Correction to: Hospital Readmission of Patients with Diabetes

Effect of Exercise Therapy on Incident Admission in Patients with Type 2Diabetes Mellitus Undergoing Inpatient Diabetes Self-manageme ntEducation and Support

BACKGROUND

Exercise therapy is the key to preventing admission of patients with type 2 diabetes mellitus (T2DM). However, a few studies have examined the effects of exercise therapy on patients with T2DM undergoing inpatient diabetes self-management education and support (IDSMES).

OBJECTIVE

This study investigated whether exercise therapy influenced the incidence of admission after discharge in patients with T2DM undergoing IDSMES.

METHODS

This retrospective cohort study included patients with T2DM who underwent IDSMES between June 2011 and May 2015. Overall, 258 patients were included in this study. The exercise therapy program was implemented in June 2013. Accordingly, patients diagnosed between June 2011 and May 2013 were categorized as the non-exercise therapy program group, while those diagnosed between June 2013 and May 2015 were categorized as the exercise therapy program group. Outcomes were incident diabetes-related and all-cause admissions within 1 year of discharge. Multiple logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) of the exercise therapy program's impact on the outcomes.

RESULTS

Within 1 year of discharge, 27 (10.5%) patients underwent diabetes-related admissions and 62 (24.0%) underwent all-cause admissions. Multiple logistic regression analyses showed a significant association of the exercise therapy program with incident diabetes-related and allcause admissions [OR: 0.22 (95% CI: 0.08-0.59) and 0.44 (95% CI: 0.22-0.86), respectively].

CONCLUSION

Exercise therapy programs significantly lowered the incidences of diabetes-related and all-cause admissions. This indicates that implementing exercise therapy during hospitalization may be important for preventing admissions of patients with T2DM receiving IDSMES.

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

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

Effect of Chronic Disease Home Telehealth Monitoring in the Veterans Health Administration on Healthcare Utilization and Mortality

BACKGROUND

The high prevalence of chronic diseases, including congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and diabetes mellitus (DM), accounts for a large burden of cost and poor health outcomes in US hospitals, and home telehealth (HT) monitoring has been proposed to improve outcomes.

OBJECTIVE

To measure the association between HT initiation and 12-month inpatient hospitalizations, emergency department (ED) visits, and mortality in veterans with CHF, COPD, or DM.

DESIGN

Comparative effectiveness matched cohort study.

PATIENTS

Veterans aged 65 years and older treated for CHF, COPD, or DM.

MAIN MEASURES

We matched veterans initiating HT with veterans with similar demographics who did not use HT (1:3). Our outcome measures included a 12-month risk of inpatient hospitalization, ED visits, and all-cause mortality.

KEY RESULTS

A total of 139,790 veterans with CHF, 65,966 with COPD, and 192,633 with DM were included in this study. In the year after HT initiation, the risk of hospitalization was not different in those with CHF (adjusted odds ratio [aOR] 1.01, 95% confidence interval [95%CI] 0.98-1.05) or DM (aOR 1.00, 95%CI 0.97-1.03), but it was higher in those with COPD (aOR 1.15, 95%CI 1.09-1.21). The risk of ED visits was higher among HT users with CHF (aOR 1.09, 95%CI 1.05-1.13), COPD (1.24, 95%CI 1.18-1.31), and DM (aOR 1.03, 95%CI 1.00-1.06). All-cause 12-month mortality was lower in those initiating HT monitoring with CHF (aOR 0.70, 95%CI 0.67-0.73) and DM (aOR 0.79, 95%CI 0.75-0.83), but higher in COPD (aOR 1.08, 95%CI 1.00-1.16).

CONCLUSIONS

The initiation of HT was associated with increased ED visits, no change in hospitalizations, and lower all-cause mortality in patients with CHF or DM, while those with COPD had both higher healthcare utilization and all-cause mortality.

Obesity Metabolic Phenotypes and Unplanned Readmission Risk in Diabetic Kidney Disease: An Observational Study from the Nationwide Readmission Database

BACKGROUND AND AIM

Obesity is a potentially modifiable factor for reducing readmissions, with heterogeneity that varies according to the metabolic status. Our objective was to examine the independent or mutual relationship between obesity and metabolic abnormalities and diabetic kidney disease (DKD)-related hospitalizations.

