The relationship between diabetes mellitus and 30-day readmission rates

Transfers between health facilities of people living with diabetes attending primary health care services in the Western Cape Province of South Africa: A retrospective cohort study

OBJECTIVES

Transfers between health facilities of people living with HIV attending primary health care (PHC) including hospital to PHC facility, PHC facility to hospital and PHC facility to PHC facility transfers occur frequently, affect health service planning, and are associated with disengagement from care and viraemia. Data on transfers among people living with diabetes attending PHC, particularly transfers between PHC facilities, are few. We assessed the transfer incidence rate of people living with diabetes attending PHC, and the association between transfers between PHC facilities and subsequent HbA1c values.

METHODS

We analysed data on HbA1c tests at public sector facilities in the Western Cape Province (2016-March 2020). Individuals with an HbA1c in 2016-2017 were followed-up for 27 months and included in the analysis if ≥18 years at first included HbA1c, ≥2 HbA1cs during follow-up and ≥1 HbA1c at a PHC facility. A visit interval was the duration between two consecutive HbA1cs. Successive HbA1cs at different facilities of any type indicated any transfer, and HbA1cs at different PHC facilities indicated a transfer between PHC facilities. Mixed effects logistic regression adjusted for sex, age, rural/urban facility attended at the start of the visit interval, disengagement (visit interval >14 months) and a hospital visit during follow-up assessed the association between transfers between PHC facilities and HbA1c >8%.

RESULTS

Among 102,813 participants, 22.6% had ≥1 transfer of any type. Including repeat transfers, there were 29,994 transfers (14.4 transfers per 100 person-years, 95% confidence interval [CI] 14.3-14.6). A total of 6996 (30.1%) of those who transferred had a transfer between PHC facilities. Visit intervals with a transfer between PHC facilities were longer (349 days, interquartile range [IQR] 211-503) than those without any transfer (330 days, IQR 182-422). The adjusted relative odds of an HbA1c ≥8% after a transfer between PHC facilities versus no transfer were 1.20 (95% CI 1.05-1.37).

CONCLUSION

The volume of transfers involving PHC facilities requires consideration when planning services. Individuals who transfer between PHC facilities require additional monitoring and support.

16. Diabetes Care in the Hospital: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Predictors of frequency of 1-year readmission in adult patients with diabetes

Diabetes mellitus (DM) is the third most common chronic condition associated with frequent hospital readmissions. Predictors of the number of readmissions within 1 year among patients with DM are less often studied compared with those of 30-day readmission. This study aims to identify predictors of number of readmissions within 1 year amongst adult patients with DM and compare different count regression models with respect to model fit. Data from 2008 to 2015 were extracted from the electronic medical records of the National University Hospital, Singapore. Inpatients aged ≥ 18 years at the time of index admission with a hospital stay > 24 h and survived until discharge were included. The zero-inflated negative binomial (ZINB) model was fitted and compared with three other count models (Poisson, zero-inflated Poisson and negative binomial) in terms of predicted probabilities, misclassification proportions and model fit. Adjusted for other variables in the model, the expected number of readmissions was 1.42 (95% confidence interval [CI] 1.07 to 1.90) for peripheral vascular disease, 1.60 (95% CI 1.34 to 1.92) for renal disease and 2.37 (95% CI 1.67 to 3.35) for Singapore residency. Number of emergency visits, number of drugs and age were other significant predictors, with length of stay fitted as a zero-inflated component. Model comparisons suggested that ZINB provides better prediction than the other three count models. The ZINB model identified five patient characteristics and two comorbidities associated with number of readmissions. It outperformed other count regression models but should be validated before clinical adoption.

Characteristics and risk factors for readmission in HIV-infected patients with Talaromyces marneffei infection

OBJECTIVES

Talaromyces marneffei (T. marneffei) is an opportunistic fungal infection (talaromycosis), which is common in subtropical regions and is a leading cause of death in HIV-1-infected patients. This study aimed to determine the characteristics and risk factors associated with hospital readmissions in HIV patients with T. marneffei infection in order to reduce readmissions.

METHODS

We conducted a retrospective study of admitted HIV-infected individuals at the Fourth People's Hospital of Nanning, Guangxi, China, from 2012 to 2019. Kaplan-Meier analyses and Principal component analysis (PCA) were used to evaluate the effects of T. marneffei infection on patient readmissions. Additionally, univariate and multifactorial analyses, as well as Propensity score matching (PSM) were used to analyze the factors associated with patient readmissions.

RESULTS

HIV/AIDS patients with T. marneffei-infected had shorter intervals between admissions and longer lengths of stay than non-T. marneffei-infected patients, despite lower readmission rates. Compared with non-T. marneffei-infected patients, the mortality rate for talaromycosis patients was higher at the first admission. Among HIV/AIDS patients with opportunistic infections, the mortality rate was highest for T. marneffei at 16.2%, followed by cryptococcus at 12.5%. However, the readmission rate was highest for cryptococcus infection (37.5%) and lowest for T. marneffei (10.8%). PSM and Logistic regression analysis identified leukopenia and elevated low-density lipoprotein (LDL) as key factors in T.marneffei-infected patients hospital readmissions.

CONCLUSIONS

The first admission represents a critical window to intervene in the prognosis of patients with T. marneffei infection. Leukopenia and elevated LDL may be potential risk factors impacting readmissions. Our findings provide scientific evidence to improve the long-term outcomes of HIV patients with T. marneffei infection.

Development of Quality Measures for Inpatient Diabetes Care and Education Specialists: A Call to Action

ABSTRACT

Diabetes and hyperglycemia are associated with an increased risk of in-hospital complications that lead to longer lengths of stay, increased morbidity, higher mortality, and risk of readmission. Diabetes care and education specialists (DCESs) working in hospital settings are uniquely prepared and credentialed to serve as content experts to facilitate change and implement processes and programs to improve glycemic-related outcomes. A recent survey of DCESs explored the topic of productivity and clinical metrics. Outcomes highlighted the need to better evaluate the impact and value of inpatient DCESs, advocate for the role, and to expand diabetes care and education teams to optimize outcomes. The purpose of this article was to recommend strategies and metrics that can be used to quantify the work of inpatient DCESs and describe how such metrics can help to show the value of the inpatient DCES and assist in making a business case for the role.

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.

Interventions incorporating a multi-disciplinary team approach and a dedicated care team can help reduce preventable hospital readmissions of people with type 2 diabetes mellitus: A scoping review of current literature

AIMS

This review aimed to identify interventions that hospitals can implement to reduce preventable hospital readmissions of people with type 2 diabetes mellitus (T2DM).

METHODS

A scoping review framework was utilised to inform the overall process. The electronic databases Cumulative Index to Nursing and Allied Health Literature (CINAHL), Medline, the University of New England (UNE) library search engine and Google Scholar were utilised to search for relevant literature.

RESULTS

The results from this review demonstrate that interventions started at index admission for people diagnosed with T2DM can result in reductions in hospital readmissions. Common strategies which attributed to the success of interventions in reducing hospital readmissions of people with T2DM included a multidisciplinary team approach, a dedicated care team, certified diabetes educator appointments, basic survival skills education and influencing hospital protocol development and implementation.

CONCLUSION

This scoping review is an attempt at exploring and synthesising current research on interventions that hospitals can implement to reduce preventable hospital readmissions of people with T2DM.

16. Diabetes Care in the Hospital: Standards of Care in Diabetes-2023

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

A transition of care model from hospital to community for Hispanic/Latino adult patients with diabetes: design and rationale for a pilot study

BACKGROUND

The Hispanic/Latino population is disproportionately affected and has a higher risk of developing diabetes than their non-Hispanic White counterparts and worse diabetes-related outcomes. Diabetes continues to be an economic burden. This economic burden is partially due to the significantly higher rates of hospital readmission for individuals with diabetes. People with diabetes, particularly those who are members of racial/ethnic minority groups, are at higher risk for readmission and emergency department (ED) visits. Despite recommendations regarding transition of care, an optimal approach to the transition of care for ethnic/minority patients remains unclear.

