Diagnosing diabetes and preventing rehospitalizations: the urban diabetes study

Outcomes of the Dulce Digital-COVID Aware (DD-CA) discharge texting platform for US/Mexico border Hispanic individuals with diabetes

BACKGROUND

Hispanic individuals have higher type 2 diabetes (T2D) prevalence, poorer outcomes, and are disproportionately affected by COVID-19. Culturally-tailored, diabetes educational text messaging has previously improved HbA1c in this population.

METHODS

During the pandemic, hospitalized Hispanic adults with T2D (N = 172) were randomized to receive Dulce Digital-COVID Aware ("DD-CA") texting platform upon discharge plus diabetes transition service (DTS) or DTS alone. DD-CA includes diabetes educational messaging with additional COVID-safe messaging (e.g., promoting masking; social distancing; vaccination).

FINDINGS

Among adults with poorly-controlled diabetes (Mean HbA1c = 9.6 ± 2.2 %), DD-CA did not reduce 30- or 90-day readmissions compared to standard care (28 % vs 15 %, p = .06; 37 % vs 35 %, p = .9, respectively). However, the improvement in HbA1c was larger among those in the DD-CA compared to DTS at 3 months (n = 56; -2.69 % vs. -1.45 %, p = .0496) with reduced effect at 6 months (n = 64; -2.03 % vs -0.91 %, p = .07). Low follow-up completion rates and the addition of covariates (to control for baseline group differences that existed despite randomization) impacted statistical power.

INTERPRETATION

During the pandemic, DD-CA offered an alternative digital approach to diabetes and COVID education and support for a high-risk Hispanic population and achieved trends toward improvement in glycemic control despite relatively low engagement and not reducing hospital readmissions.

Measures of type 2 diabetes burden in Italy assessed using the AMD dataset over a twelve year span across the Great Recession

Patients with Type 2 Diabetes Mellitus (T2DM) are rapidly increasing in Italy due to aging, preventable risk factors, and worsening socioeconomic context. T2DM and its sequelae take a heavy toll on healthcare systems and the economy, given costly management, difficulties in coping with everyday life, and decreasing patient/worker productivity. Considering long life expectancy in Italy and a decreasing mortality rate due to T2DM, this study aims to calculate the years lived with disability (YLDs) of T2DM and its sequelae grouped into three categories: Neuropathy, Chronic Kidney Disease and No Complications, taking into consideration sex, year, and geographical location. This is the first attempt to measure YLDs from data that do not rely on self-reported diabetes diagnoses. Data come from the Italian Diabetologists Association dataset, the most comprehensive longitudinal source of national outpatient data. YLDs are obtained by multiplying the number of individuals living with a specific health condition and a disability weight which represents the magnitude of health loss associated with that particular condition. Findings show increasing YLD age-standardized rates for T2DM and its sequelae, especially Neuropathy, with the trend being stronger in the central macro-region and among men, and that 2009 marks a structural change in YLD growth rate. Systematic data collection for measuring the burden of diseases is key, among other things, to policy-making and implementation.

Preventing all-cause hospitalizations in type 2 diabetes with sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists: A narrative review and proposed clinical approach

Patients with type 2 diabetes (T2D) are at increased risk for hospital admissions, and acute hospitalizations are associated with a worse prognosis. However, outcomes related to all-cause hospital admissions (ACHAs) were often overlooked in trials that demonstrated the cardiovascular and kidney benefits of sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1RAs). This review includes a contemporary literature summary of emerging data regarding the effects of SGLT2 inhibitors and GLP-1RAs on ACHAs. The role of SGLT2 inhibitors in preventing ACHAs was shown in exploratory investigations of several randomized controlled trials (RCTs) and was further supported by real-world evidence (RWE). However, the association between GLP-1RA use and lower ACHA risk was mainly shown through RWE, with minimal available RCT data. We also discuss the advantages and challenges of studying ACHAs. Finally, we propose an easily memorized ("ABCDE" acronym) clinical approach to evaluating T2D status and treatment in admitted patients, as they transition from hospital to community care. This systematic approach may assist clinicians in recognizing possible pitfalls in T2D management, thereby preventing subsequent hospitalizations and improving patient prognoses. While acute admission can sometimes be perceived as a management failure, it should also be viewed as an opportunity to take action to prevent the next hospitalization.

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.

The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers

BACKGROUND AND OBJECTIVES

Diabetes mellitus is a major chronic disease that results in readmissions due to poor disease control. Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients.

METHODS

The dataset analyzed in this study was acquired from the Health Facts Database, which includes over 100,000 records of diabetic patients from 1999 to 2008. The basic data distribution characteristics of this dataset were summarized and then analyzed. In this study, 30-days readmission was defined as a readmission period of less than 30 days. After data preprocessing and normalization, multiple risk factors in the dataset were examined for classifier training to predict the probability of readmission using ML models. Different ML classifiers such as random forest, Naive Bayes, and decision tree ensemble were adopted to improve the clinical efficiency of the classification. In this study, the Konstanz Information Miner platform was used to preprocess and model the data, and the performances of the different classifiers were compared.

RESULTS

A total of 100,244 records were included in the model construction after the data preprocessing and normalization. A total of 23 attributes, including race, sex, age, admission type, admission location, length of stay, and drug use, were finally identified as modeling risk factors. Comparison of the performance indexes of the three algorithms revealed that the RF model had the best performance with a higher area under receiver operating characteristic curve (AUC) than the other two algorithms, suggesting that its use is more suitable for making readmission predictions.

CONCLUSION

The factors influencing 30-days readmission predictions in diabetic patients, including number of inpatient admissions, age, diagnosis, number of emergencies, and sex, would help healthcare providers to identify patients who are at high risk of short-term readmission and reduce the probability of 30-days readmission. The RF algorithm with the highest AUC is more suitable for making 30-days readmission predictions and  deserves further validation in clinical trials.

The frequency and quality of delirium documentation in discharge summaries

BACKGROUND

The National Institute for Health and Care Excellence recommends documenting all delirium episodes in the discharge summary using the term "delirium". Previous studies demonstrate poor delirium documentation rates in discharge summaries and no studies have assessed delirium documentation quality. The aim of this study was to determine the frequency and quality of delirium documentation in discharge summaries and explore differences between medical and surgical services.

METHODS

This was a multi-center retrospective chart review. We included 110 patients aged ≥ 65 years identified to have delirium during their hospitalization using the Chart-based Delirium Identification Instrument (CHART-DEL). We assessed the frequency of any delirium documentation in discharge summaries, and more specifically, for the term "delirium". We evaluated the quality of delirium discharge documentation using the Joint Commission on Accreditation of Healthcare Organization's framework for quality discharge summaries. Comparisons were made between medical and surgical services. Secondary outcomes included assessing factors influencing the frequency of "delirium" being documented in the discharge summary.

RESULTS

We identified 110 patients with sufficient chart documentation to identify delirium and 80.9 % of patients had delirium documented in their discharge summary ("delirium" or other acceptable term). The specific term "delirium" was reported in 63.6 % of all delirious patients and more often by surgical than medical specialties (76.5 % vs. 52.5 %, p = 0.02). Documentation quality was significantly lower by surgical specialties in reporting delirium as a diagnosis (23.5 % vs. 57.6 %, p < 0.001), documenting delirium workup (23.4 % vs. 57.6 %, p = 0.001), etiology (43.3 % vs. 70.4 %, p = 0.03), treatment (36.7 % vs. 66.7 %, p = 0.02), medication changes (44.4 % vs. 100 %, p = 0.002) and follow-up (36.4 % vs. 88.2 %, p = 0.01).

