Association of 30-Day All-Cause Readmission with Long-Term Outcomes in Hospitalized Older Medicare Beneficiaries with Heart Failure

Arundel, Cherinne; Lam, Phillip H.; Khosla, Rahul; Blackman, Marc R.; Fonarow, Gregg C.; Morgan, Charity J.; Zeng, Qing; Fletcher, Ross D.; Butler, Javed; Wu, Wen Chih; Deedwania, Prakash; Love, Thomas E.; White, Michel; Aronow, Wilbert S.; Anker, Stefan D.; Allman, Richard M.; Ahmed, Ali

doi:10.1016/j.amjmed.2016.06.018

Cited by 47 publications

(28 citation statements)

References 28 publications

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“…Readmission for decompensated heart failure not only impacts negatively quality of life and cost‐effectiveness of the MitraClip procedure, but is also associated with increased mortality in heart failure . In the ASCEND‐HF trial, rehospitalization correlated with all‐cause mortality with a hazard ratio of 2.38.…”

Section: Discussionmentioning

confidence: 99%

Predictors of rehospitalization after percutaneous edge‐to‐edge mitral valve repair by MitraClip implantation

Keßler

Seeger

Muche

et al. 2018

European J of Heart Fail

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After MitraClip implantation, annual rate of rehospitalization for decompensated heart failure was reduced by 65.8%. Baseline left ventricular ejection fraction, baseline troponin T and pre-procedural NYHA functional class are independent predictors for rehospitalization within the first year after MitraClip implantation. Patients readmitted for decompensated heart failure after MitraClip implantation had a 2.3-fold increased risk of all-cause mortality and a 3.3-fold increased risk of cardiovascular mortality compared to patients not requiring rehospitalization.

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Section: Discussionmentioning

confidence: 99%

Predictors of rehospitalization after percutaneous edge‐to‐edge mitral valve repair by MitraClip implantation

Keßler

Seeger

Muche

et al. 2018

European J of Heart Fail

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“…Heart failure (HF) is one of the leading causes for hospital admissions in the US [1][2][3][4] with high numbers of readmissions within 30 days of discharge [2][3][4]. Based on multiple hospitalization data sources, the yearly rate of 30 days allcause readmission after an HF hospitalization is approximately 23-24% [1,2,5], posing a huge burden on the healthcare system with an estimated cost of $17 billions of total Medicare expenditure [4,6]. Beyond the associated expenses and costs, readmissions have negative consequences on patients' health status, leading to complications and increased risk of disease progression [6].…”

Section: Introductionmentioning

confidence: 99%

“…Based on multiple hospitalization data sources, the yearly rate of 30 days allcause readmission after an HF hospitalization is approximately 23-24% [1,2,5], posing a huge burden on the healthcare system with an estimated cost of $17 billions of total Medicare expenditure [4,6]. Beyond the associated expenses and costs, readmissions have negative consequences on patients' health status, leading to complications and increased risk of disease progression [6]. Efforts toward quality improvement such as introducing programs that incentivize and penalize hospitals based on the yearly readmission rate have been the focus of researchers and policy makers [2,7].…”

Section: Introductionmentioning

confidence: 99%

Neural networks versus Logistic regression for 30 days all-cause readmission prediction

Allam

Nagy

Thoma

et al. 2019

Sci Rep

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Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1) explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2) to examine the additive value of patients’ hospitalization timelines on prediction performance. Based on data from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and 343,328 HF admissions (67% of total admissions), we trained and tested our predictive readmission models following a stratified 5-fold cross-validation scheme. Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0.642 AUC (95% CI, 0.640–0.645). Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower performance, with a non-timeline based model (MLP) performing worst. A competitive model based on logistic regression with LASSO achieved a performance of 0.643 AUC (95% CI, 0.640–0.646). We conclude that data from patient timelines improve 30 day readmission prediction, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets.

