The readmission rates in patients with versus those without diabetes mellitus at an urban teaching hospital

Sonmez, Halis; Kambo, Varinder; Avtanski, Dimiter; Lutsky, Larry; Poretsky, Leonid

doi:10.1016/j.jdiacomp.2017.07.006

Cited by 22 publications

(24 citation statements)

References 11 publications

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“…Our study is one of the first to evaluate the impact of physical and cognitive status on the risk of subsequent events in elderly patients hospitalized for CVD. In common with previous studies, we found that elder patients with HF [26], DM [27], previous stroke [28], non-married [29], severe MNA-SF [30] and decreasing FT3 [31] The detection of impaired physical performance and cognition in elder patients with CVD is essential for clinical management and therapeutic decision. Increasing age is an obvious risk factor for frailty [22], and patients admitted to hospital for CVD are more older.…”

Section: Discussionsupporting

confidence: 86%

Predicting non-elective hospital readmission or death using a composite assessment of cognitive impairment and frailty in elderly inpatients with cardiovascular diseases

Yao

Wang

Yang

et al. 2020

European Heart Journal

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Background Cardiovascular disease (CVD) and related complications are significant healthcare problems in the growing elderly population. Metabolic factors are the predominant risk factors for CVD, sedentary behaviour and physical inactivity are major modifiable risk factors for CVD. Age-associated cognitive decline and impairment are associated with an increased mortality. Cognitive and physical components of frailty have pathophysiologic rationale as risk factors for CVD. There is a clinical need to identify more practical screens that can assist us to definite cognitive impairment and physical frailty, then to determine which patients with CVD are at high risk of adverse outcomes, early management of these high-risk patients can reduce readmission rates, healthcare spending, and improve quality of care. Purpose We aimed to assess the utility of a combination developed using the mini-mental state examination + clock drawing test and the Fried phenotype for predicting non-elective hospital readmission or death within 6-month in elderly inpatients with CVD. Methods A single center prospective cohort was conducted between September 2018 and February 2019. Inpatients aged 65 years or older were recruited. All enrolled patients received a comprehensive geriatric assessment during hospitalization. The Kaplan-Meier curves were used to estimate the cumulative incidence of events. The multivariate Cox regression model was used to analyze the association between frailty and cognitive status and the non-elective hospital readmission or death. Results A total of 542 patients were included; and a total of 113 patients (20.9%) died or were readmitted at 6-month. Overall 20% screened positive for cognitive impairment, including 8% were cognitive impairment combined with physical frailty, which were more older, more common in women and non-married group, had a lower education and a higher risk of malnutrition. Frail participants with normal (hazard ratio [HR]: 1.73, 95% confidence intervals [CI]: 1.06–2.82, P=0.028) and impaired cognition (HR: 2.50, 95% CI: 1.27–4.91, P=0.008) had a higher risk of non-elective hospital readmission or death than robust patients, after adjustment for age, sex, education level, marital status, the presence of diabetes mellitus, heart failure, and previous stroke. Conclusions The presence of physical frailty and cognitive frailty were powerful predictors of non-elective hospital readmission or death in elderly inpatients with CVD, and taking cognitive impairment into account in the frailty model may allow better prediction of adverse outcomes of frailty in the short time. Kaplan-Meier survival curves Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Beijing Municipal Science & Technology Commission, Chinese Academy of Medical Sciences

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Section: Discussionsupporting

confidence: 86%

Predicting non-elective hospital readmission or death using a composite assessment of cognitive impairment and frailty in elderly inpatients with cardiovascular diseases

Yao

Wang

Yang

et al. 2020

European Heart Journal

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“…Hospital readmission rates in patients with diabetes are approximately twice as high as in patients without diabetes (3)(4)(5)(6) and account for 20% of all US hospital readmissions (7). Reducing readmission rates therefore is important not only because of potential benefits to patients but also because of the impact on the cost of diabetes care.…”

Section: Introductionmentioning

confidence: 99%

The Effects of a Comprehensive Multidisciplinary Outpatient Diabetes Program on Hospital Readmission Rates in Patients with Diabetes: A Randomized Controlled Prospective Study

et al. 2020

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“…Multiple studies have been performed aiming to identify statistically significant risk factors for poor outcomes, when patients with diabetes are discharged from hospital [5,6]. These studies typically identify risk factors for generalised populations of inpatients with diabetes [7,8], or for an individual specific cohorts who may have been admitted for a particular condition or group of conditions [9][10][11]. Almost universally, these studies report statistically significant risk factors for an individual outcomes (either readmission or mortality) and report unstandardized effect size measures, usually as odds ratios.…”

Section: Introductionmentioning

confidence: 99%

Application of standardised effect sizes to hospital discharge outcomes for people with diabetes

Robbins

Keung

Sambandam

et al. 2020

BMC Med Inform Decis Mak

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Background: Patients with diabetes are at an increased risk of readmission and mortality when discharged from hospital. Existing research identifies statistically significant risk factors that are thought to underpin these outcomes. Increasingly, these risk factors are being used to create risk prediction models, and target risk modifying interventions. These risk factors are typically reported in the literature accompanied by unstandardized effect sizes, which makes comparisons difficult. We demonstrate an assessment of variation between standardised effect sizes for such risk factors across care outcomes and patient cohorts. Such an approach will support development of more rigorous risk stratification tools and better targeting of intervention measures. Methods: Data was extracted from the electronic health record of a major tertiary referral centre, over a 3-year period, for all patients discharged from hospital with a concurrent diagnosis of diabetes mellitus. Risk factors selected for extraction were pre-specified according to a systematic review of the research literature. Standardised effect sizes were calculated for all statistically significant risk factors, and compared across patient cohorts and both readmission & mortality outcome measures. Results: Data was extracted for 46,357 distinct admissions patients, creating a large dataset of approximately 10, 281,400 data points. The calculation of standardized effect size measures allowed direct comparison. Effect sizes were noted to be larger for mortality compared to readmission, as well as for being larger for surgical and type 1 diabetes cohorts of patients. Conclusions: The calculation of standardised effect sizes is an important step in evaluating risk factors for healthcare events. This will improve our understanding of risk and support the development of more effective risk stratification tools to support patients to make better informed decisions at discharge from hospital.

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The readmission rates in patients with versus those without diabetes mellitus at an urban teaching hospital

Cited by 22 publications

References 11 publications

Predicting non-elective hospital readmission or death using a composite assessment of cognitive impairment and frailty in elderly inpatients with cardiovascular diseases

Predicting non-elective hospital readmission or death using a composite assessment of cognitive impairment and frailty in elderly inpatients with cardiovascular diseases

The Effects of a Comprehensive Multidisciplinary Outpatient Diabetes Program on Hospital Readmission Rates in Patients with Diabetes: A Randomized Controlled Prospective Study

Application of standardised effect sizes to hospital discharge outcomes for people with diabetes

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