Risk Factors for Hospital Readmission and Death After Discharge of Older Adults from Acute Geriatric Units: Taking the Rank of Admission into Account

Visade, Fabien; Babykina, Génia; Puisieux, François; Bloch, Frédéric; Charpentier, Arthur; Delecluse, Christophe; Loggia, Gilles; Lescure, Pascale; Attier-Zmudka, Jadwiga; Gaxatte, Cédric; Deschasse, Guillaume; Beuscart, Jean-Baptiste

doi:10.2147/cia.s327486

Cited by 15 publications

(10 citation statements)

References 32 publications

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“…In another nationwide study of hospital admissions among seniors, functional impairments were associated with higher readmission rates, with a trend of rising odds of readmission with increasing severity of functional impairment [10]. In contrast, a prospective cohort study of patients above 75 years reported that malnutrition and loss of independence in ADL were associated only with risk of death but not hospital readmissions [12]. Several cohort studies have reported a strong association between polypharmacy and important clinical outcomes such as potentially inappropriate medications (PIMs), hospital readmission, and mortality [44][45][46].…”

Section: Cga and Readmissionmentioning

confidence: 93%

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Prediction of 30-Day Readmission in Hospitalized Older Adults Using Comprehensive Geriatric Assessment and LACE Index and HOSPITAL Score

Sun

Chou

Lee

et al. 2022

IJERPH

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(1) Background: Elders have higher rates of rehospitalization, especially those with functional decline. We aimed to investigate potential predictors of 30-day readmission risk by comprehensive geriatric assessment (CGA) in hospitalized patients aged 65 years or older and to examine the predictive ability of the LACE index and HOSPITAL score in older patients with a combination of malnutrition and physical dysfunction. (2) Methods: We included patients admitted to a geriatric ward in a tertiary hospital from July 2012 to August 2018. CGA components including cognitive, functional, nutritional, and social parameters were assessed at admission and recorded, as well as clinical information. The association factors with 30-day hospital readmission were analyzed by multivariate logistic regression analysis. The predictive ability of the LACE and HOSPITAL score was assessed using receiver operator characteristic curve analysis. (3) Results: During the study period, 1509 patients admitted to a ward were recorded. Of these patients, 233 (15.4%) were readmitted within 30 days. Those who were readmitted presented with higher comorbidity numbers and poorer performance of CGA, including gait ability, activities of daily living (ADL), and nutritional status. Multivariate regression analysis showed that male gender and moderately impaired gait ability were independently correlated with 30-day hospital readmissions, while other components such as functional impairment (as ADL) and nutritional status were not associated with 30-day rehospitalization. The receiver operating characteristics for the LACE index and HOSPITAL score showed that both predicting scores performed poorly at predicting 30-day hospital readmission (C-statistic = 0.59) and did not perform better in any of the subgroups. (4) Conclusions: Our study showed that only some components of CGA, mobile disability, and gender were independently associated with increased risk of readmission. However, the LACE index and HOSPITAL score had a poor discriminating ability for predicting 30-day hospitalization in all and subgroup patients. Further identifiers are required to better estimate the 30-day readmission rates in this patient population.

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Section: Cga and Readmissionmentioning

confidence: 93%

“…Moreover, malnutrition, frailty, and polypharmacy were associated with hospital readmissions. CGA-based interventions have also been proven to reduce mortality and institutionalization in patients admitted to an acute geriatric ward [7,[11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of 30-Day Readmission in Hospitalized Older Adults Using Comprehensive Geriatric Assessment and LACE Index and HOSPITAL Score

Sun

Chou

Lee

et al. 2022

IJERPH

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“…The DAMAGE study’s methodology has been described in detail elsewhere. 15 , 16 The main goal of the DAMAGE cohort was to study outcomes after a stay in the UGA and we included 3509 patients for the primary objective analysis. This study is a post-hoc analysis.…”

Section: Methodsmentioning

confidence: 99%

Factors Associated with Transfer from an Acute Geriatric Unit to a Post-Acute Care Facility among Community-Dwelling Patients: Results from the DAMAGE Cohort

