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

The effect of diabetes mellitus on perioperative outcomes after colorectal resection: a national cohort study

BACKGROUND

Diabetes mellitus is a significant modulator of postoperative outcomes and is an important risk factor in the patient selection process. We aimed to investigate the effect of diabetes mellitus and use of insulin on outcomes after colorectal resection using a national cohort.

METHODS

Adults with a recorded colorectal resection in England between 2010 and 2020 were identified from Hospital Episode Statistics data linked to the Clinical Practice Research Database. The primary outcome was 90-day mortality. Secondary outcomes included hospital length of stay (LOS) and readmission within 90 days.

RESULTS

Of the 106 139 (52 875, 49.8% male) patients included, diabetes mellitus was prevalent in 10 931 (10.3%), 2145 (19.6%) of whom had a record of use of insulin. Unadjusted 90-day mortality risk was 5.7%, with an increased adjusted hazard ratio (aHR) for people with diabetes mellitus (aHR 1.28, 95% confidence interval [CI] 1.19-1.37, P<0.001). This risk was higher in both people with diabetes using insulin (aHR 1.51, 95% CI 1.31-1.74, P<0.001) and not using insulin (aHR 1.22, 95% CI 1.13-1.33, P<0.001), compared with those without diabetes. Ninety-day readmission occurred in 20 542 (19.4%) patients and this was more likely in those with diabetes mellitus (aHR 1.23, 95% CI 1.18-1.29, P<0.001). Median (inter-quartile range) LOS was 8 (5-15) days and was higher in people with diabetes mellitus (adjusted time ratio 1.10, 95% CI 1.08-1.11, P<0.001).

CONCLUSIONS

People with diabetes mellitus undergoing colorectal resection are at a higher risk of 90-day mortality, prolonged LOS, and 90-day readmission, with use of insulin associated with additional risk.

Risk factors at admission of in-hospital dysglycemia, mortality, and readmissions in patients with type 2 diabetes and pneumonia

AIMS

In-hospital dysglycemia is associated with adverse outcomes. Identifying patients at risk of in-hospital dysglycemia early on admission may improve patient outcomes.

METHODS

We analysed 117 inpatients admitted with pneumonia and type 2 diabetes monitored by continuous glucose monitoring. We assessed potential risk factors for in-hospital dysglycemia and adverse clinical outcomes.

RESULTS

Time in range (3.9-10.0 mmol/l) decreased by 2.9 %-points [95 % CI 0.7-5.0] per 5 mmol/mol [2.6 %] increase in admission haemoglobin A1c, 16.2 %-points if admission diabetes therapy included insulin therapy [95 % CI 2.9-29.5], and 2.4 %-points [95 % CI 0.3-4.6] per increase in the Charlson Comorbidity Index (CCI) (integer, as a measure of severity and amount of comorbidities). Thirty-day readmission rate increased with an IRR of 1.24 [95 % CI 1.06-1.45] per increase in CCI. In-hospital mortality risk increased with an OR of 1.41 [95 % CI 1.07-1.87] per increase in Early Warning Score (EWS) (integer, as a measure of acute illness) at admission.

CONCLUSIONS

Dysglycemia among hospitalised patients with pneumonia and type 2 diabetes was associated with high haemoglobin A1c, insulin treatment before admission, and the amount and severity of comorbidities (i.e., CCI). Thirty-day readmission rate increased with high CCI. The risk of in-hospital mortality increased with the degree of acute illness (i.e., high EWS) at admission. Clinical outcomes were independent of chronic glycemic status, i.e. HbA1c, and in-hospital glycemic status.

Predictors of readmission and mortality in adults with diabetes or stress hyperglycemia after initial hospitalization for COVID-19

INTRODUCTION

We previously reported predictors of mortality in 1786 adults with diabetes or stress hyperglycemia (glucose>180 mg/dL twice in 24 hours) admitted with COVID-19 from March 2020 to February 2021 to five university hospitals. Here, we examine predictors of readmission.

RESEARCH DESIGN AND METHODS

Data were collected locally through retrospective reviews of electronic medical records from 1786 adults with diabetes or stress hyperglycemia who had a hemoglobin A1c (HbA1c) test on initial admission with COVID-19 infection or within 3 months prior to initial admission. Data were entered into a Research Electronic Data Capture (REDCap) web-based repository, and de-identified. Descriptive data are shown as mean±SD, per cent (%) or median (IQR). Student's t-test was used for comparing continuous variables with normal distribution and Mann-Whitney U test was used for data not normally distributed. X2 test was used for categorical variable.

