Validation and Comparison of Two 30-Day Re-Admission Prediction Models in Patients with Diabetes

Alamer, Ahmad; Patanwala, Asad E.; Aldayyen, Ali M.; Fazel, Maryam

doi:10.4158/ep-2019-0125

Cited by 5 publications

(8 citation statements)

References 23 publications

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“…There is a modestly increased risk of readmission associated with male gender, non-white race, and higher age. One of the strongest risk factors not considered in the meta-analysis is a history of readmission or multiple hospitalizations [ 10 , 19 , 20 , 23 ]. Abnormal values of serum sodium, creatinine, and hematocrit/hemoglobin are also reliable predictors of readmission [ 19 , 20 , 23 ].…”

Section: Epidemiology Of Readmissionsmentioning

confidence: 99%

“…One of the strongest risk factors not considered in the meta-analysis is a history of readmission or multiple hospitalizations [ 10 , 19 , 20 , 23 ]. Abnormal values of serum sodium, creatinine, and hematocrit/hemoglobin are also reliable predictors of readmission [ 19 , 20 , 23 ]. Several studies have shown that comorbidity burden, including microvascular and macrovascular diabetic complications, is associated with readmission risk [ 19 – 21 , 24 ].…”

Section: Epidemiology Of Readmissionsmentioning

confidence: 99%

“…In contrast to our external validation study, however, the DERRI TM was independently tested in another study that yielded better predictive performance than that reported in the original cohort. [ 23 ] In a case control study of 200 adults with diabetes hospitalized at an academic medical center in Arizona between 2014 and 2017, the DERRI TM was compared with the HOSPITAL model, a 30-day readmission risk prediction model not specifically developed in patients with diabetes. The DERRI TM had a C-statistic of 0.80, while the HOSPITAL had a C-statistic of 0.73, a difference that was nearly statistically significant ( p = 0.055).…”

Section: Strategies To Predict Readmission Risk Among Patients With Diabetesmentioning

confidence: 99%

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

Rubin

Shah

2021

Curr Diab Rep

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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. Summary 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.

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Section: Epidemiology Of Readmissionsmentioning

confidence: 99%

Section: Epidemiology Of Readmissionsmentioning

confidence: 99%

Section: Strategies To Predict Readmission Risk Among Patients With Diabetesmentioning

confidence: 99%

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

Rubin

Shah

2021

Curr Diab Rep

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“…Some of readmissions are avoidable although requires evidence-based treatments [6]. However, poor quality of care and/or ineffective transitions of care play a significant role [7]. As the quality of inpatient care is associated with early readmission, prediction of hospital readmission for diabetes patient would prove invaluable [1].…”

Section: Introductionmentioning

confidence: 99%

“…The United States (US) health system endures significant economic burden for diabetes care. This cost reached about $327 billion in 2017 [7]. The ability to predict patient readmissions will ultimately help the hospital to calculate and manage the quality of patient care [9].…”

Section: Introductionmentioning

confidence: 99%

Predicting Hospital Readmission of Diabetic Patients Using Machine Learning

Farajollahi

Mehmannavaz²,

Mehrjoo³

et al. 2021

Front Health Infor

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Introduction: Diabetes is a chronic disease associated with abnormal high levels of glucose in the blood. Diabetes make many kinds of complications, which also leads to a high rate of repeated admission of patients with diabetes. The goal of this study is to Predict hospital readmission of Diabetic patients with machine learning techniques.Material and Methods: The data used in the study are data obtained from the UCI Machine Learning Repository about diabetic patients. The dataset used contains 100,000 instances and it include 55 features from 130 hospitals in the United States for 10 years.Results: This article gets results from the final stages of evaluation. In this evaluation process, compared the performance of Decision tree, Random forest, Xgboost, k-Neighbors, adaboost and deep neural network with accuracy.Conclusion: The number of selected features by PCA-based feature selection method improve the predictive performance based on accuracy of deep learning and most machine learning models for predicting readmission. The improvement of machine learning models depended on the specific choice of the prediction model, number of selected features, and “k” for k-fold validation.

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