CONCLUSIONS

The frequency of delirium documentation is higher than previously reported but remains subpar. Medical services document delirium with higher quality, but surgical specialties document the term "delirium" more frequently. The documentation of delirium in discharge summaries must improve to meet quality standards.

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.

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.

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.

Individual and community-level income and the risk of diabetes rehospitalization among women and men: a Canadian population-based cohort study

BACKGROUND

Marked disparities by socioeconomic status in the risk of potentially avoidable hospitalization for chronic illnesses have been observed in many contexts, including those with universal health coverage. Less well known is how gender mediates such differences. We conducted a population-based cohort study to describe associations between household and community-level income and rehospitalizations for types 1 and 2 diabetes mellitus among Canadian women and men.

METHODS

Our cohorts were drawn from respondents to the 2006 mandatory long-form census linked longitudinally to 3 years of nationally standardized hospital records. We included adults 30-69 years hospitalized with diabetes at least once during the study period. We used logistic regressions to estimate odds ratios for 12-month diabetes rehospitalization associated with indicators of household and community-level income, with separate models by gender, and controlling for a range of other sociodemographic characteristics. Since diabetes may not always be recognized as the main reason for hospitalization, we accounted for disease progression through consideration of admissions where diabetes was previously identified as a secondary diagnosis.

RESULTS

Among persons hospitalized at least once with diabetes (n = 41,290), 1.5% were readmitted within 12 months where the initial admission had diabetes as the primary diagnosis, and 1.8% were readmitted where the initial admission had diabetes as a secondary diagnosis. For men, being in the lowest household income quintile was associated with higher odds of rehospitalization in cases where the initial admission listed diabetes as either the primary diagnosis (OR = 2.21; 95% CI = 1.38-3.51) or a secondary diagnosis (OR = 1.51; 95% CI = 1.02-2.24). For women, we found no association with income and rehospitalization, but having less than university education was associated with higher odds of rehospitalization where diabetes was a secondary diagnosis of the initial admission (OR = 1.88; 95% CI = 1.21-2.92). We also found positive, but insignificant associations between community-level poverty and odds of rehospitalization.

CONCLUSIONS

Universal health coverage remains insufficient to eliminate socioeconomic inequalities in preventable diabetes-related hospitalizations, as illustrated in this Canadian context. Decision-makers should tread cautiously with gender-blind poverty reduction actions aiming to enhance population health that may inadequately respond to the different needs of disadvantaged women and men with chronic illness.

Association of Glucose Concentrations at Hospital Discharge With Readmissions and Mortality: A Nationwide Cohort Study

CONTEXT

Low blood glucose concentrations during the discharge day may affect 30-day readmission and posthospital discharge mortality rates.

OBJECTIVE

To investigate whether patients with diabetes and low glucose values during the last day of hospitalization are at increased risk of readmission or mortality.

DESIGN AND OUTCOMES

Minimum point of care glucose values were collected during the last 24 hours of hospitalization. We used adjusted rates of 30-day readmission rate, 30-, 90-, and 180-day mortality rates, and combined 30-day readmission/mortality rate to identify minimum glucose thresholds above which patients can be safely discharged.

PATIENTS AND SETTING

Nationwide cohort study including 843,978 admissions of patients with diabetes at the Veteran Affairs hospitals 14 years.

RESULTS

The rate ratios (RRs) increased progressively for all five outcomes as the minimum glucose concentrations progressively decreased below the 90 to 99 mg/dL category, compared with the 100 to 109 mg/dL category: 30-day readmission RR, 1.01 to 1.45; 30-day readmission/mortality RR, 1.01 to 1.71; 30-day mortality RR, 0.99 to 5.82; 90-day mortality RR, 1.01 to 2.40; 180-day mortality RR, 1.03 to 1.91. Patients with diabetes experienced greater 30-day readmission rates, 30-, 90- and 180-day postdischarge mortality rates, and higher combined 30-day readmission/mortality rates, with glucose levels <92.9 mg/dL, <45.2 mg/dL, 65.8 mg/dL, 67.3 mg/dL, and <87.2 mg/dL, respectively.

CONCLUSION

Patients with diabetes who had hypoglycemia or near-normal glucose values during the last day of hospitalization had higher rates of 30-day readmission and postdischarge mortality.

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.

An individualized inpatient diabetes education and hospital transition program for poorly controlled hospitalized patients with diabetes

OBJECTIVE

To evaluate predictors of outcomes associated with an inpatient diabetes education and discharge support program for hospitalized patients with poorly controlled diabetes (glycated hemoglobin [HbA1c]>9%).

METHODS

Patients participated in individualized diabetes education conducted by a certified diabetes educator (CDE) that included an exploration of barriers and goal setting during hospitalization with telephone follow-up and communication with primary providers at discharge. Predictors of HbA1c reduction, successful follow-up, and readmission were analyzed.

RESULTS

There were 82 subjects, and 48% were insulin naïve. Patients with type 2 diabetes (T2D, n = 58) had a significant decrease in HbA1c at follow-up (-2.8%, P<.0001), while those with type 1 diabetes (T1D, n = 19) did not (+0.02%, P = .96). However, after adjustment for other factors, only increasing age, higher baseline HbA1c, earlier education, and initiation of basal insulin were significant predictors of reduction in HbA1c. Higher area level income and empowerment and earlier education were significant predictors of outpatient follow-up within 30 days. While 28% were admitted for severe hyperglycemia, only 1 patient was readmitted with severe hyperglycemia. Successful phone contact was 77% and 57% with and without the support of non-CDE assistants respectively, but all outcomes were similar.

CONCLUSION

The study suggests that an individualized inpatient diabetes education and transition program is associated with a significant reduction in HbA1c that is dependent on baseline HbA1c, older age, initiation of insulin, and earlier enrollment. Additional interventions are needed to ensure better continuity of care.

Identifying risk factors for 30-day readmission events among American Indian patients with diabetes in the Four Corners region of the southwest from 2009 to 2016

Objective

The objective of this study was to identify risk factors for 30-day readmission events for American Indian patients with diabetes in the southwest.

Research design and methods

Data from patients with diabetes admitted to Gallup Indian Medical Center between 2009 and 2016 were analyzed using logistic regression analyses.

Results

Of 2,660 patients, 394 (14.8%) patients had at least one readmission within 30 days of discharge. Older age (OR (95% CI) = 1.26, (1.17, 1.36)), longer length of stay (OR (95% CI) = 1.01, (1.0001, 1.0342)), and a history of substance use disorder (OR (95% CI) = 1.80, (1.25, 2.60)) were risk factors for 30-day readmission. An American Indian language preference was protective against readmission.

Conclusions

Readmission events are complex and may reflect broad and interwoven disparities in community systems. Future research should work to support community-defined interventions to address both in hospital and external factors that impact risk factors for readmission.

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

BACKGROUND

Hospital readmission within 30 days of discharge (30-d readmission) is an undesirable outcome. Readmission of patients with diabetes is common and costly. Most of the studies that have examined readmission risk factors among diabetes patients did not include potentially important clinical data.