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“…Highest-cost of hospitalization was also associated to 5 times longer LOS, 9 times more expensive and higher in-hospital mortality (5.6% vs 3.5%) when compared with lowest-cost hospitalizations [3]. Among Medicare beneficiaries hospitalized for congestive heart failure, 30-day all-cause readmission was associated with a higher risk of subsequent all-cause mortality, higher number of cumulative all-cause readmission, longer cumulative length of stay, and higher cumulative cost [4]. Older patients, with median age of 72 years hospitalized with acute CHF, had a higher prevalence of comorbidities, including hypertension and atrial fibrillation [5].…”

Section: Introductionmentioning

confidence: 89%

Prevalence of Alzheimer Disease in Hospitalized Patients with Congestive Heart Failure

Okunji¹,

Js²,

Nm³

et al. 2017

Int J Nurs Clin Pract

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Background: Alzheimer's disease (AD) may be the most critical medical condition of the 21st century in part because it affects more than 5 million Americans, including one out of eight Americans aged 65 or older, and nearly half of those being over the age of 85. It is also recognized that cardiovascular disease (CVD) risks can catalyze the development of AD. AD and congestive heart failure (CHF) often occur together and thus increase the cost of care and health resources. We investigated the prevalence of AD in patients hospitalized with CHF. In addition, factors that affect the outcomes of this special population were determined. Methods: Data from the National Inpatient Samples (NIS) were extracted and analyzed using ICD 9 codes (CHF 428, PD 331) for the main diagnosis. For continuous variables, we calculated the mean and standard deviations and evaluated significant differences of these factors by Alzheimer disease status using the t-test. For categorical variables, we obtained the counts (proportions) and evaluated significant differences using the Chi-square and Fisher's exact test Propensity score was utilized to match age, gender and race using logistic model for hospital death and generalized linear model for length of stay (LOS) and hospital charges. Results: The overall characteristics of matched participants with CHF and AD status showed that average age of inpatients was ~84 (SD=6.31). The prevalence of inpatients with both CHF and AD was significant (p < .0001) for females, 62.91% (n = 12,054) and for males, 37.09% (n = 7,107). White patients with CHF and AD were predominant with 76.20% (14,600) when compared with other races. While diabetes (26.05%), obstructive sleep apnea (5.67%), morbid-obesity (3.36%) were prevalent for inpatient without AD, renal insufficiency (3.60%) and stroke (2.10%) were prevalent in patient with AD. Patients with low income ($1 -$38,999) were admitted more with 6,290 (33.40%) than those with higher income ($39,000 -> $63,000). Finally, patient with CHF and AD stayed longer with higher mortality rates than those without AD, p< 0.0001. Age and race significantly affected all the outcomes, p< 0.0001 while gender showed significance for hospital death and charges (p< 0.0001). Hospital death was not affected by patient's household income but its interesting to note that LOS was affected by patients with household income between $39,000 -$62,999 and hospital charge by patients with higher household incomes from $48.000 and above. Stroke was the only comorbidity that significantly (p<0.0001) affected hospital death while diabetes significantly (p< 0.0001) affected LOS. However, diabetes, stroke and morbid obesity significantly (p< 0.0068) influenced the patient hospital charges. For hospital characteristics, it is important to note that LOS and hospital charge were significantly (p =0.0001) affected irrespective of the hospital teaching status. Conclusion:The prevalence of CHF and AD may be higher in females than males, with white patients admitted more often than other...

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Association of 30-Day All-Cause Readmission with Long-Term Outcomes in Hospitalized Older Medicare Beneficiaries with Heart Failure

Cited by 47 publications

References 28 publications

Predictors of rehospitalization after percutaneous edge‐to‐edge mitral valve repair by MitraClip implantation

Predictors of rehospitalization after percutaneous edge‐to‐edge mitral valve repair by MitraClip implantation

Neural networks versus Logistic regression for 30 days all-cause readmission prediction

Prevalence of Alzheimer Disease in Hospitalized Patients with Congestive Heart Failure

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