RESULTS

Of 1502 patients who were alive after initial hospitalization, 19.4% were readmitted; 90.3% within 30 days (median (IQR) 4 (0-14) days). Older age, lower estimated glomerular filtration rate (eGFR), comorbidities, intensive care unit (ICU) admission, mechanical ventilation, diabetic ketoacidosis (DKA), and longer length of stay (LOS) during the initial hospitalization were associated with readmission. Higher HbA1c, glycemic gap, or body mass index (BMI) were not associated with readmission. Mortality during readmission was 8.0% (n=23). Those who died were older than those who survived (74.9±9.5 vs 65.2±14.4 years, p=0.002) and more likely had DKA during the first hospitalization (p<0.001). Shorter LOS during the initial admission was associated with ICU stay during readmission, suggesting that a subset of patients may have been initially discharged prematurely.

CONCLUSIONS

Understanding predictors of readmission after initial hospitalization for COVID-19, including older age, lower eGFR, comorbidities, ICU admission, mechanical ventilation, statin use and DKA but not HbA1c, glycemic gap or BMI, can help guide treatment approaches and future research in adults with diabetes.

Feature selection and risk prediction for diabetic patients with ketoacidosis based on MIMIC-IV

Background

Diabetic ketoacidosis (DKA) is a frequent acute complication of diabetes mellitus (DM). It develops quickly, produces severe symptoms, and greatly affects the lives and health of individuals with DM.This article utilizes machine learning methods to examine the baseline characteristics that significantly contribute to the development of DKA. Its goal is to identify and prevent DKA in a targeted and early manner.

Methods

This study selected 2382 eligible diabetic patients from the MIMIC-IV dataset, including 1193 DM patients with ketoacidosis and 1186 DM patients without ketoacidosis. A total of 42 baseline characteristics were included in this research. The research process was as follows: Firstly, important features were selected through Pearson correlation analysis and random forest to identify the relevant physiological indicators associated with DKA. Next, logistic regression was used to individually predict DKA based on the 42 baseline characteristics, analyzing the impact of different physiological indicators on the experimental results. Finally, the prediction of ketoacidosis was performed by combining feature selection with machine learning models include logistic regression, XGBoost, decision tree, random forest, support vector machine, and k-nearest neighbors classifier.

Results

Based on the importance analysis conducted using different feature selection methods, the top five features in terms of importance were identified as mean hematocrit (haematocrit_mean), mean hemoglobin (haemoglobin_mean), mean anion gap (aniongap_mean), age, and Charlson comorbidity index (charlson_comorbidity_index). These features were found to have significant relevance in predicting DKA. In the individual prediction using logistic regression, these five features have been proven to be effective, with F1 scores of 1.000 for hematocrit mean, 0.978 for haemoglobin_mean, 0.747 for age, 0.692 for aniongap_mean and 0.666 for charlson_comorbidity_index. These F1 scores indicate the effectiveness of each feature in predicting DKA, with the highest score achieved by mean hematocrit. In the prediction of DKA using machine learning models, including logistic regression, XGBoost, decision tree, and random forest demonstrated excellent results, achieving an F1 score of 1.000. Additionally, by applying feature selection techniques, noticeable improvements were observed in the experimental performance of the support vector machine and k-nearest neighbors classifier.

Conclusion

The study found that hematocrit, hemoglobin, anion gap, age, and Charlson comorbidity index are closely associated with ketoacidosis. In clinical practice, these five baseline characteristics should be given with the special attention to achieve early detection and treatment, thus reducing the incidence of the disease.

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.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

Epidemiological factors affecting outpatient department service utilization and hospitalization in patients with diabetes: A time-series analysis from an Ethiopian hospital between 2018 and 2021

Background

The burden of diabetes-related deaths reached two million in 2019 globally. Accessibility to health care services and adherence to follow-up and therapy are key to improving outcomes for diabetic patients. We aimed to assess outpatient department (OPD) service utilization and diabetes-related hospitalizations over a period of 44 months.

Methods

A retrospective cohort study was conducted on OPD visits and hospitalizations recorded between January 1, 2018, and August 31, 2021, at the St Luke Catholic Hospital (Ethiopia). All diabetic patients were included in the analysis. A linear regression model was used for univariate analysis of OPD visits and hospitalizations and their association with potential predictors. The autoregressive integrated moving average (ARIMA) method was applied to both the time series of OPD visits and hospitalizations. Potential predictors were sociodemographic factors, COVID-19 cases, mean monthly temperature and precipitations.