METHODS

The study population includes self-identified Hispanic/Latino adults with diabetes. We have two aims: (1) designed and developed a transition of care model and (2) pilot test the newly developed transition of care model. For aim 1, semi-structures interviews conducted with patients and providers. For aim 2, patients admitted to the hospital enrolled to receive the newly designed transition of care model. For aim 1, patients and providers completed a short questionnaire. For aim 2, patients completed a set of questionnaires including demographic information, medical history, sociocultural, and social support. The primary outcome for aim 2 is emergency department visit within 30 days post-discharge. The secondary outcome is 30- days unplanned readmissions. Feasibility outcomes include the number of participants identified, number of patients enrolled, number of participants who completed all the questionnaires, number of participants with a 30-day follow-up call, and number of participants who completed the 30-day post-discharge questionnaire. Due to the COVID-19 pandemic, the study design was adapted to include the Plan-Do-Study-Act framework to adjust to the ongoing changes in transition of care due to the pandemic burden on the health care systems.

CONCLUSION

Transition of care for Hispanic/Latino patients with diabetes remains a major area of interest that requires further research. The pandemic required that we adapted the study to reflect the realities of health care systems during a time of crisis. The methods share in this manuscript can potentially help other investigators as they designed their studies.

TRIAL REGISTRATION

ClinicalTrials.gov identifier NCT04864639. 4/29/2021. https://clinicaltrials.gov/ct2/show/NCT04864639 .

Predicting readmission due to severe hyperglycemia after a hyperglycemic crisis episode

AIM

This study aimed to investigate the readmission pattern and risk factors for patients who experienced a hyperglycemic crisis.

METHODS

Patients admitted to MacKay Memorial Hospital for diabetic ketoacidosis (DKA) or hyperglycemic hyperosmolar state (HHS) between January 2016 and April 2019 were studied. The timing of the first readmission for hyperglycemia and other causes was recorded. Kaplan-Meier analysis was used to compare patients with hyperglycemia and all-cause readmissions. Cox regression was used to identify independent predictors for hyperglycemia and all-cause readmission post-discharge.

RESULTS

The study cohort included 410 patients, and 15.3 % and 46.3 % of them had hyperglycemia and all-cause readmissions, respectively. The DKA and HHS group showed a similar incidence for hyperglycemia, with the latter group showing a higher incidence of all-cause readmissions. The significant predictors of hyperglycemia readmissions included young age, smoking, hypoglycemia, higher effective osmolality, and hyperthyroidism in the DKA group and higher glycated hemoglobin level in the HHS group.

CONCLUSIONS

Patients who experienced DKA and HHS had similar hyperglycemia readmission rates; however, predictors in the DKA group were not applicable to the HHS group. Designing different strategies for different types of hyperglycemic crisis is necessary for preventing readmission.

Explainable Stacking-Based Model for Predicting Hospital Readmission for Diabetic Patients

Artificial intelligence is changing the practice of healthcare. While it is essential to employ such solutions, making them transparent to medical experts is more critical. Most of the previous work presented disease prediction models, but did not explain them. Many healthcare stakeholders do not have a solid foundation in these models. Treating these models as ‘black box’ diminishes confidence in their predictions. The development of explainable artificial intelligence (XAI) methods has enabled us to change the models into a ‘white box’. XAI allows human users to comprehend the results from machine learning algorithms by making them easy to interpret. For instance, the expenditures of healthcare services associated with unplanned readmissions are enormous. This study proposed a stacking-based model to predict 30-day hospital readmission for diabetic patients. We employed Random Under-Sampling to solve the imbalanced class issue, then utilised SelectFromModel for feature selection and constructed a stacking model with base and meta learners. Compared with the different machine learning models, performance analysis showed that our model can better predict readmission than other existing models. This proposed model is also explainable and interpretable. Based on permutation feature importance, the strong predictors were the number of inpatients, the primary diagnosis, discharge to home with home service, and the number of emergencies. The local interpretable model-agnostic explanations method was also employed to demonstrate explainability at the individual level. The findings for the readmission of diabetic patients could be helpful in medical practice and provide valuable recommendations to stakeholders for minimising readmission and reducing public healthcare costs.

Readmission Predictors in Patients With Type II Diabetes

BACKGROUND

In patients with type II diabetes, hospital readmissions occur frequently and contribute significantly to morbidity. Limited research has predicted the factors that contribute to preventable readmission.

PURPOSE

This study identified the predictors of 30-day hospital readmission in patients with type II diabetes.

METHODS

This single-site 400 patients study examined effects of comorbidities, race, endocrinology consultation, diabetes self-management education, and diabetes medications on 30-day hospital readmissions.

RESULTS

Patients with more comorbidities, who were Hispanics, and those who received an endocrinology consultation were more likely to be readmitted. Patients who received diabetes self-management education or were prescribed both oral and insulin medications were less likely to be readmitted.

CONCLUSION

Findings identified the factors related to 30-day readmission in patients with diabetes, emphasizing the need for diabetes self-management education. Understanding why patients are readmitted within 30 days of initial admission will empower nurses to create targeted plans to improve nursing care quality and prevent readmission.

Interpretable Identification of Comorbidities Associated with Recurrent ED and Inpatient Visits

In the hospital setting, a small percentage of recurrent frequent patients contribute to a disproportional amount of healthcare resource utilization. Moreover, in many of these cases, patient outcomes can be greatly improved by reducing re-occurring visits, especially when they are associated with substance abuse, mental health, and medical factors that could be improved by social-behavioral interventions, outpatient or preventative care. Additionally, health care costs can be reduced significantly with fewer preventable recurrent visits. To address this, we developed a novel, interpretable framework that both identifies recurrent patients with high utilization and determines which comorbidities contribute most to their recurrent visits. Specifically, we present a novel algorithm, called the minimum similarity association rules (MSAR), which balances the confidence-support trade-off, to determine the conditions most associated with re-occurring Emergency department and inpatient visits. We validate MSAR on a large Electronic Health Record dataset, demonstrating the effectiveness and consistency in ability to find low-support comorbidities with high likelihood of being associated with recurrent visits, which is challenging for other algorithms such as XGBoost. Clinical relevance- In the era of value-based care and population health management, the proposal could be used for decision making to help reduce future recurrent admissions, improve patient outcomes and reduce the cost of healthcare.

A hybrid approach for risk stratification and predictive modelling of 30-days unplanned readmission of comorbid patients with diabetes

OBJECTIVES

When comorbid patients with diabetes have 30-days Unplanned Readmission (URA), they attract more burdens to the healthcare system due to increased cost of treatment, insurance penalties to hospitals, and unavailable bed spaces for new patients. This paper, therefore, aims to develop a risk stratification and a predictive model for identifying patients at various risk severities of 30-days URA.

METHODS

Patients records of comorbid patients with diabetes treated with different medications were collected from different hospitals and analysed with Principal Component Analysis (PCA) and Multivariate Logistic Regression (MLR) to determine the probability of 30-days URA, which is classified into very low, low, moderate, high, and very high. The risk classes are later modelled using ANOVA feature selection to identify the optimal predictors and the best random forest (RF) hyperparameters for 30-days URA risk stratification. Synthetic Minority Oversampling Technique (SMOTE) was used to balance the risk classes while employing a10-fold cross-validation.

