, insulin use, hospital length of stay (LOS), and history of readmissions, most of which are non-modifiable. Several models for predicting the risk of readmission among diabetes patients have been developed, two of which have reasonable accuracy in external validation. In retrospective studies and mostly small randomized controlled trials (RCTs), interventions such as inpatient diabetes education, inpatient diabetes management services, transition of care support, and outpatient follow-up are generally associated with a reduction in the risk of acute care re-utilization. Data on readmission risk and readmission risk reduction interventions are limited or lacking among patients with diabetes hospitalized for COVID-19. The evidence supporting post-discharge follow-up by telephone is equivocal and also limited. Acute care re-utilization of patients with diabetes presents an important opportunity to improve healthcare quality and reduce costs. Currently available predictive models are useful for identifying higher risk patients but could be improved. Machine learning models, which are becoming more common, have the potential to generate more accurate acute care re-utilization risk predictions. Tools embedded in electronic health record systems are needed to translate readmission risk prediction models into clinical practice. Several risk reduction interventions hold promise but require testing in multi-site RCTs to prove their generalizability, scalability, and effectiveness.

Patient Understanding of Discharge Instructions for Home Diabetes Self-Management and Risk for Hospital Readmission and Emergency Department Visits.

OBJECTIVE: The primary objective of this study was to examine the patient comprehension of diabetes self-management instructions provided at hospital discharge as an associated risk of readmission. METHODS: Noncritically ill patients with diabetes completed patient comprehension questionnaires (PCQ) within 48 hours of discharge. PCQ scores were compared among patients with and without readmission or emergency department (ED) visits at 30 and 90 days. Glycemic measures 48 hours preceding discharge were investigated. Diabetes Early Readmission Risk Indicators (DERRIs) were calculated for each patient. RESULTS: Of 128 patients who completed the PCQ, scores were similar among those with 30-day (n = 31) and 90-day (n = 54) readmission compared with no readmission (n = 72) (79.9 ± 14.4 vs 80.4 ± 15.6 vs 82.3 ± 16.4, respectively) or ED visits. Clarification of discharge information was provided for 47 patients. PCQ scores of 100% were achieved in 14% of those with and 86% without readmission at 30 days (P = .108). Of predischarge glycemic measures, glycemic variability was negatively associated with PCQ scores (P = .035). DERRIs were significantly higher among patients readmitted at 90 days but not 30 days. CONCLUSION: These results demonstrate similar PCQ scores between patients with and those without readmission or ED visits despite the need for corrective information in many patients. Measures of glycemic variability were associated with PCQ scores but not readmission risk. This study validates DERRI as a predictor for readmission at 90 days.

Common sampling and modeling approaches to analyzing readmission risk that ignore clustering produce misleading results.

BACKGROUND: There is little consensus on how to sample hospitalizations and analyze multiple variables to model readmission risk. The purpose of this study was to compare readmission rates and the accuracy of predictive models based on different sampling and multivariable modeling approaches. METHODS: We conducted a retrospective cohort study of 17,284 adult diabetes patients with 44,203 discharges from an urban academic medical center between 1/1/2004 and 12/31/2012. Models for all-cause 30-day readmission were developed by four strategies: logistic regression using the first discharge per patient (LR-first), logistic regression using all discharges (LR-all), generalized estimating equations (GEE) using all discharges, and cluster-weighted (CWGEE) using all discharges. Multiple sets of models were developed and internally validated across a range of sample sizes. RESULTS: The readmission rate was 10.2% among first discharges and 20.3% among all discharges, revealing that sampling only first discharges underestimates a population's readmission rate. Number of discharges was highly correlated with number of readmissions (r = 0.87, P < 0.001). Accounting for clustering with GEE and CWGEE yielded more conservative estimates of model performance than LR-all. LR-first produced falsely optimistic Brier scores. Model performance was unstable below samples of 6000-8000 discharges and stable in larger samples. GEE and CWGEE performed better in larger samples than in smaller samples. CONCLUSIONS: Hospital readmission risk models should be based on all discharges as opposed to just the first discharge per patient and utilize methods that account for clustered data.

EATING AND GLYCEMIC CONTROL AMONG CRITICALLY ILL PATIENTS RECEIVING CONTINUOUS INTRAVENOUS INSULIN.