Deschasse¹,

Drumez²,

Visade³

et al. 2022

CIA

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Background Acute geriatric units (AGUs) require efficient discharge planning tools. Risk factors for discharge from an AGU to post-acute care (PAC) have not previously been investigated in detail. Methods The objective is to identify risk factors for PAC transfer. The DAMAGE (prospective multicenter cohort) consecutively included more than 3500 subjects aged 75 or older and admitted to an AGU. The patients underwent a comprehensive geriatric assessment (CGA) during their stay in the AGU. Only community-dwelling patients admitted to the AGU from the emergency department were included in the analysis. We recorded the characteristics of the care pathway and identified risk factors for discharge to home or to a PAC facility. Results 1928 patients were included. Loss of functional independence (a decrease in the Katz activities of daily living (ADL) score between 1 month prior to admission and AGU admission), living alone, social isolation, a high Katz ADL score at home, a low Katz ADL on admission, and delirium on admission were risk factors for transfer to PAC. Obesity, an elevated serum albumin level, and community-acquired infection were associated with discharge to home. Neither sex nor age was a risk factor for home discharge or transfer to PAC. Conclusion The present results might help clinicians and discharge planning teams to identify patients at risk of transfer to PAC more reliably and promptly in AGUs.

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“…Although the treatment of MM makes significant progress, MM remains a severe and incurable disease, and most patients relapse and require frequent hospitalizations (2, 3). Hospital readmission is a indicator of medical resource utilization and nursing quality evaluation, and also an important predictor of disease outcomes, such as increased morbidity, raised mortality, and the loss of functional independence (4,5). The annual cost of readmissions within 30 days after discharge accounts for more than $17 billion of avoidable Medicare expenses in the United States (6).…”

Section: Introductionmentioning

confidence: 99%

Association between metabolic obesity phenotypes and multiple myeloma hospitalization burden: A national retrospective study

et al. 2023

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Background & purposeObesity and metabolic disorders were associated with increased risk of MM, a disease characterized by high risk of relapsing and require frequent hospitalizations. In this study, we conducted a retrospective cohort study to explore the association of metabolic obesity phenotypes with the readmission risk of MM.Patients & methodsWe analyzed 34,852 patients diagnosed with MM from the Nationwide Readmissions Database (NRD), a nationally representative database from US. Hospitalization diagnosis of patients were obtained using ICD-10 diagnosis codes. According to obesity and metabolic status, the population was divided into four phenotypes: metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). The patients with different phenotypes were observed for hospital readmission at days 30-day, 60-day, 90-day and 180-day. Multivariate cox regression model was used to estimate the relationship between obesity metabolic phenotypes and readmissions risk.ResultsThere were 5,400 (15.5%), 7,255 (22.4%), 8,025 (27.0%) and 7,839 (35.6%) unplanned readmissions within 30-day, 60-day, 90-day and 180-day follow-up, respectively. For 90-day and 180-day follow-up, compared with patients with the MHNO phenotype, those with metabolic unhealthy phenotypes MUNO (90-day: P = 0.004; 180-day: P = < 0.001) and MUO (90-day: P = 0.049; 180-day: P = 0.004) showed higher risk of readmission, while patients with only obesity phenotypes MHO (90-day: P = 0.170; 180-day: P = 0.090) experienced no higher risk. However, similar associations were not observed for 30-day and 60-day. Further analysis in 90-day follow-up revealed that, readmission risk elevated with the increase of the combined factor numbers, with aHR of 1.068 (CI: 1.002-1.137, P = 0.043, with one metabolic risk factor), 1.109 (CI: 1.038-1.184, P = 0.002, with two metabolic risk factors) and 1.125 (95% CI: 1.04-1.216, P = 0.003, with three metabolic risk factors), respectively.ConclusionMetabolic disorders, rather than obesity, were independently associated with higher readmission risk in patients with MM, whereas the risk elevated with the increase of the number of combined metabolic factors. However, the effect of metabolic disorders on MM readmission seems to be time-dependent. For MM patient combined with metabolic disorders, more attention should be paid to advance directives to reduce readmission rate and hospitalization burden.

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Risk Factors for Hospital Readmission and Death After Discharge of Older Adults from Acute Geriatric Units: Taking the Rank of Admission into Account

Cited by 15 publications

References 32 publications

Prediction of 30-Day Readmission in Hospitalized Older Adults Using Comprehensive Geriatric Assessment and LACE Index and HOSPITAL Score

Prediction of 30-Day Readmission in Hospitalized Older Adults Using Comprehensive Geriatric Assessment and LACE Index and HOSPITAL Score

Factors Associated with Transfer from an Acute Geriatric Unit to a Post-Acute Care Facility among Community-Dwelling Patients: Results from the DAMAGE Cohort

Association between metabolic obesity phenotypes and multiple myeloma hospitalization burden: A national retrospective study

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