Results

In the time series analysis, OPD visits increased over time (P < 0.01) while hospitalizations were stable. The time series model was ARIMA (0,1,1) for OPD visits and ARIMA (0,0,0) for hospitalizations. There were 1685 diabetes OPD patients (F = 732, 43%). Females had an average of 16% fewer OPD accesses per month (P < 0.01) and a lower number of hospitalizations per month (P = 0.03). There were 801 patients missing follow-up (48%). The time between follow-up increased with age (P < 0.01). OPD visits decreased differently by geographic area as COVID-19 cases increased (P < 0.01). There were 57 fewer forecast OPD visits per month on average using COVID-19 cases as ARIMA regressor. The odds ratio (OR) of new diagnosis at hospitalization was lower in patients with type 2 diabetes (OR = 0.26, 95% CI = 0.14-0.49, P = 0.02).

Conclusions

Despite an increase in OPD visits for diabetic patients over the study period, the number of losses at follow-up and diagnoses at hospitalization remains high. Female sex, older age, and COVID-19 were associated with impaired OPD service accessibility. Primary health care should be implemented to achieve better health coverage and improve diabetes management.

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

OBJECTIVE

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

RESEARCH DESIGN AND SETTING

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

RESULTS

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

CONCLUSION

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

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.

Associations Between Implementation of the Caregiver Advise Record Enable (CARE) Act and Health Service Utilization for Older Adults with Diabetes: Retrospective Observational Study

BACKGROUND

The Caregiver Advise Record Enable (CARE) Act is a state level law that requires hospitals to identify and educate caregivers ("family members or friends") upon discharge.

OBJECTIVE

This study examined the association between the implementation of the CARE Act in a Pennsylvania health system and health service utilization (ie, reducing hospital readmission, emergency department [ED] visits, and mortality) for older adults with diabetes.

METHODS

The key elements of the CARE Act were implemented and applied to the patients discharged to home. The data between May and October 2017 were pulled from inpatient electronic health records. Likelihood-ratio chi-square tests and multivariate logistic regression models were used for statistical analysis.

RESULTS

The sample consisted of 2591 older inpatients with diabetes with a mean age of 74.6 (SD 7.1) years. Of the 2591 patients, 46.1% (n=1194) were female, 86.9% (n=2251) were White, 97.4% (n=2523) had type 2 diabetes, and 69.5% (n=1801) identified a caregiver. Of the 1801 caregivers identified, 399 (22.2%) received discharge education and training. We compared the differences in health service utilization between pre- and postimplementation of the CARE Act; however, no significance was found. No significant differences were detected from the bivariate analyses in any outcomes between individuals who identified a caregiver and those who declined to identify a caregiver. After adjusting for risk factors (multivariate analysis), those who identified a caregiver (12.2%, 219/1801) was associated with higher rates of 30-day hospital readmission than those who declined to identify a caregiver (9.9%, 78/790; odds ratio [OR] 1.38, 95% CI 1.04-1.87; P=.02). Significantly lower rates were detected in 7-day readmission (P=.02), as well as 7-day (P=.03) and 30-day (P=.01) ED visits, among patients with diabetes whose identified caregiver received education and training than those whose identified caregiver did not receive education and training in the bivariate analyses. However, after adjusting for risk factors, no significance was found in 7-day readmission (OR 0.53, 95% CI 0.27-1.05; P=.07), 7-day ED visit (OR 0.63, 95% CI 0.38-1.03; P=.07), and 30-day ED visit (OR 0.73, 95% CI 0.52-1.02; P=.07). No significant associations were found for other outcomes (ie, 30-day readmission and 7-day and 30-day mortality) in both the bivariate and multivariate analyses.

CONCLUSIONS

Our study found that the implementation of the CARE Act was associated with certain health service utilization. The identification of caregivers was associated with higher rates of 30-day hospital readmission in the multivariate analysis, whereas having identified caregivers who received discharge education was associated with lower rates of readmission and ED visit in the bivariate analysis.

Acceptance and Effect of Continuous Glucose Monitoring on Discharge From Hospital in Patients With Type 2 Diabetes: Open-label, Prospective, Controlled Study

Background

Continuous glucose monitors (CGM) can provide detailed information on glucose excursions. There is little information on safe transitioning from hospital back to the community for patients who have had diabetes therapies adjusted in hospital and it is unclear whether newer technologies may facilitate this process.

Objective

Our aim was to determine whether offering CGM on discharge would be acceptable and if CGM initiated on hospital discharge in people with type 2 diabetes (T2DM) would reduce hospital re-presentations at 1 month.