RESULTS

After analysing 17,933 episodes of comorbid diabetes patients' treatment, 10.71% are identified to have 30-days URA with 61.95% of patients at moderate risk, 35.5% at low risk, 2.25% at very low risk, 0.37% at high risk, and 0.08% at very high risk. The predictive accuracy of RF is: - recall: 0.947 ± 0.035, precision: 0.951 ± 0.033, F1-score: 0.947 ± 0.035, AUC: 0.994 ± 0.007 and Average Precision (AP) of 0.99. The predictive accuracies of the risk classes measured with F1-score are: - very low: 0.985 ± 0.019, low risk: 0.871 ± 0.079, moderate: 0.881 ± 0.093, high: 0.999 ± 0.003, and very high: 1.000 ± 0.00.

CONCLUSION

This study identified the risk severity of comorbid patients with diabetes treated with different medications, making it easier to identify those that will be prioritized on hospitalization to minimize 30-days URA. By relying on the technique developed, vulnerable patients to 30-days URA can be given better post-discharge monitoring to build critical self-management skills that will minimize the cost of diabetes care and improve the quality of life.

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.

Evaluation of step-down oral antibiotic therapy for uncomplicated streptococcal bloodstream infections on clinical outcomes

Background:

Despite the severity and frequency of streptococcal bloodstream infections (BSIs), the effectiveness of oral definitive therapy remains unknown. The objective of this study was to evaluate the clinical outcomes of step-down oral antibiotics for the treatment of uncomplicated streptococcal BSIs.

Methods:

In this retrospective cohort study, adult patients admitted with uncomplicated streptococcal BSI between June 2015 and June 2017 were included. Patients were excluded if they received <48 h of antibiotic therapy; therapy was started >48 h after first positive culture; had complicated infections of endocarditis, bone and joint infections, or central nervous system infections; Pitt bacteremia score (PBS) ⩾ 4; or failed to respond to effective therapy necessitating continued intravenous (IV) therapy. Patients were grouped by receipt of step-down oral antibiotic therapy (PO group) versus continued IV therapy (IV group). Outcomes included hospital length of stay (LOS), 30-day recurrence of BSI, 30-day readmission, 30-day all-cause mortality, and catheter-related or drug-related adverse events (AEs).

Results:

Of 244 patients included, 40% received step-down oral therapy (n = 98). Overall, the most common source of BSI was pneumonia (22%), followed by skin and soft tissue infections (SSTI) (18%). Severity of illness measured by intensive care unit (ICU) admission and PBS was similar. The IV group had significantly longer LOS [median 10 (interquartile range [IQR] = 5–21) versus 5 (4–6) days, p < 0.01] compared with the PO group. BSI recurrence, readmission, all-cause mortality within 30 days, and AEs were similar between the groups (p = ns).

Conclusion:

In uncomplicated streptococcal BSI, patients treated with step-down oral antibiotic therapy had significantly shorter LOS compared with continued IV therapy without compromise of clinical outcomes.

UNPLANNED REPRESENTATION TO HOSPITAL BY PATIENTS WITH DIABETES: DEVELOPMENT AND PILOT FEASIBILITY TESTING OF A SCREENING TOOL

BackgroundUnplanned representation of patients with diabetes recently discharged from emergency department or in-patient hospitals is a common but complex problem worldwide. This study set out to examine the feasibility of a risk screening interview and whether component characteristics may be associated with unplanned representation of patients with diabetes to a tertiary metropolitan hospital.MethodsA screening interview comprised of demographic, social and clinical characteristics was developed and piloted using prospective cross-sectional survey design. A convenience sample of 55 patients was recruited and screened. Outcomes were the occurrence of unplanned representation to hospital within 28 or 90 days of hospital discharge from the index presentation.ResultsThe screening interview was shown to be broadly feasible and acceptable for use by staff and patients, with identified areas for modification. Seventeen participants (30.9%) experienced unplanned representation within 90 days of hospital discharge; for 13 participants (23.6%) this occurred within 28 days. Characteristics linked with unplanned representation to hospital were identified.ConclusionsPreliminary data indicated the feasibility of tool use and informed refinement for future testing of the ability of the screening interview to predict those patients with diabetes at high risk of unplanned representation to hospital to enhance effective care planning.

16. Diabetes Care in the Hospital: Standards of Medical Care in Diabetes-2022

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Clinical risk factors and social needs of 30-day readmission among patients with diabetes: A retrospective study of the Deep South

Introduction

Evidence is needed for 30-day readmission risk factors (clinical factors and social needs) among patients with diabetes in the Deep South. To address this need, our objectives were to identify risk factors associated with 30-day readmissions among this population and determine the added predictive value of considering social needs.

Methods

This retrospective cohort study utilized electronic health records from an urban health system in the Southeastern U.S. The unit of analysis was index hospitalization with a 30-day washout period. The index hospitalizations were preceded by a 6-month pre-index period to capture risk factors (including social needs), and hospitalizations were followed 30 days post-discharge to evaluate all-cause readmissions (1=readmission; 0=no readmission). We performed unadjusted (chi-square and student's t-test, where applicable) and adjusted analyses (multiple logistic regression) to predict 30-day readmissions.

Results

A total of 26,332 adults were retained in the study population. Eligible patients contributed a total of 42,126 index hospitalizations, and the readmission rate was 15.21%. Risk factors associated with 30-day readmissions included demographics (e.g., age, race/ethnicity, insurance), characteristics of hospitalizations (e.g., admission type, discharge status, length of stay), labs and vitals (e.g., highest and lowest blood glucose measurements, systolic and diastolic blood pressure), co-existing chronic conditions, and preadmission antihyperglycemic medication use. In univariate analyses of social needs, activities of daily living (p<0.001), alcohol use (p<0.001), substance use (p=0.002), smoking/tobacco use (p<0.001), employment status (p<0.001), housing stability (p<0.001), and social support (p=0.043) were significantly associated with readmission status. In the sensitivity analysis, former alcohol use was significantly associated with higher odds of readmission compared to no alcohol use [aOR (95% CI): 1.121 (1.008-1.247)].

Conclusions

Clinical assessment of readmission risk in the Deep South should consider patients' demographics, characteristics of hospitalizations, labs, vitals, co-existing chronic conditions, preadmission antihyperglycemic medication use, and social need (i.e., former alcohol use). Factors associated with readmission risk can help pharmacists and other healthcare providers identify high-risk patient groups for all-cause 30-day readmissions during transitions of care. Further research is needed about the influence of social needs on readmissions among populations with diabetes to understand the potential clinical utility of incorporating social needs into clinical services.

Predictors of Rehospitalization and Mortality in Diabetes-Related Hospital Admissions

The risk factors of rehospitalization and death post-discharge in diabetes-related hospital admissions are not fully understood. To determine them, a population-based retrospective epidemiological survey was performed on diabetes-related admissions from the Polish national database. Logistic regression models were used, in which the dependent variables were rehospitalization due to diabetes complications and death within 90 days after the index hospitalization. In 2017, there were 74,248 hospitalizations related to diabetes. A total of 11.3% ended with readmission. Risk factors for rehospitalization were as follows: age < 35 years; male sex; prior hospitalization due to acute diabetic complications; weight loss; peripheral artery disease; iron deficiency anemia; kidney failure; alcohol abuse; heart failure; urgent, emergency, or weekend admission; length of hospitalization; and hospitalization in a teaching hospital with an endocrinology/diabetology unit. Furthermore, 7.3% of hospitalizations resulted in death within 90 days following discharge. Risk factors for death were as follows: age; neoplastic disease with/without metastases; weight loss; coagulopathy; alcohol abuse; acute diabetes complications; heart failure; kidney failure; iron deficiency anemia; peripheral artery disease; fluid, electrolytes, and acid–base balance disturbances; urgent or emergency and weekend admission; and length of hospitalization. We concluded that of all investigated factors, only hospitalization within an experienced specialist center may reduce the frequency of the assessed outcomes.