Objective: Consensus guidelines recommend that intensive care unit (ICU) patients with blood glucose (BG) levels >180 mg/dL receive continuous intravenous insulin (CII). The effectiveness of CII at controlling BG levels among patients who are eating relative to those who are eating nothing by mouth (nil per os; NPO) has not been described. Methods: We conducted a retrospective cohort study of 260 adult patients (156 eating, 104 NPO) admitted to an ICU between January 1, 2014, and December 31, 2014, who received CII. Patients were excluded for a diagnosis of diabetic ketoacidosis or hyperglycemic hyperosmolar nonketotic syndrome, admission to an obstetrics service, or receiving continuous enteral or parenteral nutrition. Results: Among 22 baseline characteristics, the proportion of patients receiving glucocorticoid treatment (GCTx) (17.3% eating, 37.5% NPO; P<.001) and APACHE II score (15.0 ± 7.5 eating, 17.9 ± 7.9 NPO; P = .004) were significantly different between eating and NPO patients. There was no significant difference in the primary outcome of patient-day weighted mean BG overall (153 ± 8 mg/dL eating, 156 ± 7 mg/dL NPO; P = .73), or day-by-day BG (P = .37) adjusted for GCTx and APACHE score. Surprisingly, there was a significant difference in the distribution of BG values, with eating patients having a higher percentage of BG readings in the recommended range of 140 to 180 mg/dL. However, eating patients showed greater glucose variability (coefficient of variation 23.1 ± 1.0 eating, 21.2 ± 1.0 NPO; P = .034). Conclusion: Eating may not adversely affect BG levels of ICU patients receiving CII. Whether or not prandial insulin improves glycemic control in this setting should be studied. Abbreviations: BG = blood glucose; CII = continuous insulin infusion; CV = coefficient of variation; HbA1c = hemoglobin A1c; ICU = intensive care unit; NPO = nil per os; PDWMBG = patient day weighted mean blood glucose.

Predischarge and Postdischarge Risk Factors for Hospital Readmission Among Patients With Diabetes.

BACKGROUND: Hospital readmission within 30 days of discharge (30-d readmission) is an undesirable outcome. Readmission of patients with diabetes is common and costly. Most of the studies that have examined readmission risk factors among diabetes patients did not include potentially important clinical data. OBJECTIVES: To provide a more comprehensive understanding of 30-day readmission risk factors among patients with diabetes based on predischarge and postdischarge data. RESEARCH DESIGN: In this retrospective cohort study, 48 variables were evaluated for association with readmission by multivariable logistic regression. SUBJECTS: In total, 17,284 adult diabetes patients with 44,203 hospital discharges from an urban academic medical center between January 1, 2004 and December 1, 2012. MEASURES: The outcome was all-cause 30-day readmission. Model performance was assessed by c-statistic. RESULTS: The 30-day readmission rate was 20.4%, and the median time to readmission was 11 days. A total of 27 factors were statistically significant and independently associated with 30-day readmission (P<0.05). The c-statistic was 0.82. The strongest risk factors were lack of a postdischarge outpatient visit within 30 days, hospital length-of-stay, prior discharge within 90 days, discharge against medical advice, sociodemographics, comorbidities, and admission laboratory values. A diagnosis of hypertension, preadmission sulfonylurea use, admission to an intensive care unit, sex, and age were not associated with readmission in univariate analysis. CONCLUSIONS: There are numerous risk factors for 30-day readmission among patients with diabetes. Postdischarge factors add to the predictive accuracy achieved by predischarge factors. A better understanding of readmission risk may ultimately lead to lowering that risk.

Correction to: Hospital Readmission of Patients with Diabetes.

This article was originally published with errors that were introduced during the editing process. The corrected version of this article appears below.

EXTERNAL VALIDATION OF THE DIABETES EARLY RE-ADMISSION RISK INDICATOR (DERRI™).

