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

Transitions of Care From Hospital to Home: Can Continuous Glucose Monitoring Improve Outcomes for Patients With Diabetes?

PURPOSE

The purpose of this study was to examine the impact of continuous blood glucose monitoring (CGM) on transitions of care as patients with diabetes are discharged from the hospital on insulin.

METHODS

This is a descriptive study with 2 cohorts of patients (transition to home with CGM and transition to home without CGM) who were assessed prior to discharge (baseline) and 30 days post discharge (follow-up). The key outcome measures were satisfaction with diabetes management, diabetes-related quality of life, frequency of blood glucose monitoring, and 30-day readmission rates.

RESULTS

Patients in the CGM group reported significantly higher levels of satisfaction with diabetes self-care management and higher levels of diabetes-related quality of life compared to those patients discharged without CGM.

CONCLUSION

The results of this study suggest that CGM enables a smoother transition from hospital to home for patients with diabetes placed on insulin at discharge. CGM was associated with higher satisfaction and diabetes-related quality of life, perhaps as a result of timely, ongoing information about glucose levels without the burden and pain of finger sticks. CGM may provide greater confidence in self-care decisions regarding insulin dosing, food intake, and exercise. Further research is needed to confirm our results and explore the additional factors associated with greater quality of life and satisfaction.

SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes

PURPOSE

Poor medication adherence (MA) is linked to an increased likelihood of hospital admission. Early interventions to address MA may reduce this risk and associated health-care costs. This study aimed to evaluate a holistic Patient Reported Outcome Measure (PROM) of MA, known as SPUR, as a predictor of general admission and early readmission in patients living with Type 2 Diabetes.

PATIENTS AND METHODS

An observational study design was used to assess data collected over a 12-month period including 6-month retrospective and 6-month prospective monitoring of the number of admissions and early readmissions (admissions occurring within 30 days of discharge) across the cohort. Patients (n = 200) were recruited from a large South London NHS Trust. Covariates of interest included: age, ethnicity, gender, level of education, income, the number of medicines and medical conditions, and a Covid-19 diagnosis. A Poisson or negative binomial model was employed for count outcomes, with the exponentiated coefficient indicating incident ratios (IR) [95% CI]. For binary outcomes (Coefficient, [95% CI]), a logistic regression model was developed.

RESULTS

Higher SPUR scores (increased adherence) were significantly associated with a lower number of admissions (IR = 0.98, [0.96, 1.00]). The number of medical conditions (IR = 1.07, [1.01, 1.13]), age ≥80 years (IR = 5.18, [1.01, 26.55]), a positive Covid-19 diagnosis during follow-up (IR = 1.83, [1.11, 3.02]) and GCSE education (IR = 2.11, [1.15,3.87]) were factors associated with a greater risk of admission. When modelled as a binary variable, only the SPUR score (-0.051, [-0.094, -0.007]) was significantly predictive of an early readmission, with patients reporting higher SPUR scores being less likely to experience an early readmission.

CONCLUSION

Higher levels of MA, as determined by SPUR, were significantly associated with a lower risk of general admissions and early readmissions among patients living with Type 2 Diabetes.

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.

Predicting and Validating 30-day Hospital Readmission in Adults With Diabetes Whose Index Admission Is Diabetes-related

OBJECTIVE

The primary objective is to develop a prediction model of 30-day hospital readmission among adults with diabetes mellitus (DM) whose index admission was DM-related. The secondary aims are to internally and externally validate the prediction model and compare its performance with 2 existing models.

RESEARCH DESIGN AND SETTING

Data of inpatients aged ≥ 18 years from 2008 to 2015 were extracted from the electronic medical record system of the National University Hospital, Singapore. Unplanned readmission within 30 days was calculated from the discharge date of the index hospitalization. Multivariable logistic regression and 10-fold cross-validation were performed. For external validation, simulations based on prevalence of 30-day readmission, and the regression coefficients provided by referenced papers were conducted.

RESULTS

Eleven percent of 2355 patients reported 30-day readmission. The prediction model included 4 predictors: length of stay, ischemic heart disease, peripheral vascular disease, and number of drugs. C-statistics for the prediction model and 10-fold cross-validation were 0.68 (95% CI 0.66, 0.70) and 0.67 (95% CI 0.63 to 0.70), respectively. Those for the 3 simulated external validation data sets ranged from 0.64 to 0.68.