Reasons for hospitalizations in patients with type 2 diabetes in the CANVAS programme: A secondary analysis

AIM

To determine the reasons for hospitalizations in the CANagliflozin cardioVascular Assessment Study (CANVAS) programme and the effects of the sodium-glucose co-transporter-2 inhibitor canagliflozin on hospitalization.

MATERIALS AND METHODS

A secondary analysis was performed on the CANVAS programme that included 10 142 participants with type 2 diabetes randomized to canagliflozin or placebo. The primary outcome was the rate of total (first plus all recurrent) all-cause hospitalizations (ACH). Secondary outcomes were total hospitalizations categorized by the Medical Dictionary for Regulatory Activities hierarchy at the system organ class level, reported by investigators at each centre. Outcomes were assessed using negative binomial models.

RESULTS

Of the 7115 hospitalizations reported, the most common reasons were cardiac disorders (23.7%), infections and infestations (15.0%), and nervous system disorders (9.0%). The rate of total ACH was lower in the canagliflozin group (n = 5795) compared with the placebo group (n = 4347): 197.9 versus 215.8 participants per 1000 patient-years, respectively (rate ratio [RR] 0.92; 95% confidence interval [CI] 0.86, 0.98). Canagliflozin reduced the rate of total hospitalizations because of cardiac disorders (RR 0.81; 95% CI 0.75, 0.88). There was no significant difference between the canagliflozin and placebo groups in the rates of total hospitalizations because of infections and infestations (RR 0.96; 95% CI 0.86, 1.02) or nervous system disorders (RR 0.96; 95% CI 0.88, 1.05).

CONCLUSIONS

In the CANVAS programme, the most common reasons for hospitalization were cardiac disorders, infections and infestations, and nervous system disorders. Canagliflozin, compared with placebo, reduced the rate of total ACH.

Effects of routinely collected health information system variables on the readmission of patients with type 2 diabetes

OBJECTIVES

This research explores the association between variables routinely collected in a health information system and the readmission of patients with type 2 diabetes within 30 days of discharge.

METHODS

This retrospective cohort study was conducted at King Fahd Hospital of the University (KFHU) in Al-Khobar, KSA. The study population comprised patients with type 2 diabetes who were admitted to the hospital from January 2016 to November 2016. Data were obtained from the hospital's information system at KFHU. The association between the readmission of patients with type 2 diabetes and routinely collected health information system variables such as demographics, type of diabetes, length of stay, and discharge type were analyzed.

RESULTS

A total of 497 cases met the inclusion criteria. Of these, 31 (6.2%) cases were readmitted within 30 days. Type 2 diabetes was the only variable found to be significantly associated with readmission within 30 days (χ2 (1, N = 497) = 6.116, p = 0.0134). Diabetes type (p = 0.0133) and discharge type (p = 0.0403) were the only variables that displayed significance utilizing a logistic regression model.

CONCLUSION

Overall, the routinely collected demographic, diagnostic, and administrative variables were found to be poor predictors of 30-day readmission for type 2 diabetes at the institution studied. Nonetheless, the only significant variables in the prediction of 30-day readmission were diabetes type and discharge type. To determine the predictors of readmission, it is recommended that future studies include height and weight to the routinely collected health information system variables. We also suggest that future studies be based on data collected over several years or on pooled data collected from several hospitals.

The effect of home care on readmission and mortality rate in patients with diabetes who underwent general surgeries

BACKGROUND

More than one-half of people with diabetes need at least one surgery in their lifespan. Few studies have addressed how to manage the needs of these patients after discharge from the hospital. The present study is designed to determine the effect of home care on readmission of Type 2 diabetic patients who underwent surgical procedures.

MATERIALS AND METHODS

The present study was a randomized clinical trial. Sixty-nine patients with Type 2 diabetes undergoing surgery were assigned to the intervention and control groups via blocking order in the selected educational hospitals of Isfahan 2019. Home care was performed for 3 months with interprofessional team approach. Data collection tools were re-admission checklist. Data were entered in SPSS software version 23 and were analyzed by nonparametric tests.

RESULTS

The background characteristics in the intervention and control groups were not different. The frequency of readmission in the control and intervention groups from the time of discharge until 3 months later was 25.7% and 18.9%, respectively. Frequency of readmission in the intervention and control groups was not significant in 3 months from discharge, P > 0.05. The mortality rate was 11.4% and 0% in control and intervention groups, respectively, P < 0.05.

CONCLUSION

It can be argued that continued home care can decrease the rate of readmission and mortality rate in patients with Type 2 diabetes who will discharge from surgical wards.

Digital interventions to improve safety and quality of inpatient diabetes management: A systematic review

IMPORTANCE

Diabetes is common amongst hospitalised patients and contributes to increased length of stay and poorer outcomes. Digital transformation, particularly the implementation of electronic medical records (EMRs), is rapidly occurring across the healthcare sector and provides an opportunity to improve the safety and quality of inpatient diabetes care. Alongside this revolution has been a considerable and ongoing evolution of digital interventions to optimise care of inpatients with diabetes including optimisation of EMRs, digital clinical decision support systems (CDSS) and solutions utilising data visibility to allow targeted patient review.

OBJECTIVE

To systematically appraise the recent literature to determine which digitally-enabled interventions including EMR, CDSS and data visibility solutions improve the safety and quality of non-critical care inpatient diabetes management.

METHODS

Pubmed, Embase and Cochrane databases were searched for suitable articles. Selected articles underwent quality assessment and analysis with results grouped by intervention type.

RESULTS

1202 articles were identified with 42 meeting inclusion criteria. Four key interventions were identified; computerised physician order entry (n = 4), clinician decision support systems (n = 21), EMR driven active case finding (data visibility solutions) and targeted patient review (n = 10) and multicomponent system interventions (n = 7). Studies reported on glucometric outcomes, evidence-based medication ordering including medication errors, and patient and user outcomes. An improvement in glucometric measures particularly mean blood glucose and proportion of target range blood glucose levels and rates of evidence-based insulin prescribing were consistently demonstrated.

CONCLUSION

Digitally-enabled interventions utilised to improve quality and safety of inpatient diabetes care were heterogenous in design. The majority of studies across all intervention types reported positive effects for evidence-based prescribing and glucometric outcomes. There was less evidence for digital interventions reducing diabetes medication administration errors or impacting patient outcomes (length of stay).

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.

Electronic dashboard-based remote glycemic management program reduces length of stay and readmission rate among hospitalized adults

AIMS/INTRODUCTION

Currently, the impact of hospital-wide glycemic control interventions on length of hospital stay (LOS) and readmission rates are largely unknown. We investigated the impact of a 4-year hospital-wide remote glycemic management program on LOS and 30-day readmission rates among hospitalized adults who received glucose monitoring.

MATERIALS AND METHODS

In this retrospective study, hospitalized patients who received glucose monitoring were classified into groups 1 (high glucose variability), 2 (hypoglycemia), 3 (hyperglycemia) and 4 (relatively stable). The monthly percentage changes, and average monthly percentage changes of hyperglycemia, hypoglycemia and treat to target were determined using joinpoint regression analysis.

RESULTS

A total of 106,528 hospitalized patients (mean age 60.9 ± 18.5 years, 57% men) were enrolled. We observed a significant reduction in the percentage of inpatients in poor glycemic control groups (groups 1, 2 and 3, all P < 0.001), and a reciprocal increase in the relatively stable group (group 4) from 2016 to 2019. We found a significant reduction in LOS by 11.4% (10.5-9.3 days, P = 0.002, after adjustment for age, sex, and admission department). The 30-day readmission rate decreased from 29.9% to 29.3%, mainly among those in group 4 in 2019 (P < 0.001 after adjustment of sex, age, admission department and LOS).