OBJECTIVE: The Diabetes Early Re-admission Risk Indicator (DERRI™) was previously developed and internally validated as a tool to predict the risk of all-cause re-admission within 30 days of discharge (30-day re-admission) of hospitalized patients with diabetes. In this study, the predictive performance of the DERRI™ with and without additional predictors was assessed in an external sample. METHODS: We conducted a retrospective cohort study of adult patients with diabetes discharged from two academic medical centers between January 1, 2000 and December 31, 2014. We applied the previously developed DERRI™, which includes admission laboratory results, sociodemographics, a diagnosis of certain comorbidities, and recent discharge information, and evaluated the effect of adding metabolic indicators on predictive performance using multivariable logistic regression. Total cholesterol and hemoglobin A1c (A1c) were selected based on clinical relevance and univariate association with 30-day re-admission. RESULTS: Among 105,974 discharges, 19,032 (18.0%) were followed by 30-day re-admission for any cause. The DERRI™ had a C-statistic of 0.634 for 30-day re-admission. Total cholesterol was the lipid parameter most strongly associated with 30-day re-admission. The DERRI™ predictors A1c and total cholesterol were significantly associated with 30-day re-admission; however, their addition to the DERRI™ did not significantly change model performance (C-statistic, 0.643 [95% confidence interval, 0.638 to 0.647]; P = .92). CONCLUSION: Performance of the DERRI™ in this external cohort was modest but comparable to other re-admission prediction models. Addition of A1c and total cholesterol to the DERRI™ did not significantly improve performance. Although the DERRI™ may be useful to direct resources toward diabetes patients at higher risk, better prediction is needed. ABBREVIATIONS: A1c = hemoglobin A1c; CI = confidence interval; DERRI™ = Diabetes Early Re-admission Risk Indicator; GEE = generalized estimating equation; HDL-C = high-density-lipoprotein cholesterol; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification; LDL-C = low-density-lipoprotein cholesterol.

THE EFFICACY AND SAFETY OF CO-ADMINISTRATION OF SITAGLIPTIN WITH METFORMIN IN PATIENTS WITH TYPE 2 DIABETES AT HOSPITAL DISCHARGE.

OBJECTIVE: Few randomized controlled trials have focused on the optimal management of patients with type 2 diabetes (T2D) during the transition from the inpatient to outpatient setting. This multicenter open-label study explored a discharge strategy based on admission hemoglobin A1c (HbA1c) to guide therapy in general medicine and surgery patients with T2D. METHODS: Patients with HbA1c ≤7% (53 mmol/mol) were discharged on sitagliptin and metformin; patients with HbA1c between 7 and 9% (53-75 mmol/mol) and those >9% (75 mmol/mol) were discharged on sitagliptinmetformin with glargine U-100 at 50% or 80% of the hospital daily dose. The primary outcome was change in HbA1c at 3 and 6 months after discharge. RESULTS: Mean HbA1c on admission for the entire cohort (N = 253) was 8.70 ± 2.3% and decreased to 7.30 ± 1.5% and 7.30 ± 1.7% at 3 and 6 months ( P<.001). Patients with HbA1c <7% went from 6.3 ± 0.5% to 6.3 ± 0.80% and 6.2 ± 1.0% at 3 and 6 months. Patients with HbA1c between 7 and 9% had a reduction from 8.0 ± 0.6% to 7.3 ± 1.1% and 7.3 ± 1.3%, and those with HbA1c >9% from 11.3 ± 1.7% to 8.0 ± 1.8% and 8.0 ± 2.0% at 3 and 6 months after discharge (both P<.001). Clinically significant hypoglycemia (<54 mg/dL) was observed in 4%, 4%, and 7% among patients with a HbA1c <7%, 7 to 9%, and >9%, while a glucose <40 mg/dL was reported in <1% in all groups. CONCLUSION: The proposed HbA1c-based hospital discharge algorithm using a combination of sitagliptin-metformin was safe and significantly improved glycemic control after hospital discharge in general medicine and surgery patients with T2D. ABBREVIATIONS: BG = blood glucose; DPP-4 = dipeptidyl peptidase-4; eGFR = estimated glomerular filtration rate; HbA1c = hemoglobin A1c; T2D = type 2 diabetes.

DEVELOPMENT AND VALIDATION OF A NOVEL TOOL TO PREDICT HOSPITAL READMISSION RISK AMONG PATIENTS WITH DIABETES.

OBJECTIVE: To develop and validate a tool to predict the risk of all-cause readmission within 30 days (30-d readmission) among hospitalized patients with diabetes. METHODS: A cohort of 44,203 discharges was retrospectively selected from the electronic records of adult patients with diabetes hospitalized at an urban academic medical center. Discharges of 60% of the patients (n = 26,402) were randomly selected as a training sample to develop the index. The remaining 40% (n = 17,801) were selected as a validation sample. Multivariable logistic regression with generalized estimating equations was used to develop the Diabetes Early Readmission Risk Indicator (DERRI™). RESULTS: Ten statistically significant predictors were identified: employment status; living within 5 miles of the hospital; preadmission insulin use; burden of macrovascular diabetes complications; admission serum hematocrit, creatinine, and sodium; having a hospital discharge within 90 days before admission; most recent discharge status up to 1 year before admission; and a diagnosis of anemia. Discrimination of the model was acceptable (C statistic 0.70), and calibration was good. Characteristics of the validation and training samples were similar. Performance of the DERRI™ in the validation sample was essentially unchanged (C statistic 0.69). Mean predicted 30-d readmission risks were also similar between the training and validation samples (39.3% and 38.7% in the highest quintiles). CONCLUSION: The DERRI™ was found to be a valid tool to predict all-cause 30-d readmission risk of individual patients with diabetes. The identification of high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs. ABBREVIATIONS: DERRI™ = Diabetes Early Readmission Risk Indicator ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification GEE = generalized estimating equations ROC = receiver operating characteristic.