OBJECTIVES

To provide a more comprehensive understanding of 30-day readmission risk factors among patients with diabetes based on predischarge and postdischarge data.

RESEARCH DESIGN

In this retrospective cohort study, 48 variables were evaluated for association with readmission by multivariable logistic regression.

SUBJECTS

In total, 17,284 adult diabetes patients with 44,203 hospital discharges from an urban academic medical center between January 1, 2004 and December 1, 2012.

MEASURES

The outcome was all-cause 30-day readmission. Model performance was assessed by c-statistic.

RESULTS

The 30-day readmission rate was 20.4%, and the median time to readmission was 11 days. A total of 27 factors were statistically significant and independently associated with 30-day readmission (P<0.05). The c-statistic was 0.82. The strongest risk factors were lack of a postdischarge outpatient visit within 30 days, hospital length-of-stay, prior discharge within 90 days, discharge against medical advice, sociodemographics, comorbidities, and admission laboratory values. A diagnosis of hypertension, preadmission sulfonylurea use, admission to an intensive care unit, sex, and age were not associated with readmission in univariate analysis.

CONCLUSIONS

There are numerous risk factors for 30-day readmission among patients with diabetes. Postdischarge factors add to the predictive accuracy achieved by predischarge factors. A better understanding of readmission risk may ultimately lead to lowering that risk.

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.

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

OBJECTIVE

The Diabetes Early Re-admission Risk Indicator (DERRI^™) was previously developed and internally validated as a tool to predict the risk of all-cause re-admission within 30 days of discharge (30-day re-admission) of hospitalized patients with diabetes. In this study, the predictive performance of the DERRI^™ with and without additional predictors was assessed in an external sample.

METHODS

We conducted a retrospective cohort study of adult patients with diabetes discharged from two academic medical centers between January 1, 2000 and December 31, 2014. We applied the previously developed DERRI^™, which includes admission laboratory results, sociodemographics, a diagnosis of certain comorbidities, and recent discharge information, and evaluated the effect of adding metabolic indicators on predictive performance using multivariable logistic regression. Total cholesterol and hemoglobin A1c (A1c) were selected based on clinical relevance and univariate association with 30-day re-admission.

RESULTS

Among 105,974 discharges, 19,032 (18.0%) were followed by 30-day re-admission for any cause. The DERRI^™ had a C-statistic of 0.634 for 30-day re-admission. Total cholesterol was the lipid parameter most strongly associated with 30-day re-admission. The DERRI^™ predictors A1c and total cholesterol were significantly associated with 30-day re-admission; however, their addition to the DERRI^™ did not significantly change model performance (C-statistic, 0.643 [95% confidence interval, 0.638 to 0.647]; P = .92).

CONCLUSION

Performance of the DERRI^™ in this external cohort was modest but comparable to other re-admission prediction models. Addition of A1c and total cholesterol to the DERRI^™ did not significantly improve performance. Although the DERRI^™ may be useful to direct resources toward diabetes patients at higher risk, better prediction is needed.

ABBREVIATIONS

A1c = hemoglobin A1c; CI = confidence interval; DERRI^™ = Diabetes Early Re-admission Risk Indicator; GEE = generalized estimating equation; HDL-C = high-density-lipoprotein cholesterol; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification; LDL-C = low-density-lipoprotein cholesterol.

Correction to: Hospital Readmission of Patients with Diabetes

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

The association between patient experience factors and likelihood of 30-day readmission: a prospective cohort study

OBJECTIVE

Hospital care comprises nearly a third of US healthcare expenditures. Fifteen to 20 per cent of this spending is considered to be potentially preventable. Risk prediction models have suboptimal accuracy and typically exclude patient experience data. No studies have explored patient perceptions of the likelihood of readmission during index admission. Our objective was to examine associations between patient perceptions of care during index hospital admission and 30-day readmission.

DESIGN

Prospective cohort study.

SETTING

Two inpatient adult medicine units at Massachusetts General Hospital, Boston, Massachusetts.

PARTICIPANTS

Eight hundred and forty-six patients admitted to study units between January 2012 and January 2016 who met eligibility criteria and consented to enrolment.

MAIN OUTCOME

Odds of 30-day readmission.

RESULTS

Of 1754 eligible participants, 846 (48%) were enrolled and 201 (23.8%) were readmitted within 30 days. Readmitted participants were less likely to have a high school diploma/GED (44.3% not readmitted vs 53.5% readmitted, P=0.02). In multivariable models adjusting for baseline differences, respondents who reported being 'very satisfied' with the care received during the index hospitalisation were less likely to be readmitted (adjusted OR 0.61, 95% CI 0.43 to 0.88, P=0.007). Participants reporting doctors 'always listened to them carefully' were less likely to be readmitted (adjusted OR 0.68, 95% CI 0.48 to 0.97, P=0.03). Participants reporting they were 'very likely' to be readmitted were not more likely to be readmitted (adjusted OR 1.35, 95% CI 0.83 to 2.19, P=0.22).

CONCLUSION

Participants reporting high satisfaction and good provider communication were less likely to be readmitted. Rates of readmission were increased among participants stating they were very likely to be readmitted though this association was not statistically significant. Incorporating patient-reported measures during index hospitalisations may improve readmission prediction.

Effectiveness of a multi-component quality improvement intervention on rates of hyperglycaemia

Purpose

To evaluate the effectiveness of a multifaceted, hospital-wide glycaemic control quality improvement programme.

Methods

The quality improvement intervention comprised three components, derived through root cause analysis: standardising and simplifying care (including evidence-based order sets), increasing visibility (through provider access to clinical data and direct feedback) and educational outreach (directed at the entire institution). Effectiveness was determined at a single urban acute care hospital through time-series analysis with statistical process control charts. Primary outcomes included rate of hyperglycaemia and rate of hypoglycaemia.

Results

The study included 70 992 hospital admissions for 50 404 patients, with 3 35 645 patient days. The hyperglycaemia ratio decreased 25.2% from 14.1% to 10.5% (95% CI 3.3 to 3.9 percentage points, p<0.001). The ratio of patient days with highly elevated blood glucose (>299 mg/dL) decreased 31.8% from 4.8% to 3.3% (95% CI 1.4 to 1.7 percentage points, p<0.001). Hypoglycaemia ratio decreased from 5.2% to 4.6% (95% CI 0.27 to 0.89 percentage points, p<0.001) in patients with diabetes, but increased in patients without diabetes from 1.2% to 1.7% (95% CI 0.46 to 0.70 percentage points, p<0.001).