CONCLUSIONS

Improved glycemic control through a hospital-wide electronic remote glycemic management system reduced LOS and 30-day readmission rates. Findings observed in this study might be associated with the reduction in cost of avoidable hospitalizations.

Use of Endocrine Consultation for HbA1c ≥ 9.0% as a Standardized Practice in an Emergency Department Observation Unit

OBJECTIVE

Severely uncontrolled Diabetes Mellitus (DM) is associated with poor long-term outcomes, and may remain unrecognized. A high frequency of uncontrolled DM has been identified in the acute care setting, including the Emergency Department Observation Unit (EDOU). We assess the use of standardized endocrine consultation in the EDOU for Hemoglobin A1c (HbA1c) ≥ 9%.

MATERIALS AND METHODS

Standard practice in our EDOU includes universal HbA1c screening and endocrine consultation for HbA1c ≥ 9.0%. As part of a quality improvement program, EDOU patients with HbA1c ≥ 9.0% had an endocrinology consult. One month follow up phone calls assessed effects of consultation after discharge.

RESULTS

3,688 (95.7%) of 3,853 EDOU patients received an HbA1c test. 7.0% (n=258) were found to have HbA1c ≥ 9% (Mean HbA1c 11.7 ±1.8%; range 9 - 16.6%). Endocrine consults were completed for 190/258 (73.6%) patients with severely uncontrolled DM. Among the 190 patients, 92.1% (n=175) had discharge DM medication adjustments. Known DM patients (n=142) injectable diabetes medication prescriptions increased from 47.2% (67/142) on EDOU arrival to 78.2% (111/142) on discharge. Newly diagnosed DM injectable prescriptions increased from 0% (0/48) on arrival to 72.9% (35/48) on discharge. A total of 72.6% (n=138) were contacted at one-month and 94.9% (n=131) reported taking DM medications compared to 68.2% (n=94) prior to consult.

CONCLUSIONS

HbA1c screening coupled with endocrine consultation for HbA1c ≥ 9.0% was assessed as a performance improvement study and shown to have valuable results. Further study is recommended to determine long-term clinical impact and cost analysis of this novel approach.

The Queensland Inpatient Diabetes Survey (QuIDS) 2019: the bedside audit of practice

OBJECTIVES

To assess the quality of care for patients with diabetes in Queensland hospitals, including blood glucose control, rates of hospital-acquired harm, the incidence of insulin prescription and management errors, and appropriate foot and peri-operative care.

DESIGN, SETTING

Cross-sectional audit of 27 public hospitals in Queensland: four of five tertiary/quaternary referral centres, four of seven large regional or outer metropolitan hospitals, seven of 13 smaller outer metropolitan or small regional hospitals, and 12 of 88 hospitals in rural or remote locations.

PARTICIPANTS

850 adult inpatients with diabetes mellitus in medical, surgical, mental health, high dependency, or intensive care wards.

RESULTS

Twenty-seven of 115 public hospitals that admit acute inpatients participated in the audit, including 4175 of 6652 eligible acute hospital beds in Queensland. A total of 1003 patients had diabetes (24%), and data were collected for 850 (85%). Their mean age was 65.9 years (SD, 15.1 years), 357 were women (42%), and their mean HbA_1c level was 66 mmol/mol (SD, 26 mmol/mol). Rates of good diabetes days (appropriate monitoring, no more than one blood glucose measurement greater than 10 mmol/L, and none below 5 mmol/L) were low in patients with type 1 diabetes (22.1 per 100 patient-days) or type 2 diabetes treated with insulin (40.1 per 100 patient-days); hypoglycaemia rates were high for patients with type 1 diabetes mellitus (24.1 episodes per 100 patient-days). One or more medication errors were identified for 201 patients (32%), including insulin prescribing errors for 127 patients (39%). Four patients with type 1 diabetes experienced diabetic ketoacidosis in hospital (8%); 121 patients (14%) met the criteria for review by a specialist diabetes team but were not reviewed by any diabetes specialist (medical, nursing, allied health).

CONCLUSIONS

We identified several deficits in inpatient diabetes management in Queensland, including high rates of medication error and hospital-acquired harm and low rates of appropriate glycaemic control, particularly for patients treated with insulin. These deficits require attention, and ongoing evaluation of outcomes is necessary.

Effects of a Dedicated Inpatient Diabetes Management Service on Glycemic Control in a Community Hospital Setting

BACKGROUND

Community hospitals account for over 84% of all hospitals and over 94% of hospital admissions in the United States. In academic settings, implementation of an Inpatient Diabetes Management Service (IDMS) model of care has been shown to reduce rates of hyper- and hypoglycemia, hospital length of stay (LOS), and associated hospital costs. However, few studies to date have evaluated the implementation of a dedicated IDMS in a community hospital setting.

METHODS

This retrospective study examined the effects of changing the model of inpatient diabetes consultations from a local, private endocrine practice to a full-time endocrine hospitalist on glycemic control, LOS, and 30-day readmission rates in a 267-bed community hospital.

RESULTS

Overall diabetes patient days for the hospital were similar pre- and post-intervention (20,191 vs 20,262); however, the volume of patients seen by IDMS increased significantly after changing models. Rates of hyperglycemia decreased both among patients seen by IDMS (53.8% to 42.5%, P < .0001) and those not consulted on by IDMS (33.2% to 29.9%; P < .0001). When examined over time, rates of hypoglycemia steadily decreased in the 24 months after dedicated IDMS initiation (P = .02); no such time effect was seen prior to IDMS (P = .34). LOS and 30DRR were not significantly different between IDMS models.

CONCLUSIONS

Implementation of an endocrine hospitalist-based IDMS at a community hospital was associated with significantly decreased hyperglycemia, while avoiding concurrent increases in hypoglycemia. Further studies are needed to investigate whether these effects are associated with improvements in clinical outcomes, patient or staff satisfaction scores, or total cost of care.

Disease-dependent variations in the timing and causes of readmissions in Germany: A claims data analysis for six different conditions

BACKGROUND

Hospital readmissions place a major burden on patients and health care systems worldwide, but little is known about patterns and timing of readmissions in Germany.

METHODS

We used German health insurance claims (AOK, 2011-2016) of patients ≥ 65 years hospitalized for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack, or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type 2 diabetes mellitus, or osteoporosis to identify hospital readmissions within 30 or 90 days. Readmissions were classified into all-cause, specific, and non-specific and their characteristics were analyzed.

RESULTS

Within 30 and 90 days, about 14-22% and 27-41% index admissions were readmitted for any reason, respectively. HF and S/AF contributed most index cases, and HF and COPD accounted for most all-cause readmissions. Distributions and ratios of specific to non-specific readmissions were disease-specific with highest specific readmissions rates among COPD and AMI.

CONCLUSION

German claims are well-suited to investigate readmission causes if longer periods than 30 days are evaluated. Conditions closely related with the primary disease are the most frequent readmission causes, but multiple comorbidities among readmitted cases suggest that a multidisciplinary care approach should be implemented vigorously addressing comorbidities already during the index hospitalization.

Magnitude and predictors of hospital admission, readmission, and length of stay among patients with type 2 diabetes at public hospitals of Eastern Ethiopia: a retrospective cohort study

BACKGROUND

Type 2 Diabetes (T2D) represents one of the leading causes for hospital admissions and outpatient visits. Hence, T2D continuously imposes a significant burden to healthcare systems. The aim of this study was to assess predictors of hospital admission, readmission rates, and length of hospital stay among T2D patients in government hospitals of Eastern Ethiopia from 2013 to 2017.