Hospital readmission of patients with diabetes.

Hospital readmission is a high-priority health care quality measure and target for cost reduction. Despite broad interest in readmission, relatively little research has focused on patients with diabetes. The burden of diabetes among hospitalized patients, however, is substantial, growing, and costly, and readmissions contribute a significant portion of this burden. Reducing readmission rates of diabetic patients has the potential to greatly reduce health care costs while simultaneously improving care. Risk factors for readmission in this population include lower socioeconomic status, racial/ethnic minority, comorbidity burden, public insurance, emergent or urgent admission, and a history of recent prior hospitalization. Hospitalized patients with diabetes may be at higher risk of readmission than those without diabetes. Potential ways to reduce readmission risk are inpatient education, specialty care, better discharge instructions, coordination of care, and post-discharge support. More studies are needed to test the effect of these interventions on the readmission rates of patients with diabetes.

SGLT-2 inhibitors in heart failure and chronic kidney disease: A review for internists.

Despite current therapies, heart failure and chronic kidney disease continue to be major causes of morbidity and mortality. Sodium-glucose cotransporter 2 (SGLT-2) inhibitors have recently become standard-of-care therapy for these conditions. This review summarizes important randomized controlled trials of SGLT-2 inhibitors and guidelines for using these agents in patients with heart failure and chronic kidney disease in both clinic and hospital settings.

Mechanical reinforcement of polymeric fibers through peptide nanotube incorporation.

High aspect ratio nanotubular assemblies can be effective fillers in mechanically reinforced composite materials. However, most existing nanotubes used for structural purposes are limited in their range of mechanical, chemical, and biological properties. We demonstrate an alternative approach to mechanical reinforcement of polymeric systems by incorporating synthetic D,L-cyclic peptide nanotube bundles as a structural filler in electrospun poly D-, L-lactic acid fibers. The nanotube bundles self-assemble through dynamic hydrogen bonding from synthetic cyclic peptides to yield structures whose dimensions can be altered based on processing conditions, and can be up to hundreds of micrometers long and several hundred nanometers wide. With 8 wt % peptide loading, the composite fibers are >5-fold stiffer than fibers composed of the polymer alone, according to atomic force microscopy-based indentation experiments. This represents a new use for self-assembling cyclic peptides as a load-bearing component in biodegradable composite materials.

Adverse childhood event subtypes and routine diabetes care utilization among U.S. adults with Type 2 diabetes.

OBJECTIVE: Adverse childhood events (ACEs) have significant impacts on adulthood health, including greater risk of Type 2 diabetes (T2D). While there is a known connection to increased use of outpatient and emergency healthcare services, the potential role of ACEs in routine diabetes care utilization remains unclear. This study uses ACE subtypes to explain pathways to routine diabetes care utilization among adults with T2D. METHOD: Cross-sectional data were obtained from the 2019 Behavioral Risk Factor Surveillance System, a survey of a representative sample of U.S. adults. Eligible participants resided in a state that completed both the Adverse Childhood Experiences (ACEs) and Diabetes modules and were diagnosed with non-gestational diabetes (N = 9,904), of whom the majority were presumed to have T2D. Confirmatory factor analysis was used to test the measurement model for three ACE factors: household dysfunction, physical and emotional abuse, and sexual abuse. Structural equation modeling was used to relate factors to four routine diabetes care utilization outcomes. RESULTS: Factor loadings were strong, and fit indices indicated good measurement model and full structural model fits. In the full structural equation model, household dysfunction was associated with decreased likelihood of meeting frequency recommendations for A1C testing (ß = -0.22, p < .05) and foot exams (ß = -0.29, p < .01). Physical and emotional abuse were associated with greater likelihood of meeting A1C testing frequency recommendations (ß = 0.21, p < .05). CONCLUSION: Specific types of ACEs may differentially relate to routine diabetes care utilization in adulthood. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Hospital Readmission Risk and Risk Factors of People with a Primary or Secondary Discharge Diagnosis of Diabetes.