Conclusions

We demonstrate improved hospital-wide glycaemic control after a multifaceted quality improvement intervention in the context of strong institutional commitment, national mentorship and Lean management

The prevalence and long-term variation of hospital readmission for patients with diabetes in Tianjin, China: A cross-sectional study

Little is known about hospital readmission for patients with diabetes in China. We aimed to assess the temporal pattern, risk factors, and variations of all-cause readmission among hospitalized patients with diabetes in Tianjin, China, from 2008 to 2013.The Tianjin Basic Medical Insurance Register System database was used to identify discharged patients with diabetes from 2008 to 2013. The influential factors and trends of rehospitalization were analyzed for 30-, 60- and 90-day predicted readmission rates. The Blinder-Oaxaca decomposition was used to explain the readmission variations between 2008 and 2013.The long stay-time at the index hospitalization is a shared risk factor for readmission at 30, 60, and 90 days each year. The 90-day predicted readmission rates were the highest for each year (all P < .001). The adjusted readmission rates generally decreased by year (all P < .001), except for at the 90-day interval, which decreased in 2010 and slightly increased in 2013 (from 7.47% in 2012 to 7.65% in 2013). If the patients had been readmitted to the hospital in 2013 and the only changes that had occurred since 2008 were observable characteristics, then the readmission rates would have decreased by 0.84%, 0.27%, and 0.18% at 30, 60, and 90 days, respectively. The potential policy changes decreased the readmission rates at 1.35%, 2.01%, and 1.04% for the 3 intervals, respectively.Identifying targeted factors for the decrease in readmission rates may help to control readmission, particularly for long-interval patients.

Predictors of all-cause 30 day readmission among Medicare patients with type 2 diabetes

OBJECTIVE

Readmission is costly among patients with type 2 diabetes (T2DM) in Medicare Advantage Prescription Drug Plans; identifying high-risk patients is necessary for targeting reduction programs. The objective of this study was to develop a claims-based algorithm to predict all-cause 30 day readmission among patients with T2DM.

METHODS

This study used administrative data from 1 January 2012 through 31 January 2014. The cohort included hospitalized T2DM patients, aged 18-90 with ≥12 months' continuous enrollment before an unplanned hospital admission and ≥1 month of enrollment post-discharge, excluding patients in long-term care >30 days pre-index. Multivariate logistic regression predicted the likelihood of readmission following hospitalization in 2013. The analytic file was randomly split into training and test datasets to build and validate the model. Candidate variables included physician and patient demographics, baseline clinical conditions, and healthcare utilization metrics. Clinical conditions were classified using the Healthcare Cost and Utilization Project clinical classification system for ICD-9-CM.

RESULTS

Of 63,237 individuals, 17.1% experienced a readmission. Of nearly 200 candidate variables, 14 were predictors of readmission, including total cumulative number of days for inpatient stays and the number of emergency department visits in the baseline period. Male gender, older age, and certain comorbidities were associated with higher likelihood of readmission. The final model demonstrated good discriminant ability (c-statistic = 0.82).

CONCLUSIONS

This study provided evidence that certain patient characteristics and healthcare utilization are predictive of readmission. An algorithm with good discriminant ability was developed which could be used to target readmission reduction programs. Physician gender, specialty, and ownership status did not appear to influence the likelihood of readmission.

Predicting readmission risk of patients with diabetes hospitalized for cardiovascular disease: a retrospective cohort study

OBJECTIVE

To develop and validate a tool that predicts 30d readmission risk of patients with diabetes hospitalized for cardiovascular disease (CVD), the Diabetes Early Readmission Risk Indicator-CVD (DERRI-CVD™).

METHODS

A cohort of 8189 discharges was retrospectively selected from electronic records of adult patients with diabetes hospitalized for CVD. Discharges of 60% of the patients (n=4950) were randomly selected as a training sample and the remaining 40% (n=3219) were the validation sample.

RESULTS

Statistically significant predictors of all-cause 30d readmission risk were identified by multivariable logistic regression modeling: education level, employment status, living within 5miles of the hospital, pre-admission diabetes therapy, macrovascular complications, admission serum creatinine and albumin levels, having a hospital discharge within 90days pre-admission, and a psychiatric diagnosis. Model discrimination and calibration were good (C-statistic 0.71). Performance in the validation sample was comparable. Predicted 30d readmission risk was similar in the training and validation samples (38.6% and 35.1% in the highest quintiles).

CONCLUSIONS

The DERRI-CVD™ may be a valid tool to predict all-cause 30d readmission risk of patients with diabetes hospitalized for CVD. Identifying high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs.

The effect of diabetes case management and Diabetes Resource Nurse program on readmissions of patients with diabetes mellitus

AIMS

Patients with diabetes have higher readmission rates than those without diabetes, yet limited data on efforts to reduce their readmissions are available. We describe a novel model of inpatient diabetes care, expanding the role of diabetes educators to include case management, and establishment of a Diabetes Resource Nurse program, aimed at increasing the knowledge of staff nurses, and evaluate the impact of this program on readmission rates.

METHODS

We performed retrospective analysis of 30-day readmission rates of patients with diabetes before (July 2010-December 2011), and after (January 2012-June 2013) starting the implementation of this tiered inpatient diabetes care delivery model.

RESULTS

We analyzed 34,472 discharged patient records from the 18-month pre-intervention period, and 32,046 records from the 18-month post-intervention period. The overall 30-day readmission rate for patients with diabetes decreased significantly from 20.1% (pre) to 17.6% (post) intervention (p < 0.0001). Patients seen by diabetes educators had the lowest 30-day readmission rates (∼15% during the whole study), a rate approaching the overall hospital readmission rates in those without diabetes in our institution.

CONCLUSION

The Diabetes Resource Nurse program is effective in decreasing readmission rates. Patients seen by the diabetes educators have the lowest rates of readmission.

Understanding 30-day re-admission after hospitalisation of older patients for diabetes: identifying those at greatest risk

OBJECTIVE

To identify factors that contribute to older Australians admitted to hospital with diabetes being re-hospitalised within 30 days of discharge.

DESIGN, SETTING AND PARTICIPANTS

A retrospective cohort study of Department of Veterans' Affairs administrative data for all patients hospitalised for diabetes and discharged alive during the period 1 January - 31 December 2012.

MAIN OUTCOME MEASURES

Causes of re-hospitalisation and prevalence of clinical factors associated with re-hospitalisation within 30 days of discharge.

METHODS

Multivariate logistic regression analysis (backward stepwise) was used to identify characteristics predictive of 30-day re-hospitalisation.

RESULTS

848 people were hospitalised for diabetes; their median age was 87 years (interquartile range, 77-89 years) and 60% were men. 209 patients (24.6%) were re-hospitalised within 30 days of discharge, of whom 77.5% were re-admitted within 14 days of discharge. 51 re-hospitalisations (24%) were for diabetes-related conditions; 41% of those re-admitted within 14 days had not seen their general practitioner between discharge and re-admission. Factors predictive of re-hospitalisation included comorbid heart failure (adjusted odds ratio [aOR], 1.49; 95% confidence interval [CI], 1.03-2.17; P = 0.036), numbers of prescribers in previous year (aOR [for each additional prescriber], 1.06; 95% CI, 1.01-1.08; P = 0.031), and two or more hospitalisations in the 6 months before the index admission (aOR, 1.79; 95% CI 1.15-2.78; P = 0.009).

CONCLUSION

Older people hospitalised for diabetes who have comorbid heart failure, multiple recent hospitalisations, and multiple prescribers involved in their care are at greatest risk of being re-admitted to hospital within 30 days. Targeted follow-up during the initial 14 days after discharge may facilitate appropriate interventions that avert re-admission of these at-risk patients.