METHODS

493,570 subjects with DKD were enrolled in the 2018 Nationwide Readmission Database (NRD, United States). The at-risk population was reclassified into refined obesity subtypes based on the body mass index (BMI) classification of metabolic abnormalities (hypertension and/or dyslipidemia) to investigate the 180 d readmission risk and hospitalization costs related to DKD.

RESULTS

The overall readmission rate was 34.1%. Patients with metabolic abnormalities, regardless of obesity, had a significantly higher risk of readmission compared to non-obese counterparts (adjusted HR, 1.11 [95% CI, 1.07-1.14]; 1.12 [95% CI, 1.08-1.15]). Hypertension appeared to be the only metabolic factor associated with readmission among individuals with DKD. Obesity without metabolic abnormalities was independently associated with readmission (adjusted HR,1.08 [1.01,1.14]), especially among males and those >65 years (adjusted HR,1.10 [1.01-1.21]; 1.20 [1.10-1.31]). Women or those ≤65 years with metabolic abnormalities (all p <0.050) had elevated readmission rates, regardless of obesity; however, no such trend was observed in obese subjects without metabolic abnormalities (adjusted HR, 1.06 [0.98,1.16]). Additionally, obesity and metabolic abnormalities were associated with elevated hospitalization costs (all p <0.0001).

CONCLUSIONS

Increased BMI and hypertension are positively associated with readmissions and related costs among patients with DKD, which should be considered in future studies.

Readmission Predictors in Patients With Type II Diabetes

BACKGROUND

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

Introduction

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

Methods

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

Results

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

Conclusions

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

Predictors of Rehospitalization and Mortality in Diabetes-Related Hospital Admissions

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

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

AIMS/INTRODUCTION

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Relationships of depression and anxiety to readmission rates among patients with diabetes from Harare and Parirenyatwa referral hospitals in Zimbabwe

BACKGROUND

The knowledge of determinants of readmission among individuals with diabetes minimises relapse and decreases diabetes associated morbidity and mortality.

OBJECTIVES

To explore the prevalence of depression and anxiety as well as determinants of readmission in individuals with diabetes from Harare, Zimbabwe.

METHODS

A cross sectional study was carried out at Parirenyatwa and Harare group of hospitals. Participants were recruited through purposive sampling and interviewed at the diabetic clinics. Depression and anxiety were measured using the Hospital Anxiety and Depression Scale. Binary logistic regression was used to determine predictors of readmission.

RESULTS

In total 65 participants took part, 36.9% were males. The mean age ±SD was 44.89±14.2 years. Anxiety affected 40% and 20% were at risk of anxiety, while depression was reported in 27.7% and 30.8% were at risk of depression. Depression [OR=0.64, 95%CI: 0.42-0.97 (p=0.037)] and checking of blood glucose [OR=0.06, 95%CI: 0.01-0.71 (p=0.025)] were significant negative predictors of readmission among diabetic patients while anxiety was a significant positive predictor OR=1.55, 95%CI: 1.09-2.21 (p=0.015).

CONCLUSIONS

Mental health conditions in people living with diabetes are factors contributing to increased re admissions and are more prevalent with aging. Psychotherapy and education interventions are recommended for the elderly diabetic population.

A family nurse-led intervention for reducing health services' utilization in individuals with chronic diseases: The ADVICE pilot study

Objectives

Intensive health services' utilization is common in older individuals affected by chronic diseases. This study assessed whether a structured family nurse-led educational intervention would be effective in reducing health services' use (readmissions and/or emergency service access) among older people affected by chronic conditions.

Methods

This is a non-randomized before-after pilot study. A sample of 78 patients was recruited from two general practices in Italy and 70 among them were followed for 8 months. Standard home care was provided during the first four months' period (months 1-4), followed by the educational intervention until the end of the study (months 5-8). The intervention, based on the teach-back method, consisted of by-weekly 60-min home sessions targeting aspects of the disease and its treatment, potential complications, medication adherence, and health behaviours. Rates of health services' use were collected immediately before (T0), and after the interventions (T1). Differences in utilization rates were examined by the McNemar's test. Potential factors associated with the risk of health services' use were explored with a Cox proportional hazard regression model.