CONCLUSION

The prediction model performs well with good internal and external validity for identifying patients with DM at risk of unplanned 30-day readmission.

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).

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.

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.

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.

Association between glycosylated haemoglobin and outcomes for patients discharged from hospital with diabetes: A health informatics approach

Aims/Objectives

Extensive research considers associations between inpatient glycaemic control and outcomes during hospital admission; this cautions against overly tight glycaemic targets. Little research considers glycaemic control following hospital discharge. This is despite a clear understanding that people with diabetes are at increased risk of negative outcomes, following discharge. We evaluate absolute and relative Hba1c values, and frequency of Hba1c monitoring, on readmission and mortality rates for people discharged from hospital with diabetes.

Methods

All discharges (n = 46,357) with diabetes from a major tertiary referral centre over 3 years were extracted, including biochemistry data. We conducted an evaluation of association between Hba1c, mortality and readmission, statistical significance and standardised Cohen's D effect size calculations.

Results

399 patients had a Hba1c performed during their admission. 3,138 patients had a Hba1c within 1 year of discharge. Mean average Hba1c for readmissions was 57.82 vs 60.39 for not readmitted (p = 0.009, Cohen's D 0.28). Mean average number of days to Hba1c testing in readmitted was 97 vs 113 for those not readmitted (p = 0.00006, Cohen's D 0.39). Further evaluation of mortality outcomes, cohorts of T1DM and T2DM and association of relative change in Hba1c was performed.

Conclusions

Lower Hba1c values following discharge from hospital are significantly associated with increased risk of readmission, as is a shorter duration until testing. Similar patterns observed for mortality. Findings particularly prominent for T1DM. Further research needed to consider underlying causation and design of appropriate risk stratification models.

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.

Predictors of 30-day unplanned hospital readmission among adult patients with diabetes mellitus: a systematic review with meta-analysis

Adult patients with diabetes mellitus (DM) represent one-fifth of all 30-day unplanned hospital readmissions but some may be preventable through continuity of care with better DM self-management. We aim to synthesize evidence concerning the association between 30-day unplanned hospital readmission and patient-related factors, insurance status, treatment and comorbidities in adult patients with DM. We searched full-text English language articles in three electronic databases (MEDLINE, Embase and CINAHL) without confining to a particular publication period or geographical area. Prospective and retrospective cohort and case-control studies which identified significant risk factors of 30-day unplanned hospital readmission were included, while interventional studies were excluded. The study participants were aged ≥18 years with either type 1 or 2 DM. The random effects model was used to quantify the overall effect of each factor. Twenty-three studies published between 1998 and 2018 met the selection criteria and 18 provided information for the meta-analysis. The data were collected within a period ranging from 1 to 15 years. Although patient-related factors such as age, gender and race were identified, comorbidities such as heart failure (OR=1.81, 95% CI 1.67 to 1.96) and renal disease (OR=1.69, 95% CI 1.34 to 2.12), as well as insulin therapy (OR=1.45, 95% CI 1.24 to 1.71) and insurance status (OR=1.41, 95% CI 1.22 to 1.63) were stronger predictors of 30-day unplanned hospital readmission. The findings may be used to target DM self-management education at vulnerable groups based on comorbidities, insurance type, and insulin therapy.

Identifying Common Predictors of Multiple Adverse Outcomes Among Elderly Adults With Type-2 Diabetes

OBJECTIVE

As part of a multidisciplinary team managing patients with type-2 diabetes, pharmacists need a consistent approach of identifying and prioritizing patients at highest risk of adverse outcomes. Our objective was to identify which predictors of adverse outcomes among type-2 diabetes patients were significant and common across 7 outcomes and whether these predictors improved the performance of risk prediction models. Identifying such predictors would allow pharmacists and other health care providers to prioritize their patient panels.

RESEARCH DESIGN AND METHODS

Our study population included 120,256 adults aged 65 years or older with type-2 diabetes from a large integrated health system. Through an observational retrospective cohort study design, we assessed which risk factors were associated with 7 adverse outcomes (hypoglycemia, hip fractures, syncope, emergency department visit or hospital admission, death, and 2 combined outcomes). We split (50:50) our study cohort into a test and training set. We used logistic regression to model outcomes in the test set and performed k-fold validation (k=5) of the combined outcome (without death) within the validation set.