The relationship between diabetes mellitus and 30-day readmission rates

Background

It is estimated that 9.3% of the population in the United States have diabetes mellitus (DM), 28% of which are undiagnosed. The high prevalence of DM makes it a common comorbid condition in hospitalized patients. In recent years, government agencies and healthcare systems have increasingly focused on 30-day readmission rates to determine the complexity of their patient populations and to improve quality. Thirty-day readmission rates for hospitalized patients with DM are reported to be between 14.4 and 22.7%, much higher than the rate for all hospitalized patients (8.5–13.5%). The objectives of this study were to (1) determine the incidence and causes of 30-day readmission rates for patients with diabetes listed as either the primary reason for the index admission or with diabetes listed as a secondary diagnosis compared to those without DM and (2) evaluate the impact on readmission of two specialized inpatient DM services: the Hyperglycemic Intensive Insulin Program (HIIP) and Endocrine Consults (ENDO).

Methods

For this study, DM was defined as any ICD-9 discharge diagnosis (principal or secondary) of 250.xx. Readmissions were defined as any unscheduled inpatient admission, emergency department (ED) visit, or observation unit stay. We analyzed two separate sets of patient data. The first pilot study was a retrospective chart review of all patients with a principle or secondary admission diagnosis of diabetes admitted to any adult service within the University of Michigan Health System (UMHS) between October 1, 2013 and December 31, 2013. We then did further uncontrolled analysis of the patients with a principal admitting diagnosis of diabetes. The second larger retrospective study included all adults discharged from UMHS between October 1, 2013 and September 30, 2014 with principal or secondary discharge diagnosis of DM (ICD-9-CM: 250.xx).

Results

In the pilot study of 7763 admissions, the readmission rate was 26% for patients with DM and 22% for patients without DM. In patients with a primary diagnosis of DM on index admission, the most common cause for readmission was DM-related. In the larger study were 37,702 adult inpatient discharges between October 1, 2013 and September 30, 2014. Of these, 20.9% had DM listed as an encounter diagnosis. Rates for all encounters (inpatient, ED and Observation care) were 24.3% in patients with DM compared to 17.7% in those without DM (p < 0.001). The most common cause for readmission in patients with DM as a secondary diagnosis to the index admission was infection-related.

During the index hospital stay, only a small proportion of patients with DM (approximately 12%) received any DM service consult. Those who received a DM consult had a higher case mix index compared to those who did not. Despite the higher acuity, there was a lower rate of ED /observation readmission in patients followed by the DM services (6.6% HIIP or ENDO vs. 9.6% no HIIP or ENDO, p = 0.0012), though no difference in the inpatient readmission rates (17.6% HIIP or ENDO vs. 17.4% no HIIP or ENDO, p = 0.89) was noted.

Conclusions

Patients with both a primary or secondary diagnosis of DM have higher readmission rates. The reasons for readmission vary; patients with a principal diagnosis of DM have more DM related readmissions and those with secondary diagnosis having more infection-related readmissions. DM services were used in a small proportion of patients and may have contributed to lower DM related ED revisits. Further prospective studies evaluating the role of these services in terms of glucose management, patient education and outpatient follow up on readmission are needed to identify interventions important to reducing readmission rates.

Risk factors associated with 30-day readmission and length of stay in patients with type 2 diabetes

AIMS

Patients with type 2 diabetes mellitus (type 2 DM) are at greater risk of poor hospital outcomes. The purpose of this study was to determine the impact of type 2 DM on 30-day hospital readmission and length of stay (LOS).

METHODS

We studied all inpatient admissions in Pennsylvania during 2011 using data from the Pennsylvania Health Care Cost Containment Council. Outcomes included 30-day readmission and inpatient LOS. We estimated the impact of type 2 DM on readmission and LOS, and identified risk factors for readmission and prolonged LOS.

RESULTS

Among inpatient admissions, patients with diabetes were more likely to be readmitted (AOR=1.17, P<0.001) and have longer LOS (0.19days, P<0.001) compared to patients without diabetes. Among those with diabetes, several factors were associated with readmission, including demographics, source of admission, and comorbidities. Patients with diabetes were more likely to be readmitted for infectious complications (9.4% vs. 7.7%), heart failure (6.0% vs. 3.1%), and chest pain/MI (5.5% vs. 3.3%) than patients without diabetes.

CONCLUSIONS

Diabetes is associated with risk of 30-day readmission and LOS, and several patient-specific factors are associated with outcomes for patients with diabetes. Future studies may target risk factors to develop strategies to reduce readmissions and LOS.

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

OBJECTIVE

To develop and validate a tool to predict the risk of all-cause readmission within 30 days (30-d readmission) among hospitalized patients with diabetes.

METHODS

A cohort of 44,203 discharges was retrospectively selected from the electronic records of adult patients with diabetes hospitalized at an urban academic medical center. Discharges of 60% of the patients (n = 26,402) were randomly selected as a training sample to develop the index. The remaining 40% (n = 17,801) were selected as a validation sample. Multivariable logistic regression with generalized estimating equations was used to develop the Diabetes Early Readmission Risk Indicator (DERRI™).

RESULTS

Ten statistically significant predictors were identified: employment status; living within 5 miles of the hospital; preadmission insulin use; burden of macrovascular diabetes complications; admission serum hematocrit, creatinine, and sodium; having a hospital discharge within 90 days before admission; most recent discharge status up to 1 year before admission; and a diagnosis of anemia. Discrimination of the model was acceptable (C statistic 0.70), and calibration was good. Characteristics of the validation and training samples were similar. Performance of the DERRI™ in the validation sample was essentially unchanged (C statistic 0.69). Mean predicted 30-d readmission risks were also similar between the training and validation samples (39.3% and 38.7% in the highest quintiles).

CONCLUSION

The DERRI™ was found to be a valid tool to predict all-cause 30-d readmission risk of individual patients with diabetes. The identification of high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs.

ABBREVIATIONS

DERRI™ = Diabetes Early Readmission Risk Indicator ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification GEE = generalized estimating equations ROC = receiver operating characteristic.

Prevalence and temporal pattern of hospital readmissions for patients with type I and type II diabetes

OBJECTIVE

Repeated hospitalisation for patients is common and costly, yet partly preventable. However, we know little about readmissions for patients with diabetes in China. The current study aims to assess the frequency and temporal pattern of and risk factors for all-cause readmission among hospitalised patients with diabetes in Tianjin, China.

METHOD

This retrospective, cohort analysis used the Tianjin Basic Medical Insurance Register System data of 2011. The patterns of and the reasons for all-cause readmissions for patients with diabetes were described. The differences of readmission-free survival (RFS) between newly and previously diagnosed patients were compared. Time-dependent Cox models were established to identify the risk factors for readmission at different time intervals after discharge.

RESULTS

Readmission rates were approximately 30%, with the most common diagnoses of cerebral infarction (for type I) or diabetes (for type II) for patients with diabetes. The majority of patients were readmitted to the hospital after more than 90 days, followed by 8-30 days (all p=0.002). Approximately 37.2% and 42.8% of readmitted patients with type I and type II diabetes were diagnosed previously, and the RFS rates for previously diagnosed patients were significantly lower than for newly diagnosed patients at any time interval after discharge. Prior history of diabetes (all p<0.05), length of stay (all p<0.01) and reimbursement ratio (90% vs >92%, all p<0.0002) were consistently associated with the RFS for patients readmitted to the hospital at <7, 8-30, 31-60 and 61-90 days.

CONCLUSIONS

Hospital readmissions among patients with diabetes were affected by the diagnosis status. Patient characteristics and the quality of healthcare might regulate short-interval and long-interval hospital readmission, respectively, after discharge.