Hospital readmission among people with diabetes is common and costly. A better understanding of the differences between people requiring hospitalization primarily for diabetes (primary discharge diagnosis, 1°DCDx) or another condition (secondary discharge diagnosis, 2°DCDx) may translate into more effective ways to prevent readmissions. This retrospective cohort study compared readmission risk and risk factors between 8054 hospitalized adults with a 1°DCDx or 2°DCDx. The primary outcome was all-cause hospital readmission within 30 days of discharge. The readmission rate was higher in patients with a 1°DCDx than in patients with a 2°DCDx (22.2% vs. 16.2%, p < 0.01). Several independent risk factors for readmission were common to both groups including outpatient follow up, length of stay, employment status, anemia, and lack of insurance. C-statistics for the multivariable models of readmission were not significantly different (0.837 vs. 0.822, p = 0.15). Readmission risk of people with a 1°DCDx was higher than that of people with a 2°DCDx of diabetes. Some risk factors were shared between the two groups, while others were unique. Inpatient diabetes consultation may be more effective at lowering readmission risk among people with a 1°DCDx. These models may perform well to predict readmission risk.

Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) technology design and development continues to be rapid, despite major limitations in its current form as a practice and discipline to address all sociohumanitarian issues and complexities. From these limitations emerges an imperative to strengthen AI and ML literacy in underserved communities and build a more diverse AI and ML design and development workforce engaged in health research. OBJECTIVE: AI and ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities within the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Ethics and Equity Workgroup (EEWG) that led to the development of deliverables that will help put ethics and fairness at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. METHODS: The AIM-AHEAD EEWG was created in 2021 with 3 cochairs and 51 members in year 1 and 2 cochairs and ~40 members in year 2. Members in both years included AIM-AHEAD principal investigators, coinvestigators, leadership fellows, and research fellows. The EEWG used a modified Delphi approach using polling, ranking, and other exercises to facilitate discussions around tangible steps, key terms, and definitions needed to ensure that ethics and fairness are at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. RESULTS: The EEWG developed a set of ethics and equity principles, a glossary, and an interview guide. The ethics and equity principles comprise 5 core principles, each with subparts, which articulate best practices for working with stakeholders from historically and presently underrepresented communities. The glossary contains 12 terms and definitions, with particular emphasis on optimal development, refinement, and implementation of AI and ML in health equity research. To accompany the glossary, the EEWG developed a concept relationship diagram that describes the logical flow of and relationship between the definitional concepts. Lastly, the interview guide provides questions that can be used or adapted to garner stakeholder and community perspectives on the principles and glossary. CONCLUSIONS: Ongoing engagement is needed around our principles and glossary to identify and predict potential limitations in their uses in AI and ML research settings, especially for institutions with limited resources. This requires time, careful consideration, and honest discussions around what classifies an engagement incentive as meaningful to support and sustain their full engagement. By slowing down to meet historically and presently underresourced institutions and communities where they are and where they are capable of engaging and competing, there is higher potential to achieve needed diversity, ethics, and equity in AI and ML implementation in health research.

Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes.

A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling approach, however, is unclear. In 2,836,569 encounters of 36,641 diabetes patients, deep learning (DL) long short-term memory (LSTM) models predicting unplanned, all-cause, 30-day readmission were developed and compared to several traditional models. Models used EHR data defined by a Common Data Model. The LSTM model Area Under the Receiver Operating Characteristic Curve (AUROC) was significantly greater than that of the next best traditional model [LSTM 0.79 vs Random Forest (RF) 0.72, p<0.0001]. Experiments showed that performance of the LSTM models increased as prior encounter number increased up to 30 encounters. An LSTM model with 16 selected laboratory tests yielded equivalent performance to a model with all 981 laboratory tests. This new DL model may provide the basis for a more useful readmission risk prediction tool for diabetes patients.

Sex differences in glycemic measures, complications, discharge disposition, and postdischarge emergency room visits and readmission among non-critically ill, hospitalized patients with diabetes.

