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

Enomoto, Laura M.; Shrestha, Deepika; Rosenthal, Meredith B.; Hollenbeak, Christopher S.; Gabbay, Robert A.

doi:10.1016/j.jdiacomp.2016.10.021

Cited by 49 publications

(62 citation statements)

References 26 publications

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“…Irrespective of the initial reason for admission, inpatients with diabetes act as a distinct cohort of patients with shared risk factors for adverse events [2]. People with diabetes are at a significantly increased risk of readmission following discharge [3,4]. Hospital readmissions rates are a psychological and physical burden to patients, and a financial burden on healthcare systems [5].…”

Section: Introductionmentioning

confidence: 99%

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

Robbins

Keung

Sambandam

et al. 2019

Journal of Diabetes and its Complications

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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.

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Section: Introductionmentioning

confidence: 99%

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

Robbins

Keung

Sambandam

et al. 2019

Journal of Diabetes and its Complications

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“…The most common multivariable modeling approach by far is logistic regression 5,38 . Interestingly, logistic regression is utilized in some studies that sample all discharges without acknowledging the probable violation of the independence assumption 13,34,37,39 . In addition, some studies of readmission risk factors that utilize logistic regression do not report how hospitalizations were sampled [40][41][42] .…”

Section: Discussionmentioning

confidence: 99%

Common sampling and modeling approaches to analyzing readmission risk that ignore clustering produce misleading results

Zhao

Tanner

Golden

et al. 2020

Preprint

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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 (CW) GEE 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 6 000–8 000 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.

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“…Especially patients with end-organ sequela had a longer duration of hospitalization. Enomoto et al 10 detected that comorbidities like congestive heart failure, rheumatoid diseases, peripheral vascular disease, renal diseases, cancer may affect length of stay and 30-day readmission. They support our findings.…”

Section: Discussionmentioning

confidence: 99%

Effects of Comorbidities, Inflammation and Glycaemic Control on Length of Hospital Stay/Potentially Avoidable Readmission Risk in Type 2 Diabetic Patients- A Retrospective Analysis

Karahan¹

2019

jemds

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BACKGROUND Patients with diabetes can have long hospitalization and frequent readmissions for different reasons. Predicting the duration of hospitalization and readmission risk may be helpful for management. We wanted to identify factors that affect the length of hospital stay and potentially avoidable readmission risk. METHODS 102 type 2 adults with diabetes that were hospitalized and discharged between July 2017 and July 2018 in Kırıkkale University, Department of Internal Medicine, were investigated retrospectively. HOSPITAL score was calculated for detecting 30-day readmission risk. Haemoglobin A1c for glycaemic control, C-reactive protein/serum albumin ratio (CAR) for inflammation, Charlson comorbidity score for multimorbidities, and age were researched as factors that affect the length of stay/ HOSPITAL score. RESULTS There were 70 female and 32 male patients. The median age of participants was 69. Median HbA1c was 9.2%, median Charlson comorbidity score was 6, median CAR was 3.33, median HOSPITAL score was 4. Significant positive correlation was detected between age, Charlson comorbidity score, CAR, HbA1c level and both hospital length of stay/HOSPITAL score by Spearman's correlation analysis. However, Charlson comorbidity score and CAR had significant values with both length of hospital stay/ HOSPITAL score according to the linear regression model. CONCLUSIONS Identifying the factors that affect readmission and length of stay, benefits hospitalized diabetes patient management. This can have favourable impact on costs, sources and approach. Type 2 diabetes patients can have multiple comorbidities and inflammatory conditions. Inflammation and comorbidity presence have negative effects on length of stay and avoidable readmission. Presence of these indices' in high levels should be considered high risk for readmission and these patients should be evaluated carefully.

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

Cited by 49 publications

References 26 publications

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

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

Common sampling and modeling approaches to analyzing readmission risk that ignore clustering produce misleading results

Effects of Comorbidities, Inflammation and Glycaemic Control on Length of Hospital Stay/Potentially Avoidable Readmission Risk in Type 2 Diabetic Patients- A Retrospective Analysis

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