Detecting and Managing Diabetes Mellitus and Prediabetes in Patients With Acute Stroke

PURPOSE

The purpose of the study was to determine the prevalence of undiagnosed diabetes mellitus (DM) and prediabetes (pre-DM) in acute stroke patients, to evaluate recommendations of diabetes treatment and follow-up care in a hospital setting, and to examine 1-year readmission rates based on admission A1C measure.

METHODS

This retrospective study comprised 200 patients randomly selected from 1095 patients admitted with an acute stroke and an A1C measurement during admission. DM and pre-DM prevalence levels were determined per A1C level. Recommendations for diabetes treatment during and after hospitalization were assessed; charts were reviewed for readmission.

RESULTS

Among 200 patients, 43% had known DM, and 0.5% had pre-DM. Among 113 patients without history of DM or pre-DM, 61.9% had A1C 5.7% to 6.4% (39-46 mmol/mol), and 8.8% had A1C ≥6.5% (48 mmol/mol). None of the newly diagnosed pre-DM and 60% of newly diagnosed DM were documented. Only 7 of newly diagnosed DM or pre-DM patients received diabetes education. For patients with known DM and A1C ≥7.0% (53 mmol/mol), 40.5% registered no change of diabetic regimen. Few patients were recommended for diabetes follow-up care. Patients with A1C ≥6.5% or <5.7% were more likely to be readmitted for any reason within 1 year (33.3% and 31.6%, respectively) than patients with A1C levels of 5.7% to 6.4% (16.5%).

CONCLUSIONS

The majority of acutely admitted stroke patients without known DM or pre-DM had A1C ≥5.7%. Newly diagnosed DM or pre-DM patients received inadequate diabetes education and follow-up care. These findings provide significant opportunities for improving acute stroke management.

Type 2 Diabetes Mellitus Patients' Healthcare Costs Related to Inpatient Hospitalizations: A Retrospective Administrative Claims Database Study

INTRODUCTION

Diabetes-related healthcare costs are increasing in the United States, with inpatient hospitalization the largest component of medical expenditures. The aims of this study were to characterize hospitalized type 2 diabetes mellitus (T2DM) patients, understand the relationship between hospitalization and healthcare costs, and explore treatment modification after inpatient hospitalization.

METHODS

A retrospective cohort analysis of Humana Medicare Advantage and commercial members with T2DM was conducted. T2DM members were identified and assigned to three groups: (1) inpatient hospitalization (IPH) without a 30-day readmit (IPH group); (2) IPH with a 30-day readmission (IPH readmission group); and, (3) matched non-IPH group. Demographics, clinical characteristics, comorbidities and healthcare costs were measured based on enrollment data and claims. Descriptive statistics were used and the relationship between IPH and costs was assessed using generalized linear models.

RESULTS

A total of 15,555 IPH patients, 1757 IPH readmission patients, and 17,312 matched non-IPH patients were included in the study. The IPH readmission group had the highest adjusted mean all-cause total costs ($76,806), followed by the IPH group ($42,011), and the non-IPH group ($9624). A similar trend was observed for adjusted all-cause mean medical and pharmacy costs. DM-related total healthcare costs were highest for the IPH readmission group ($13,714), followed by the IPH group ($7477), and non-IPH group ($1620). While overall therapy modification (discontinuation, addition, switch) was low, T2DM patients with an IPH (with or without a readmission) had greater rates of therapy modification relative to the non-IPH patients.

CONCLUSION

Adjusted all-cause and DM-related total costs were greatest for IPH readmission patients. Rates of treatment modification within 10 days of discharge after IPH were generally low. Identifying T2DM patients at high risk of readmission and employing methods to decrease that risk during the index hospitalization could have a significant impact on health system costs.

FUNDING

Novo Nordisk.

Hospital readmission of patients with diabetes

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

Predictors of 30 day hospital readmission in patients with type 2 diabetes: a retrospective, case-control, database study

OBJECTIVE

To assess factors predictive of all-cause, 30 day hospital readmission among patients with type 2 diabetes in the United States.

METHODS

A retrospective, case-control study using deidentified Humedica electronic health record data was conducted to identify patients ≥18 years old with ≥6 months of data prior to index hospitalization (pre-period) and ≥30 days of data after discharge (post-period). Combined methods of bootstrap resampling and stepwise logistic regression were used to identify factors associated with readmission.

RESULTS

Among 52,070 patients with type 2 diabetes and an initial hospitalization for any reason, 5201 (10.0%) patients were readmitted within 30 days and 46,869 (90.0%) patients showed no evidence of readmission. Diabetic treatment escalation; race; type 2 diabetes diagnosis prior to the index stay; pre-period heart failure; and number of pre-period, inpatient healthcare visits were among the strongest predictors of 30 day readmission. From a receiver-operating characteristic plot (mean area under curve of 0.693), the predictive accuracy of the final logistic regression model is considered modest. This result might be due to the unavailability of some variables or data.

CONCLUSIONS

These results highlight the importance of the appropriate recognition of and treatment for type 2 diabetes, prior to and during hospitalization and following discharge, in order to impact a subsequent hospitalization. In our analysis, escalation of diabetic treatments (especially those escalated from having no records of anti-diabetic medications to treatment with insulin) was the strongest predictor of 30 day readmission. Limitations of this study include the fact that hospitalizations and other encounters, outside the Humedica network, were not captured in this analysis.

Early readmission among patients with diabetes: a qualitative assessment of contributing factors

AIMS

To explore causes of early readmission, i.e., hospital readmission within 30 days of discharge, among patients with diabetes.

METHODS

We performed thematic analysis of semi-structured interviews among 20 adults with diabetes hospitalized with an early readmission at an urban academic medical center.

RESULTS

Five themes emerged as contributors to readmission risk: (1) poor health literacy (lack of knowledge about diabetes and discharge instructions), (2) health system failure (of the discharge process and post-discharge support), (3) failure of expected protective factors, (e.g., following the discharge instructions, being aware of medication changes upon discharge, and having help and social support), (4) social determinants of health impeding care, and (5) loss of control over illness. A majority of patients reported needing assistance with transportation, obtaining and taking medications, and preparing food. Most patients denied an active role in exacerbating their condition prior to readmission, and many believed that being readmitted was out of their control.

CONCLUSIONS

Our findings suggest several interventions that may reduce the risk of early readmission for patients with diabetes, including inpatient diabetes education, improving communication of discharge instructions, and involving patients more in medication reconciliation and post-discharge planning.

Effectively identifying the inpatient with hyperglycemia to increase patient care and lower costs

Recent years have seen an increased focus on merging quality care and financial results. This focus not only extends to the inpatient setting but also is of major importance in assuring effective transitions of care from hospital to home. Inducements to meld the 2 factors include tying payment to quality standards, investing in patient safety, and offering new incentives for providers who deliver high-quality and coordinated care. Once seen as the purview of primary care or specific surgical screening programs, identification of patients with hyperglycemia or undiagnosed diabetes mellitus now presents providers with opportunities to improve care. Part of the new focus will need to address the length of stay for patients with diabetes mellitus. These patients are proven to require longer hospital stays regardless of the admission diagnosis. With reducing length of stay as a major objective, efficiency combined with improved quality is the desired outcome. Even with the mounting evidence supporting the benefits of improving glycemic control in the hospital setting, institutions continue to struggle with inpatient glycemic control. Multiple national groups have provided recommendations for blood glucose assessment and glycated hemoglobin testing. This article identifies the key benefits in identifying patients with hyperglycemia and reviews possible ways to identify, monitor, and treat this potential problem area and thereby increase the level of patient care and cost-effectiveness.