METHODS

This study utilized retrospective data from a cohort of T2D patients following their treatment in government hospitals in Harari regional state of Ethiopia. Predictor of hospital admission was determined using parametric survival analysis methods. The readmission rate and length of hospital stay were determined by Poisson regression and mixed effect Poisson regression, respectively. All association were performed at 95% confidence level. Significance of association with determinants was reported using the hazard rate for hospital admission, and the incidence rate for readmission and length of hospital stay. Optimal model for each outcome was selected by using information criteria after fitness was checked.

RESULTS

The hospital admission rate for T2D patients was 9.85 (95%CI: 8.32, 11.66) per 1000-person-year observation. Alcohol drinking, inactive lifestyle, being a rural resident, history of comorbidities, and experiencing chronic diabetes complications were predictors of hospital admission. Seventy-one (52.2%) of the admitted patients had a history of readmission. Readmission rate was increased by being female, duration of disease, inactive lifestyle, having BMI greater than 29.9 kg/m2, and higher blood glucose. The median time of hospital stay for admitted patients was 18 (IQR:7). The length of hospital stay was longer among females, patients with the history of insulin administration, and higher blood glucose.

CONCLUSION

Multiple and complex factors were contributing for high diabetes admission and readmission rates as well as for longer in-hospital duration among T2D patients in Harari regional state. Socio-demographic characteristics (sex, place of residence), behavioral factors (alcohol intake, lifestyle), and medical conditions (longer duration of disease, comorbidities, chronic diabetes complications, higher blood glucose level, and treatment modality) were significant determinants of hospital admission, readmission and longer hospital stay among T2D patients.

The effectiveness of in-hospital interventions on reducing hospital length of stay and readmission of patients with Type 2 Diabetes Mellitus: A systematic review

AIM

This review aimed to assess the effectiveness of multifaceted in-hospital interventions for patients with type 2 diabetes mellitus on hospital readmission, hospital length of stay (LOS), and glycated haemoglobin (HbA1c).

METHODS

The search included MEDLINE, EMBASE, Emcare, Web of Science, PsycINFO and Google Scholar from 2007 to current date and restricted to English. The differences in outcome measures were calculated to determine the effectiveness.

RESULTS

The title and abstract of 3251 records were initially screened. Nine studies met the inclusion criteria. Most studies comprised of a wide range of intervention components and outcome measures. The reduction in hospital LOS ranged from 0.5 to 0.8 of a day. Clinically significant improvements in HbA1c concentration levels ranged from a mean reduction of -1.1 (±2.2) mmol/L to -2.8 (±2.7) mmol/L. There were no significant changes in hospital readmission rates and no evidence of the impact of HbA1c on hospital LOS and readmission. Common strategies in reducing hospital LOS and HbA1c were a dedicated care team, hospital wide approach, quality improvement focus, insulin therapy, early short-term intensive program, transition to primary care physicians, and on-going outpatient follow-up for at least 6-12 months.

CONCLUSIONS

The findings illustrate that multifaceted in-hospital intervention for patients diagnosed with type 2 diabetes can contribute to improvements in hospital LOS and HbA1c concentration.

Racial Disparities in Post-Acute Home Health Care Referral and Utilization among Older Adults with Diabetes

Racial and ethnic disparities exist in diabetes prevalence, health services utilization, and outcomes including disabling and life-threatening complications among patients with diabetes. Home health care may especially benefit older adults with diabetes through individualized education, advocacy, care coordination, and psychosocial support for patients and their caregivers. The purpose of this study was to examine the association between race/ethnicity and hospital discharge to home health care and subsequent utilization of home health care among a cohort of adults (age 50 and older) who experienced a diabetes-related hospitalization. The study was limited to patients who were continuously enrolled in Medicare for at least 12 months and in the United States. The cohort (n = 786,758) was followed for 14 days after their diabetes-related index hospitalization, using linked Medicare administrative, claims, and assessment data (2014–2016). Multivariate logistic regression models included patient demographics, comorbidities, hospital length of stay, geographic region, neighborhood deprivation, and rural/urban setting. In fully adjusted models, hospital discharge to home health care was significantly less likely among Hispanic (OR 0.8, 95% CI 0.8–0.8) and American Indian (OR 0.8, CI 0.8–0.8) patients compared to White patients. Among those discharged to home health care, all non-white racial/ethnic minority patients were less likely to receive services within 14-days. Future efforts to reduce racial/ethnic disparities in post-acute care outcomes among patients with a diabetes-related hospitalization should include policies and practice guidelines that address structural racism and systemic barriers to accessing home health care services.

Facilitators and barriers to the provision of type 1 diabetes inpatient care: An interpretive phenomenological analysis

AIM

The aim and objective of this study was to understand how non-specialized nurses understand the possible barriers and facilitators of inpatient care for type 1 diabetes.

DESIGN

An interpretative phenomenology approach was conducted.

METHODS

The sample consisted of non-specialized nurses (N = 24) working in medical, surgical and nephrology wards in the state hospitals in Cyprus. The data were collected during 2016-2018 from one focus group with nurses (N = 6) and individual semi-structured interviews with nurses (N = 18) conducted. The Standards for Reporting Qualitative Research checklist used to ensure the quality of the study.

RESULTS

It is evident from the study findings that nurses experience several barriers in diabetes inpatient care reported which are of great concern since this could have adverse effects on patients' outcomes. Only one facilitator has been reported by few nurses.

The Financial Impact of an Inpatient Diabetes Management Service

CONTEXT

Diabetes is a leading metabolic disorder with a substantial cost burden, especially in inpatient settings. The complexity of inpatient glycemic management has led to the emergence of inpatient diabetes management service (IDMS), a multidisciplinary team approach to glycemic management.

OBJECTIVE

To review recent literature on the financial and clinical impact of IDMS in hospital settings.

METHODS

We searched PubMed using a combination of controlled vocabulary and keyword terms to describe the concept of IDMS and combined the search terms with a comparative effectiveness filter for costs and cost analysis developed by the National Library of Medicine.

FINDINGS

In addition to several improved clinical endpoints such as glycemic management outcomes, IDMS implementation is associated with hospital cost savings through decreased length of stay, preventing hospital readmissions, hypoglycemia reduction, and optimizing resource allocation. There are other downstream potential cost savings in long-term patient health outcomes and avoidance of litigation related to suboptimal glycemic management.

CONCLUSION

IDMS may play an important role in helping both academic and community hospitals to improve the quality of diabetes care and reduce costs. Clinicians and policymakers can utilize existing literature to build a compelling business case for IDMS to hospital administrations and state legislatures in the era of value-based healthcare.

The impact of diabetology consultations on length of stay in hospitalized patients with diabetes

Introduction

Both hyperglycaemia and hypoglycaemia in hospitalized patients have been shown to be associated with a longer length of stay, higher readmission rates, and higher rates of morbidity and mortality. With 25%-30% of all hospitalized patients carrying a diagnosis of diabetes, it is important to optimize glycaemic control. Current guidelines for care of inpatients with diabetes now suggest consulting a specialized diabetes team for all patients when possible.

Aim

This study was a retrospective cohort study to evaluate the impact of an inpatient diabetology consult within 48 hours of admission on patients' length of stay.

Methods

All patients admitted to the general medicine service between 2013 and 2018 with a diagnosis of diabetes in their medical record were included, which consisted of 11 477 inpatient stays. We looked at the effect of an inpatient diabetology consultation within the first 48 hours on length of stay, complications and 30-day readmission rates.

Results

We found that patients whose care included a diabetology consult within 48 hours of admission had a statistically significant shorter length of stay by 1.56 days compared to the remainder of the group. There was no difference in complications or 30-day readmission rates between the groups.

Conclusion

Among general medicine patients with a diagnosis of diabetes, timely diabetology consultations reduced patients' length of stay and have the potential to improve their care and lessen the economic impact.

15. Diabetes Care in the Hospital: Standards of Medical Care in Diabetes-2021

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-020-01162-0.

