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

Collins, Jenna; Abbass, Ibrahim M.; Harvey, Raymond A.; Suehs, Brandon T.; Uribe, Claudia; Bouchard, Jonathan; Prewitt, Todd; DeLuzio, Tony; Allen, Elsie

doi:10.1080/03007995.2017.1330258

Cited by 42 publications

(36 citation statements)

References 15 publications

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“…Multiple methods for sampling hospitalizations and analyzing risk factors are used in studies that model predictors of readmission risk. Sampling only the rst discharge per patient is a commonly used approach [22][23][24][25][26][27][28][29][30][31][32] . This approach eliminates clusters of multiple hospitalizations per patient, enabling the valid analysis of such data by logistic regression.…”

Section: Discussionmentioning

confidence: 99%

“…Among the 7 studies that sampled all discharges in their respective datasets 7,12,33−37 , the mean readmission rate was 18.9% with a range of 16.0-21.5%. Among the 7 studies that sampled only rst discharges [22][23][24][25][26][27][28] , the mean readmission rate was 13.5% with a range of 10.0-17.1%. Given that in clinical practice a provider may be treating a patient experiencing their rst hospitalization or one of many hospitalizations, we believe it is more generalizable to sample all discharges for analysis so the entire experience of the study population is captured.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…Readmissions are attributed to e.g. falls, urinary tract infections or other inter‐current infections that could be prevented or managed in the home environment (Collins et al., ; Kim, Ross, Melkus, Zhao, & Boockvar, ; Raval et al., ). Adverse events due to sub‐optimal diabetes care in the home are avoidable if the care is coordinated, if there is optimal communication among services and if the individual and their family carers are supported (Caughey et al., ).…”

Section: Introductionmentioning

confidence: 99%

Integrative review: Patient safety among older people with diabetes in home care services

Haltbakk

Graue

Harris

et al. 2019

Journal of Advanced Nursing

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Aims To identify diabetes specific patient safety domains that need to be addressed to improve home care of older people; to assess research from primary studies to review evidence on patient safety in home care services for older people with diabetes. Design An integrative review. Data Sources Domains for patient safety in diabetes home care settings were identified by conducting two searches. We performed searches in: CINAHL, Medline, Embase, and Cochrane Library for the years 2000–2017. Review Methods The first search identified frameworks or models on patient safety in home care services published up to October 2017. The second search identified primary studies about older people with diabetes in the home care setting published between 2000–2017. Results Data from the 21 articles populated and refined 13 predetermined domains of patient safety in diabetes home care. These were used to explore how the domains interact to either increase or reduce risk. The domains constitute a model of associations between aspects of diabetes home care and adverse events. The results highlight a knowledge gap in safety for older persons with diabetes, influenced by e.g. hypoglycaemia, falls, pain, foot ulcers, cognitive impairment, depression, and polypharmacy. Moreover, providers’ inadequate diabetes‐specific knowledge and assessment skills contribute to the risk of adverse events. Conclusion Older persons with diabetes in home care are at risk of adverse events due to their reduced ability to self‐manage their condition, adverse medication effects, the family's ability to take responsibility or home care service's suboptimal approaches to diabetes care.

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“…Un indicador para valorar la adecuación del alta hospitalaria es mediante el número de reingresos, cuya incidencia global está entre el 8 y el 13% a los 30 días, mientras que en el caso de pacientes diabéticos asciende al 14-22% a los 30 días y al 40% a los 90 días del alta 15 . En DM tipo 2 se ha observado que las características del paciente y el uso que hace del sistema sanitario condicionarían el reingreso 16 . El grado de control metabólico podría condicionar la recuperación del primer evento, pero también desencadenar hiper o hipoglucemias que requieran reingreso.…”

Section: Introductionunclassified

Evaluation of a follow-up program for diabetes after hospital discharge

Errea

Diez

Echarri

et al. 2019

An Sist Sanit Navar

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

Cited by 42 publications

References 15 publications

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

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

Integrative review: Patient safety among older people with diabetes in home care services

Evaluation of a follow-up program for diabetes after hospital discharge

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