RESULTS

The most significant predictors across the 7 outcomes were: age, number of medicines, prior history of outcome within the past 2 years, chronic kidney disease, depression, and retinopathy. Experiencing an adverse outcome within the prior 2 years was the strongest predictor of future adverse outcomes (odds ratio range: 4.15-7.42). The best performing models across all outcomes included: prior history of outcome, physiological characteristics, comorbidities and pharmacy-specific factors (c-statistic range: 0.71-0.80).

CONCLUSIONS

Pharmacists and other health care providers can use models with prior history of adverse event, number of medicines, chronic kidney disease, depression and retinopathy to prioritize interventions for elderly patients with type-2 diabetes.

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.

VALIDATION AND COMPARISON OF TWO 30-DAY RE-ADMISSION PREDICTION MODELS IN PATIENTS WITH DIABETES

Objective: The objective was to evaluate the 30-day re-admission predictive performance of the HOSPITAL score and Diabetes Early Re-admission Risk Indicator (DERRI^™) in hospitalized diabetes patients. Methods: This was a case-control study in an academic, tertiary center in the United States. Adult hospitalized diabetes patients were randomly identified between January 1, 2014, and September 30, 2017. Patients were categorized into two groups: (1) re-admitted within 30 days, and (2) not re-admitted within 30 days. Predictive performance of the HOSPITAL and DERRI^™ scores was evaluated by calculating receiver operating characteristics curves (c-statistic), Hosmer-Lemeshow goodness-of-fit tests, and Brier scores. Results: A total of 200 patients were included (100 re-admitted, 100 non-re-admitted). The HOSPITAL score had a c-statistic of 0.731 (95% confidence interval [CI], 0.661 to 0.800), Hosmer-Lemeshow test P = .211, and Brier score 0.212. The DERRI^™ score had a c-statistic of 0.796 (95% CI, 0.734 to 0.857), Hosmer-Lemeshow test P = .114, and Brier score 0.212. The difference in receiver operating characteristic curves was not statistically significant between the two scores but showed a higher c-statistic with the DERRI^™ score (P = .055). Conclusion: Both HOSPITAL and DERRI^™ scores showed good predictive performance in 30-day re-admission of adult hospitalized diabetes patients. There was no significant difference in discrimination and calibration between the scores. Abbreviations: CI = confidence interval; DERRI^™ = Diabetes Early Re-admission Risk Indicator; IQR = interquartile range.

Risk factors for readmission of inpatients with diabetes: A systematic review

AIM

We have limited understanding of which risk factors contribute to increased readmission rates amongst people discharged from hospital with diabetes. We aim to complete the first review of its kind, to identify, in a systematic way, known risk factors for hospital readmission amongst people with diabetes, in order to better understand this costly complication.

METHOD

The review was prospectively registered in the PROSPERO database. Risk factors were identified through systematic review of literature in PubMed, EMBASE & SCOPUS databases, performed independently by two authors prior to data extraction, with quality assessment and semi-quantitative synthesis according to PRISMA guidelines.

RESULTS

Eighty-three studies were selected for inclusion, predominantly from the United States, and utilising retrospective analysis of local or regional data sets. 76 distinct statistically significant risk factors were identified across 48 studies. The most commonly identified risk factors were; co-morbidity burden, age, race and insurance type. Few studies conducted power calculations; unstandardized effect sizes were calculated for the majority of statistically significant risk factors.

CONCLUSION

This review is important in assessing the current state of the literature and in supporting development of interventions to reduce readmission risk. Furthermore, it provides an important foundation for development of rigorous, pre-specified risk prediction models.

Strategies to Prevent Readmission in High-Risk Patients with Diabetes: the Importance of an Interdisciplinary Approach

PURPOSE OF REVIEW

Patients with diabetes are known to have higher 30-day readmission rates compared to the general inpatient population. A number of strategies have been shown to be effective in lowering readmission rates.

RECENT FINDINGS

A review of the current literature revealed several strategies that have been associated with a decreased risk of readmission in high-risk patients with diabetes. These strategies include inpatient diabetes survival skills education and medication reconciliation prior to discharge to send the patient home with the "right" medications. Other key strategies include scheduling a follow-up phone call soon after discharge and an office visit to adjust the diabetes regimen. The authors identified the most successful strategies to reduce readmissions as well as some institutional barriers to following a transitional care program. Recent studies have identified risk factors in the diabetes population that are associated with an increased risk of readmission as well as interventions to lower this risk. A standardized transitional care program that focuses on providing interventions while reducing barriers to implementation can contribute to a decreased risk of readmission.