INTRODUCTION: The purpose of this prospective observational cohort study was to examine sex differences in glycemic measures, diabetes-related complications, and rates of postdischarge emergency room (ER) visits and hospital readmissions in non-critically ill, hospitalized patients with diabetes. RESEARCH DESIGN AND METHODS: Demographic data including age, body mass index, race, blood pressure, reason for admission, diabetes medications at admission and discharge, diabetes-related complications, laboratory data (hematocrit, creatinine, hemoglobin A1c, point-of-care blood glucose measures), length of stay (LOS), and discharge disposition were collected. Patients were followed for 90 days following hospital discharge to obtain information regarding ER visits and readmissions. RESULTS: 120 men and 100 women consented to participate in this study. There were no sex differences in patient demographics, diabetes duration or complications, or LOS. No differences were observed in the percentage of men and women with an ER visit or hospital readmission within 30 (39% vs 33%, p=0.40) or 90 (60% vs 49%, p=0.12) days of hospital discharge. More men than women experienced hypoglycemia prior to discharge (18% vs 8%, p=0.026). More women were discharged to skilled nursing facilities (p=0.007). CONCLUSIONS: This study demonstrates that men and women hospitalized with an underlying diagnosis of diabetes have similar preadmission glycemic measures, diabetes duration, and prevalence of diabetes complications. More men experienced hypoglycemia prior to discharge. Women were less likely to be discharged to home. Approximately 50% of men and women had ER visits or readmissions within 90 days of hospital discharge. TRIAL REGISTRATION NUMBER: NCT03279627.

The Diabetes Transition of Hospital Care (DiaTOHC) Pilot Study: A Randomized Controlled Trial of an Intervention Designed to Reduce Readmission Risk of Adults with Diabetes.

Hospital readmission within 30 days of discharge (30-day readmission) is a high-priority quality measure and cost target. The purpose of this study was to explore the feasibility and efficacy of the Diabetes Transition of Hospital Care (DiaTOHC) Program on readmission risk in high-risk adults with diabetes. This was a non-blinded pilot randomized controlled trial (RCT) that compared usual care (UC) to DiaTOHC at a safety-net hospital. The primary outcome was all-cause 30-day readmission. Between 16 October 2017 and 30 May 2019, 93 patients were randomized. In the intention-to-treat (ITT) population, 14 (31.1%) of 45 DiaTOHC subjects and 15 (32.6%) of 46 UC subjects had a 30-day readmission, while 35.6% DiaTOHC and 39.1% UC subjects had a 30-day readmission or ED visit. The Intervention−UC cost ratio was 0.33 (0.13−0.79) 95%CI. At least 93% of subjects were satisfied with key intervention components. Among the 69 subjects with baseline HbA1c >7.0% (53 mmol/mol), 30-day readmission rates were 23.5% (DiaTOHC) and 31.4% (UC) and composite 30-day readmission/ED visit rates were 26.5% (DiaTOHC) and 40.0% (UC). In this subgroup, the Intervention−UC cost ratio was 0.21 (0.08−0.58) 95%CI. The DiaTOHC Program may be feasible and may decrease combined 30-day readmission/ED visit risk as well as healthcare costs among patients with HbA1c levels >7.0% (53 mmol/mol).

Understanding Type 2 Diabetes Self-Management in Racial/Ethnic Minorities: Application of the Extended Parallel Processing Model and Sensemaking Theory in a Qualitative Study.

PURPOSE: The purpose of the study was to understand the role of perceived disease threat and self-efficacy in type 2 diabetes (T2DM) patients' self-management by using the extended parallel processing model (EPPM) and sensemaking theory. METHODS: Semistructured interviews (n = 25) were conducted with T2DM patients from an urban safety-net hospital. Participants were 50% male/female median age was 55 years and 76% were Black. Participants were categorized by EPPM group based on validated questionnaires (high/low disease threat [HT/LT]; high/low self-efficacy [HE/LE]). Nine were HT/HE, 7 HT/LE, 6 LT/HE, and 3 LT/LE. Interviews were transcribed and analyzed using inductive and deductive coding. Sensemaking theory was applied to contextualize and analyze data. RESULTS: Those with HT indicated threat fluctuated throughout diagnosis but that certain triggers (eg, diabetic complications) drove changes in disease view. Those in the HT/HE group more frequently expressed disease acceptance, whereas the HT/LE group more often expressed anger or denial. HT/HE participants expressed having adequate social support and higher trust in health care providers. HT/LE participants reported limited problem-solving skills. In those with LT, the HE group took more ownership of self-management behaviors. The LT/LE group had heightened positive and negative emotional responses that appeared to limit their ability to perform self-care. They also less frequently described problem-solving skills, instead expressing reliance on medical guidance from their providers. CONCLUSIONS: EPPM and sensemaking theory are effective frameworks for understanding how perceived health threat and self-efficacy may impede T2DM self-care. A greater focus on these constructs is needed to improve care among low-income minority patients, especially those with low threat and self-efficacy.