Clustering of cardiometabolic risk factors and risk of elevated HbA1c in non-Hispanic White, non-Hispanic Black and Mexican-American adults with type 2 diabetes

AIM

To determine which cardiometabolic risk factors and clusters of cardiometabolic risk factors that are mostly associated with elevated HbA1c in non-Hispanic White (NHW), non-Hispanic Black (NHB) and Mexican-American (MA) adults who have type 2 diabetes.

METHODS

Data (n=2910) from the United States National Health and Nutritional Examination Surveys were used in this study. Elevated HbA1c was defined as having HbA1c value was 7% or greater. Race/ethnicity-specific associations of individual and clustered (2-5 factors) cardiometabolic risk factors with elevated HbA1c were determined using prevalence odds ratio from multivariate logistic regression analyses. Statistical adjustments were made for sex, age, education, income and marital status.

RESULTS

Joint occurrence of abdominal obesity, high blood pressure, and elevated triglycerides and joint occurrence of high blood pressure, elevated triglycerides and low HDL were more highly associated with elevated odds of HbA1c compared to other cardiometabolic risk factors joint occurrences. Joint occurrences of abdominal obesity, high blood pressure, and elevated triglycerides was associated with 2.3 (95% CI: 1.2-3.3), 9.1 (95% CI: 2.9-28.7) and 4.8 (95% CI: 2.0-11.5) increased odds of elevated HbA1c in NHW, NHB and MA, respectively. The corresponding values for the joint occurrence of high blood pressure, elevated triglycerides and low HDL was associated with 2.4 (95% CI: 1.2-3.7), 3.5 (95% CI: 1.1-5.5) and 2.6 (95% CI: 1.5-4.7) increased odds of elevated HbA1c in NHW, NHB and MA, respectively.

CONCLUSION

This finding calls for consideration of cardiovascular risk factor clustering in deciding medical therapies to optimize glycemic control in individuals with type 2 diabetes. Interventions designed to achieve glycemic control coupled with modification of cardiometabolic risk factors may be crucial in alleviating sequelae resulting from type 2 diabetes.

Inpatient diabetes education is associated with less frequent hospital readmission among patients with poor glycemic control

OBJECTIVE

To explore the relationship between inpatient diabetes education (IDE) and hospital readmissions in patients with poorly controlled diabetes.

RESEARCH DESIGN AND METHODS

Patients with a discharge diagnosis of diabetes (ICD-9 code 250.x) and HbA1c>9% who were hospitalized between 2008 and 2010 were retrospectively identified. All-cause first readmissions were determined within 30 days and 180 days after discharge. IDE was conducted by a certified diabetes educator or trainee. Relationships between IDE and hospital readmission were analyzed with stepwise backward logistic regression models.

RESULTS

In all, 2,265 patients were included in the 30-day analysis and 2,069 patients were included in the 180-day analysis. Patients who received IDE had a lower frequency of readmission within 30 days than did those who did not (11 vs. 16%; P=0.0001). This relationship persisted after adjustment for sociodemographic and illness-related factors (odds ratio 0.66 [95% CI 0.51-0.85]; P=0.001). Medicaid insurance and longer stay were also independent predictors in this model. IDE was also associated with reduced readmissions within 180 days, although the relationship was attenuated. In the final 180-day model, no IDE, African American race, Medicaid or Medicare insurance, longer stay, and lower HbA1c were independently associated with increased hospital readmission. Further analysis determined that higher HbA1c was associated with lower frequency of readmission only among patients who received a diabetes education consult.

CONCLUSIONS

Formal IDE was independently associated with a lower frequency of all-cause hospital readmission within 30 days; this relationship was attenuated by 180 days. Prospective studies are needed to confirm this association.

Pathways to quality inpatient management of hyperglycemia and diabetes: a call to action

Currently patients with diabetes comprise up to 25-30% of the census of adult wards and critical care units in our hospitals. Although evidence suggests that avoidance of hyperglycemia (>180 mg/dL) and hypoglycemia (<70 mg/dL) is beneficial for positive outcomes in the hospitalized patient, much of this evidence remains controversial and at times somewhat contradictory. We have recently formed a consortium for Planning Research in Inpatient Diabetes (PRIDE) with the goal of promoting clinical research in the area of management of hyperglycemia and diabetes in the hospital. In this article, we outline eight aspects of inpatient glucose management in which randomized clinical trials are needed. We refer to four as system-based issues and four as patient-based issues. We urge further progress in the science of inpatient diabetes management. We hope this call to action is supported by the American Diabetes Association, The Endocrine Society, the American Association of Clinical Endocrinologists, the American Heart Association, the European Association for the Study of Diabetes, the International Diabetes Federation, and the Society of Hospital Medicine. Appropriate federal research funding in this area will help ensure high-quality investigations, the results of which will advance the field. Future clinical trials will allow practitioners to develop optimal approaches for the management of hyperglycemia in the hospitalized patient and lessen the economic and human burden of poor glycemic control and its associated complications and comorbidities in the inpatient setting.

The effect of diabetes on hospital readmissions

Hospital readmission is an important contributor to total medical expenditures and is an emerging indicator of quality of care. Diabetes, similar to other chronic medical conditions, is associated with increased risk of hospital readmission. Risk factors include previous hospitalization, extremes in age, and socioeconomic barriers. Preliminary studies suggest that acute and/or chronic glycemic control may be of importance when diabetes is the primary diagnosis or when it is a comorbidity. Very limited evidence from prospective randomized controlled trials aimed at improving glycemic control is available. However, whether one concludes that inpatient or outpatient glycemic control is partly responsible for reduced hospitalizations, attention to glycemic control in the hospital may facilitate sustained glycemic control post-discharge. Limited prospective and retrospective evidence suggest that the involvement of a diabetes specialist team may improve readmission rates, but attention to more generalized comprehensive approaches may also be worthwhile. Prospective interventional studies targeting interventions for improving glycemic control are needed to determine whether glycemic control impacts readmission rates.

Managing inpatient hyperglycemia in a resource-constrained county hospital: the Parkland Memorial Hospital experience

Diabetes is a common comorbidity among hospitalized patients and has been linked to increased length of stay, morbidity, and mortality. In addition, multiple pathophysiological factors contribute to incident hyperglycemia in a sizable proportion of inpatients without previously diagnosed diabetes. Insulin is the mainstay of therapy for inpatient management of diabetes and hyperglycemia. In this article, we discuss initial treatment planning and insulin initiation for established and treatment-naïve patients with diabetes who are being treated with human and analog-based insulin therapy. As a publicly funded and cost-conscious hospital, we rely on human insulin for first-line therapy and generally find good results, reserving more costly insulin analogs for patients with type 1 diabetes. We also describe a novel continuous insulin-infusion protocol, the Parkland glucose insulin infusion protocol, which controls severe hyperglycemia safely and effectively in hospitalized patients who are unable to tolerate oral nutrition or are in other complicated clinical situations. We outline transitions from intravenous to subcutaneous insulin and other planning and diabetes education necessary to facilitate discharge. Lastly, we discuss steps for the development and implementation of a continuous intravenous insulin-infusion protocol at the institutional level.