Quality improvement project for improving inpatient glycaemic control in non-critically ill patients admitted on medical floor with type 2 diabetes mellitus

A significant number of patients admitted to the medical floor have type 2 diabetes mellitus (DM). Lack of a standardised inpatient hyperglycaemia management protocol leads to improper glycaemic control adding to morbidity in such patients. American Diabetes Association, in its 2019 guidelines, recommends initiation of a regimen consisting of basal insulin (long-acting insulin) or basal plus correctional insulin for non-critically ill hospitalised patients with poor or no oral intake. A combination of basal insulin, bolus (short-acting premeal or prandial) insulin and correctional scale insulin is recommended for inpatient hyperglycaemia management in non-critical patients with type 2 DM who have proper oral intake. Baseline data of 100 patients with diabetes admitted to Hamad General Hospital Doha, Qatar, showed that although insulin was used in the majority of patients, there was lack of uniformity in the initiation of insulin regimen. Adequate glycaemic control (7.8–10 mmol/L) was achieved in 45% of patients. Using Plan–Do–Study–Act (PDSA) model of improvement, a quality improvement project was initiated with the introduction of a standardised inpatient hyperglycaemia management protocol aiming to achieve 50% compliance to protocol and improvement in inpatient glycaemic control from baseline of 45% to 70%. Interventions for change included development of a standardised inpatient hyperglycaemia management protocol and its provision to medical trainees, teaching sessions for trainees and nurses, active involvement of medical consultants for supervision of trainees to address the fear of hypoglycaemia, regular reminders/feedbacks to trainees and nurses about glycaemic control of their patients and education about goals of diabetes management during hospitalisation for patients with diabetes. Overall, glycaemic control improved significantly with target glycaemic control of 70% achieved in 4 of the 10 PDSA cycles without an increase in the number of hypoglycaemic episodes. We conclude that development of a standardised inpatient insulin prescribing protocol, educational sessions for medical trainees and nurses about goals of diabetes management during hospitalisation, regular reminders to healthcare professionals and patient education are some of the measures that can improve glycaemic control of patients with type 2 DM during inpatient stay.

The Early Impact of the Centers for Medicare & Medicaid Services State Innovation Models Initiative on 30-Day Hospital Readmissions Among Adults With Diabetes

BACKGROUND

The Centers for Medicare & Medicaid Services (CMS) State Innovation Models (SIM) Initiative funds states to accelerate delivery system and payment reforms. All SIM states focus on improving diabetes care, but SIM's effect on 30-day readmissions among adults with diabetes remains unclear.

METHODS

A quasi-experimental research design estimated the impact of SIM on 30-day hospital readmissions among adults with diabetes in 3 round 1 SIM states (N=671,996) and 3 comparison states (N=2,719,603) from 2010 to 2015. Difference-in-differences multivariable logistic regression models that incorporated 4-group propensity score weighting were estimated. Heterogeneity of SIM effects by grantee state and for CMS populations were assessed.

RESULTS

In adjusted difference-in-difference analyses, SIM was associated with an increase in odds of 30-day hospital readmission among patients in SIM states in the post-SIM versus pre-SIM period relative to the ratio in odds of readmission among patients in the comparison states post-SIM versus pre-SIM (ratio of adjusted odds ratio=1.057, P=0.01). Restricting the analyses to CMS populations (Medicare and Medicaid beneficiaries), resulted in consistent findings (ratio of adjusted odds ratio=1.057, P=0.034). SIM did not have different effects on 30-day readmissions by state.

CONCLUSIONS

We found no evidence that SIM reduced 30-day readmission rates among adults with diabetes during the first 2 years of round 1 implementation, even among CMS beneficiaries. It may be difficult to reduce readmissions statewide without greater investment in health information exchange and more intensive use of payment models that promote interorganizational coordination.

Views of People with Diabetes Regarding Their Experiences of the Facilitators and Barriers in Type 1 Diabetes Inpatient Care: An Interpretative Phenomenological Analysis

BACKGROUND

The aim of this study was to comprehend how people with diabetes view their experiences of the possible barriers and facilitators in inpatient care for type 1 diabetes from non-specialized nurses.

DESIGN

An interpretative phenomenology analysis (IPA) was conducted.

METHODS

The sample consisted of people with type 1 diabetes 1 (n = 24) who use the services of the state hospitals in Cyprus. The data were collected in two phases: firstly, focus groups with people with diabetes (n = 2) were conducted and analysed, and then individual semi-structured interviews with people with diabetes (n = 12) were conducted.

RESULTS

It is evident from the findings that people with diabetes experienced several barriers in diabetes inpatient care, which is concerning since this can have adverse effects on patients' outcomes. No facilitators were reported.

CONCLUSION

Significant results were found in relation to the barriers to diabetes inpatient care. Crucially, the findings demonstrate that all these factors can negatively affect the quality of care of patients with diabetes, and most of these factors are related not only to diabetes care but also generally to all patients who receive inpatient care. Interestingly, no participant reported any facilitators to their care, which further affected the negative perceptions of the care received.

Application of standardised effect sizes to hospital discharge outcomes for people with diabetes

BACKGROUND

Patients with diabetes are at an increased risk of readmission and mortality when discharged from hospital. Existing research identifies statistically significant risk factors that are thought to underpin these outcomes. Increasingly, these risk factors are being used to create risk prediction models, and target risk modifying interventions. These risk factors are typically reported in the literature accompanied by unstandardized effect sizes, which makes comparisons difficult. We demonstrate an assessment of variation between standardised effect sizes for such risk factors across care outcomes and patient cohorts. Such an approach will support development of more rigorous risk stratification tools and better targeting of intervention measures.

METHODS

Data was extracted from the electronic health record of a major tertiary referral centre, over a 3-year period, for all patients discharged from hospital with a concurrent diagnosis of diabetes mellitus. Risk factors selected for extraction were pre-specified according to a systematic review of the research literature. Standardised effect sizes were calculated for all statistically significant risk factors, and compared across patient cohorts and both readmission & mortality outcome measures.

RESULTS

Data was extracted for 46,357 distinct admissions patients, creating a large dataset of approximately 10,281,400 data points. The calculation of standardized effect size measures allowed direct comparison. Effect sizes were noted to be larger for mortality compared to readmission, as well as for being larger for surgical and type 1 diabetes cohorts of patients.

CONCLUSIONS

The calculation of standardised effect sizes is an important step in evaluating risk factors for healthcare events. This will improve our understanding of risk and support the development of more effective risk stratification tools to support patients to make better informed decisions at discharge from hospital.

Type 2 Diabetes: A Risk Factor for Hospital Readmissions and Mortality in Australian Patients With Cirrhosis

Although there is evidence that type 2 diabetes mellitus (T2D) impacts adversely on liver‐related mortality, its influence on hospital readmissions and development of complications in patients with cirrhosis, particularly in alcohol‐related cirrhosis (the most common etiological factor among Australian hospital admissions for cirrhosis) has not been well studied. This study aimed to investigate the association between T2D and liver cirrhosis in a population‐based cohort of patients admitted for cirrhosis in the state of Queensland, Australia. A retrospective cohort analysis was conducted using data from the Queensland Hospital Admitted Patient Data Collection, which contains information on all hospital episodes of care for patients with liver cirrhosis, and the Death Registry during 2008‐2017. We used demographic, clinical data, and socioeconomic characteristics. A total of 8,631 patients were analyzed. A higher proportion of patients with T2D had cryptogenic cirrhosis (42.4% vs. 27.3%, respectively; P < 0.001) or nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (13.8% vs. 3.4%, respectively; P < 0.001) and an admission for hepatocellular carcinoma (18.0% vs. 12.2%, respectively; P < 0.001) compared to patients without T2D. Patients with liver cirrhosis with T2D compared to those without T2D had a significantly increased median length of hospital stay (6 [range, 1‐11] vs. 5 [range, 1‐11] days, respectively; P < 0.001), double the rate of noncirrhosis‐related admissions (incidence rate ratios [IRR], 2.03; 95% confidence interval [CI], 1.98‐2.07), a 1.35‐fold increased rate of cirrhosis‐related admissions (IRR, 1.35; 95% CI, 1.30‐1.41), and significantly lower survival (P < 0.001). Conclusion: Among hospitalized patients with cirrhosis, the cohort with T2D is at higher risk and may benefit from attention to comorbidities and additional support to reduce readmissions.