Results

The sample (n = 78) was predominantly female (n = 50, 64.1%), and had a mean age of 76.2 (SD = 4.8) years. Diabetes mellitus was the most frequent disease (n = 27, 34.6%). McNemar's test indicated a significant reduction in health services' use at T1 (McNemar χ ² = 28.03, P < 0.001). Cox regressions indicated that time and patient education, as well as their interaction, were the only variables positively associated with the probability of health services' use.

Conclusion

A teach-back intervention led by a family nurse practitioner has the potential to reduce health services' use in older patients with chronic diseases.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Examining Racial Disparities in Diabetes Readmissions in the United States Military Health System

INTRODUCTION

Diabetes is one of the most common chronic conditions in the United States and has a cost burden over $120 billion per year. Readmissions following hospitalization for diabetes are common, particularly in minority patients, who experience greater rates of complications and lower quality healthcare compared to white patients. This study examines disparities in diabetes-related readmissions in the Military Health System, a universally insured, population of 9.5 million beneficiaries, who may receive care from military (direct care) or civilian (purchased care) facilities.

METHODS

The study identified a population of 7,605 adult diabetic patients admitted to the hospital in 2014. Diagnostic codes were used to identify hospital readmissions, and logistic regression was used to analyze associations among race, beneficiary status, patient or sponsor's rank, and readmissions at 30, 60, and 90 days.

RESULTS

A total of 239 direct care patients and 545 purchased care patients were included in our analyses. After adjusting for age and sex, we found no significant difference in readmission rates for black versus white patients; however, we found a statistically significant increase in the likelihood for readmission of Native American/Alaskan Native patients compared to white patients, which persisted in direct care at 60 days (adjusted odds ratio [AOR] 11.51, 95% CI 1.11-119.41) and 90 days (AOR 18.42, 95% CI 1.78-190.73), and in purchased care at 90 days (AOR 4.54, 95% CI 1.31-15.74).

CONCLUSION

Our findings suggest that universal access to healthcare alleviates disparities for black patients, while Native America/Alaskan Native populations may still be at risk of disparities associated with readmissions among diabetic patients in both the closed direct care system and the civilian fee for service purchased care system.

Patterns and predictors of antihyperglycemic intensification at hospital discharge for type 2 diabetic patients not on home insulin

BACKGROUND

Diabetes mellitus is a prevalent condition among hospitalized patients and the inpatient setting presents an opportunity for providers to review and adjust antihyperglycemic medications. We sought to describe practice patterns and predictors of antihyperglycemic intensification (AHI) at hospital discharge for type 2 diabetes mellitus (T2DM) patients not on home insulin.

METHODS

We conducted a retrospective study of adult patients with T2DM receiving either non-insulin antihyperglycemic (NIA) or no antihyperglycemic medications prior to admission who were hospitalized within two hospitals in the Johns Hopkins Health System from December 2015 to September 2016. Mean hospital glucose values and observed vs. individualized target hemoglobin A1C values (based on risk of mortality score) were used to define an indication for AHI. Multivariable logistic regression was used to identify predictors of AHI.

RESULTS

A total of 554 discharges of 475 unique patients were included. An indication for AHI was present in 104 (18.8%) of discharges, and AHI occurred in 30 (28.8%) of these discharges. Higher mean admission BG values and A1C, fewer pre-admission antihyperglycemic agents, involvement of the diabetes service, and admitting service were associated with AHI, while no association was observed with age, sex, race, risk of mortality and severity of illness scores, or length of stay. AHI was not associated with 30-day readmission.

CONCLUSION

An indication for AHI occurs relatively infrequently among hospitalized patients, but when present, AHI occurs in approximately 1 in 3 discharges. AHI appears to be related largely to the degree of hyperglycemia, and diabetes service involvement. Further studies are needed to understand the implications of AHI at hospital discharge on short and long-term outcomes in this population.