New bundled world: quality of care and readmission in diabetes patients

BACKGROUND

Hospital readmissions among patients with diabetes are substantial and costly. Although prior studies have shown that receipt of outpatient quality of care significantly reduces the risk of hospitalization among patients with diabetes, little is known about its impact on hospital readmission. The objective of this study is to assess the impact of outpatient quality of care on 30-day readmission among patients with diabetes.

METHODS

We used deidentified administrative claims data from the IMS LifeLink and included commercially insured diabetes patients ≥ 19 years old discharged from hospitals in the United States in 2009 and 2010 (n = 30,139). The outcome was readmission within 2-30 days of discharge. The main independent variables were receipt of outpatient quality-of-care measures (i.e., two hemoglobin A1c tests, low-density lipoprotein (LDL) test, 90-day supply of statin, and 90-day supply of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers). Multivariate logistic regression was used to examine the impact of outpatient quality of care on hospital readmission while controlling for demographics, clinical characteristics, health care utilization, and insurance type in the year prior to admission.

RESULTS

Overall 30-day readmission rates among patients with diabetes were 18.9%. Patients who received at least one LDL test [odds ratio (OR) = 0.918, 95% confidence interval (CI; 0.852 0.989), p < .025] and ≥90-day supply of statins (OR = 0.91, 95% CI [0.85 0.97], p < .01) were less likely to be readmitted to the hospital.

CONCLUSIONS

Receipt of LDL testing and adherence to statin medications were effective in decreasing the likelihood of 30-day hospital readmission and may be considered as elements of a quality focused incentive-based health care delivery package for diabetes patients.

[Consensus document on the management after hospital discharge of patient with hyperglycaemia]

The present document intends to adapt the general recommendations set up in a consensus to elaborate the hospital discharge report in medical specialties to the specific needs of the hospitalized diabetic population. Diabetes is an illness with a very high health cost, being the global risk of death in people with diabetes almost double than in non-diabetes people, justifying the fact that diabetes constitutes one of the most frequent diagnoses in hospitalized patients and the growing interest upon hyperglycaemia management during hospitalization and at discharge. To set up an adequate treatment plan at discharge suitable for each patient, the most important elements to take into account are the etiology and prior hyperglycaemia treatment, the patient's clinical situation and the degree of glycaemia control. Due to instability of glycaemia control, it is also needed to anticipate the educational needs for each patient, as well as to set up the monitoring schedule and follow-up at discharge, and an adequate treatment plan at discharge.

Outpatient follow-up and rehospitalizations for sickle cell disease patients

BACKGROUND

Rehospitalization rates are increasingly used as quality indicators for a variety of illnesses, including sickle cell disease. While one small, single center study suggested outpatient follow-up with a pediatric hematologist was associated with fewer rehospitalizations, no study has examined the effect of post-discharge outpatient follow-up on rehospitalization rates across ages and beyond a single site.

PROCEDURE

This is a retrospective cohort study using Wisconsin Medicaid claims data for hospitalized children and adults with sickle cell disease from 2003 to 2007. The primary outcomes were rehospitalization at both 14 and 30 days after an index hospitalization for sickle cell pain crisis (ICD-9-CM codes 28242, 28262, 28264, 28269). Univariate survival analyses were performed based on outpatient visit, severe disease, asthma, and age. The Cox proportional hazards model was used for multivariate analyses yielding hazard ratios for the association between outpatient visits and subsequent rehospitalization rates.

RESULTS

Of the 408 patients included, 42 (10.2%) patients were rehospitalized within 14 days and 70 (17.1%) were rehospitalized within 30 days. Multivariate analysis showed that an outpatient visit is associated with lower rates of both 30-day rehospitalization (Hazard Ratio (HR) 0.442; 95%CI: 0.330-0.593) and 14-day rehospitalization (HR 0.226; 95%CI: 0.124-0.412), with the majority of 30-day rehospitalizations occurring within 14 days.

CONCLUSIONS

For sickle cell disease, post-discharge planning should emphasize early follow-up to prevent subsequent hospitalization and improve care quality. Pediatr Blood Cancer 2012; 58: 406-409. © 2011 Wiley Periodicals, Inc.

Post-discharge intervention in vulnerable, chronically ill patients

BACKGROUND

Studies suggest that the inpatient to outpatient transition of care is a vulnerable period for patients, and socioeconomically disadvantaged populations may be particularly susceptible.

OBJECTIVE

In this prospective cohort study, clustered by hospital, we sought to determine the feasibility and utility of a simple, post-discharge intervention in reducing hospital readmissions.

METHODS

Chronically ill Medicaid managed care members were consecutively identified from the discharge records of 10 area hospitals. For patients from the 7 intervention hospitals, trained medical assistants performed a brief telephone needs assessment, within 1 week of discharge, in which issues requiring near-term resolution were identified and addressed. Patients with more complicated care needs were identified according to a 4-domain care needs framework and enrolled in more intensive care management. Patients discharged from the 3 control hospitals received usual care. We used a generalized estimating equation model, which adjusts for clustering by hospital, to evaluate the primary outcome of hospital readmission within 60 days.

RESULTS

There were 97 intervention and 130 control patients. Intervention patients were slightly younger and had higher adjusted clinical group (ACG) scores. In unadjusted analysis, the intervention group had lower, but statistically similar, 60-day rehospitalization rates (23.7% vs 29.2%, P = 0.35). This difference became significant after controlling for ACG score, prior inpatient utilization, and age: adjusted odds ratio (OR) [95% confidence interval (CI)] 0.49 [0.24-1.00].

CONCLUSIONS

A simple post-discharge intervention and needs assessment may be associated with reduced recurrent hospitalization rates in a cohort of chronically ill Medicaid managed care patients with diverse care needs.

Relationship between glycemic control and readmission rates in patients hospitalized with congestive heart failure during implementation of hospital-wide initiatives

OBJECTIVE

To determine the relationship between inpatient glycemic control and hospital readmission in patients with congestive heart failure (CHF).

METHODS

We used an electronic data collection tool to identify patients with a discharge diagnosis of CHF who underwent point-of-care glucose assessments. Timeweighted mean glucose (TWMG), hemoglobin A1c, and glycemic lability index (GLI) served as glycemic indicators, and readmission for CHF was determined at 30 days and between 30 and 90 days.

RESULTS

The analysis included 748 patients. After adjustment for significant covariates, log-transformed increasing TWMG (odds ratio 3.3; P = .03) and log-transformed hemoglobin A1c (odds ratio 5.5; P = .04) were independently associated with higher readmission for CHF between 30 and 90 days, but not by 30 days. Renal disease, African American race, and year of hospital admission were also significantly associated with readmission, but GLI was not. There was no significant difference in TWMG when analyzed on the basis of race or renal status. We noted a decrease in TWMG (P = .004) and a trend for reduction in readmission rates between 30 and 90 days (P = .06) after hospital-wide interventions were implemented to improve glycemic control, but no significant difference was detected in GLI or hypoglycemia.

CONCLUSION

Increasing glucose exposure, but not glycemic variability, was associated with higher risk of readmission between 30 and 90 days in patients with CHF. Prospective studies are needed to confirm or refute these results.