Association of glucose variability at the last day of hospitalization with 30-day readmission in adults with diabetes

OBJECTIVE

To evaluate whether increased glucose variability (GV) during the last day of inpatient stay is associated with increased risk of 30-day readmission in patients with diabetes.

RESEARCH DESIGN AND METHODS

A comprehensive list of clinical, pharmacy and utilization files were obtained from the Veterans Affairs (VA) Central Data Warehouse to create a nationwide cohort including 1 042 150 admissions of patients with diabetes over a 14-year study observation period. Point-of-care glucose values during the last 24 hours of hospitalization were extracted to calculate GV (measured as SD and coefficient of variation (CV)). Admissions were divided into 10 categories defined by progressively increasing SD and CV. The primary outcome was 30-day readmission rate, adjusted for multiple covariates including demographics, comorbidities and hypoglycemia.

RESULTS

As GV increased, there was an overall increase in the 30-day readmission rate ratio. In the fully adjusted model, admissions with CV in the 5th-10th CV categories and admissions with SD in the 4th-10th categories had a statistically significant progressive increase in 30-day readmission rates, compared with admissions in the 1st (lowest) CV and SD categories. Admissions with the greatest CV and SD values (10th category) had the highest risk for readmission (rate ratio (RR): 1.08 (95% CI 1.05 to 1.10), p<0.0001 and RR: 1.11 (95% CI 1.09 to 1.14), p<0.0001 for CV and SD, respectively).

CONCLUSIONS

Patients with diabetes who exhibited higher degrees of GV on the final day of hospitalization had higher rates of 30-day readmission.

TRIAL REGISTRATION NUMBER

NCT03508934, NCT03877068.

Closed-loop management of inpatient hyperglycaemia

The prevalence of diabetes in the inpatient setting is increasing, and suboptimal glucose control in hospital is associated with increased morbidity and mortality. Attaining the recommended glucose levels is challenging with standard insulin therapy. Hypoglycaemia and hyperglycaemia are common and diabetes management in hospital can be a considerable workload burden for health-care professionals. Fully automated insulin delivery (closed-loop) has been shown to be safe, and achieves superior glucose control than standard insulin therapy in the hospital, including in those patients receiving haemodialysis and enteral or parenteral nutrition where glucose control can be particularly challenging. Evidence that the improved glucose control achieved using closed-loop systems can translate into improved clinical outcomes for patients is key to support widespread adoption of this technology. The closed-loop approach has the potential to provide a paradigm shift in the management of inpatient diabetes, particularly in the most challenging inpatient populations, and may reduce staff work burden and the health-care costs associated with inpatient diabetes.

Impact of the Diabetes Inpatient Care and Education (DICE) project on length of stay and mortality

AIM

To determine whether the Diabetes Inpatient Care and Education (DICE) programme, a whole-systems approach to managing inpatient diabetes, reduces length of stay, in-hospital mortality and readmissions.

RESEARCH DESIGN AND METHODS

Diabetes Inpatient Care and Education initiatives included identification of all diabetes admissions, a novel DICE care-pathway, an online system for prioritizing referrals, use of web-linked glucose meters, an enhanced diabetes team, and novel diabetes training for doctors. Patient administration system data were extracted for people admitted to Ipswich Hospital from January 2008 to June 2016. Logistic regression was used to compare binary outcomes (mortality, 30-day readmissions) 6 months before and after the intervention; generalized estimating equations were used to compare lengths of stay. Interrupted time series analysis was performed over the full 7.5-year period to account for secular trends.

RESULTS

Before-and-after analysis revealed a significant reduction in lengths of stay for people with and without diabetes: relative ratios 0.89 (95% CI 0.83, 0.97) and 0.93 (95% CI 0.90, 0.96), respectively; however, in interrupted time series analysis the change in long-term trend for length of stay following the intervention was significant only for people with diabetes (P=0.017 vs P=0.48). Odds ratios for mortality were 0.63 (0.48, 0.82) and 0.81 (0.70, 0.93) in people with and without diabetes, respectively; however, the change in trend was not significant in people with diabetes, while there was an apparent increase in those without diabetes. There was no significant change in 30-day readmissions, but interrupted time series analysis showed a rising trend in both groups.

CONCLUSION

The DICE programme was associated with a shorter length of stay in inpatients with diabetes beyond that observed in people without diabetes.

Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Several deep learning architectures making use of attention mechanisms, recurrent layers, neural ordinary differential equations (ODEs), and medical concept embeddings with time-aware attention were trained using publicly available electronic medical record data (MIMIC-III) associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was used to compute the posterior over weights of an attention-based model. Odds ratios associated with an increased risk of readmission were computed for static variables. Diagnoses, procedures, medications, and vital signs were ranked according to the associated risk of readmission. A recurrent neural network, with time dynamics of code embeddings computed by neural ODEs, achieved the highest average precision of 0.331 (AUROC: 0.739, F₁-Score: 0.372). Predictive accuracy was comparable across neural network architectures. Groups of patients at risk included those suffering from infectious complications, with chronic or progressive conditions, and for whom standard medical care was not suitable. Attention-based networks may be preferable to recurrent networks if an interpretable model is required, at only marginal cost in predictive accuracy.

A Dashboard Prototype for Tracking the Impact of Diabetes on Hospital Readmissions Using a National Administrative Database

Background

Over the past several decades, diabetes mellitus has contributed to a significant disease burden in the general population. Evidence suggests that patients with a coexisting diabetes diagnosis consume more hospital resources, and have higher readmission rates compared to those who do not. Against the backdrop of bundled-payment programs, healthcare systems cannot underestimate the importance of monitoring patient health information at the population level.

Methods

Using the data from the Centers for Medicare and Medicaid Services (CMS) administrative claims database, we created a dashboard prototype to enable hospitals to examine the impact of diabetes on their all-cause readmission rates and financial implications if diabetes was present at the index hospitalization. The technical design involved loading the relevant 10th revision of International Classification of Diseases (ICD-10) codes provided by the medical team and flagging diabetes patients at the claim. These patients were then tagged for readmissions within the same database. The odds ratios were determined based on data from two groups: those with diabetes at index hospitalization which include type 1 only, type 2 only, and type 1 and type 2 diabetes, plus those without diabetes at index hospitalization.

Results

The dashboard presents summary data of diabetes readmissions quality metrics at a national level. Users can visualize summary data of each state and compare odds ratios for readmissions as well as raw hospitalization data at their facility. Dashboard users can also view data classified by a diagnosis-related group (DRG) system. In addition to a “national” data view, for users who inquire about data specific to demographic regions, the DRG view can be further stratified at the state level or county level. At the DRG level, users can view data about the cost per readmissions for all index hospitalization with and without diabetes.

Conclusions

The dashboard prototype offers users a virtual interface which displays visual data for quick interpretation, monitors changes at a population level, and enables administrators to benchmark facility data against local and national trends. This is an important step in using data analytics to drive population level decision making to ultimately improve medical systems.

15. Diabetes Care in the Hospital: Standards of Medical Care in Diabetes-2020

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc20-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc20-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.