Methods

This was an open-label study. Adult inpatients with T2DM, who were to be discharged home and required postdischarge glycemic stabilization, were offered usual care consisting of clinic review at 2 weeks and at 3 months. In addition to usual care, participants in the intervention arm were provided with a Libre flash glucose monitoring system (Abbott Australia). An initial run-in phase for the first 20 participants was planned, where all consenting participants were enrolled in an active arm. Subsequently, all participants were to be randomized to the active arm or usual care control group.

Results

Of 237 patients screened during their hospital admission, 34 had comorbidities affecting cognition that prevented informed consent and affected their ability to learn to use the CGM device. In addition, 21 were not able to be approached as the material was only in English. Of 101 potential participants who fulfilled eligibility criteria, 19 provided consent and were enrolled. Of the 82 patients who declined to participate, 31 advised that the learning of a new task toward discharge was overwhelming or too stressful and 26 were not interested, with no other details. Due to poor recruitment, the study was terminated without entering the randomization phase to determine whether CGM could reduce readmission rate.

Conclusions

These results suggest successful and equitable implementation of telemedicine programs requires that any human factors such as language, cognition, and possible disengagement be addressed. Recovery from acute illness may not be the ideal time for introduction of newer technologies or may require more novel implementation frameworks.

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.

Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina

Readmissions constitute a major health care burden among peripheral artery disease (PAD) patients. This study aimed to 1) estimate the zip code tabulation area (ZCTA)-level prevalence of readmission among PAD patients and characterize the effect of covariates on readmissions; and (2) identify hotspots of PAD based on estimated prevalence of readmission. Thirty-day readmissions among PAD patients were identified from the South Carolina Revenue and Fiscal Affairs Office All Payers Database (2010-2018). Bayesian spatial hierarchical modeling was conducted to identify areas of high risk, while controlling for confounders. We mapped the estimated readmission rates and identified hotspots using local Getis Ord (G*) statistics. Of the 232,731 individuals admitted to a hospital or outpatient surgery facility with PAD diagnosis, 30,366 (13.1%) experienced an unplanned readmission to a hospital within 30 days. Fitted readmission rates ranged from 35.3 per 1000 patients to 370.7 per 1000 patients and the risk of having a readmission was significantly associated with the percentage of patients who are 65 and older (0.992, 95%CI: 0.985-0.999), have Medicare insurance (1.013, 1.005-1.020), and have hypertension (1.014, 1.005-1.023). Geographic analysis found significant variation in readmission rates across the state and identified priority areas for targeted interventions to reduce readmissions.

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.

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

BACKGROUND

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

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

PURPOSE OF REVIEW

Acute care re-utilization, i.e., hospital readmission and post-discharge Emergency Department (ED) use, is a significant driver of healthcare costs and a marker for healthcare quality. Diabetes is a major contributor to acute care re-utilization and associated costs. The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization.

RECENT FINDINGS

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

Rate and Predictors of 30-day Readmission Following Diabetic Ketoacidosis in Type 1 Diabetes Mellitus: A US Analysis

CONTEXT

Diabetic ketoacidosis (DKA) is a serious endocrine emergency, associated with morbidity and mortality. Readmissions play a significant but sometimes preventable role in healthcare cost burden on the US.

OBJECTIVE

This study aimed to describe rates and characteristics of nonelective 30-day readmission among adult patients with diabetes mellitus type 1 (T1DM) hospitalized for DKA and also identify predictors of readmission.

METHODS

The study analyzed the 2018 Nationwide Readmission Database. DKA hospitalizations in patients with T1DM were classified using International Classification of Diseases, Tenth Revision, Clinical Modification codes. We utilized chi-squared tests to compare baseline characteristics between readmissions and index hospitalizations. Multivariable Cox regression was employed to identify independent predictors of readmission. Following this, we developed a 30-day readmission risk scoring system based on independent predictors.

RESULTS

The 30-day all-cause readmission rate for DKA was 19.4%. A majority of patients (64.8%) had DKA as the principal diagnosis on readmission. Readmitted patients had a significantly higher mean age (35.3 vs 34.9 years, P = .018) and a higher proportion of females (52.8 vs 49.6%, P < .001) than the index admission. Readmission following DKA was associated with higher odds of inpatient mortality (0.69 vs 0.24%, OR 2.84, 95% CI 1.99-4.06, P < .001). Independent predictors of 30-day all-cause readmission included female sex, index hospitalizations with Charlson Comorbidity Index (CCI) score of 3 or greater, and being discharged against medical advice (AMA).

CONCLUSION

The readmission rate for DKA in T1DM patients is high, and most patients have DKA as the principal diagnosis on readmission. A CCI equal to or greater than 3, hypertension, female sex, and being discharged AMA were significant predictors of readmission.

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.