Dataset Information

Association between Inpatient Delirium and Hospital Readmission in Patients ? 65 Years of Age: A Retrospective Cohort Study.

ABSTRACT: BACKGROUND:Delirium affects more than seven million hospitalized adults in the United States annually. However, its impact on postdischarge healthcare utilization remains unclear. OBJECTIVE:To determine the association between delirium and 30-day hospital readmission. DESIGN:A retrospective cohort study. SETTING:A general community medical and surgical hospital. PATIENTS:All adults who were at least 65 years old, without a history of delirium or alcohol-related delirium, and were hospitalized from September 2010 to March 2015. MEASUREMENTS:The patients deemed at risk for or displaying symptoms of delirium were screened by nurses using the Confusion Assessment Method with a followup by a staff psychiatrist for a subset of screen-positive patients. Patients with delirium confirmed by a staff psychiatrist were compared with those without delirium. The primary outcome was the 30-day readmission rate. The secondary outcomes included emergency department (ED) visits 30 days postdischarge, mortality during hospitalization and 30 days postdischarge, and discharge location. RESULTS:The cohort included 718 delirious patients and 7,927 nondelirious patients. Using an unweighted multivariable logistic regression, delirium was determined to be significantly associated with the increased odds of readmission within 30 days of discharge (odds ratio (OR): 2.60; 95% CI, 1.96-3.44; P < .0001). Delirium was also significantly (P < .0001) associated with ED visits within 30 days postdischarge (OR: 2.18; 95% CI: 1.77-2.69) and discharge to a facility (OR: 2.52; 95% CI: 2.09-3.01). CONCLUSIONS:Delirium is a significant predictor of hospital readmission, ED visits, and discharge to a location other than home. Delirious patients should be targeted to reduce postdischarge healthcare utilization.

SUBMITTER: LaHue SC

PROVIDER: S-EPMC6628723 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Association between Inpatient Delirium and Hospital Readmission in Patients ≥ 65 Years of Age: A Retrospective Cohort Study.

LaHue Sara C SC Douglas Vanja C VC Kuo Teresa T Conell Carol A CA Liu Vincent X VX Josephson S Andrew SA Angel Clay C Brooks Kristen B KB

Journal of hospital medicine 20190401 4

<h4>Background</h4>Delirium affects more than seven million hospitalized adults in the United States annually. However, its impact on postdischarge healthcare utilization remains unclear.<h4>Objective</h4>To determine the association between delirium and 30-day hospital readmission.<h4>Design</h4>A retrospective cohort study.<h4>Setting</h4>A general community medical and surgical hospital.<h4>Patients</h4>All adults who were at least 65 years old, without a history of delirium or alcohol-relate ...[more]

PMID: 30933669

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Project description:Introduction. Hospital readmission within 30 days of discharge is a target for health care cost savings through the medicare Value Based Purchasing initiative. Because of this focus, hospitals and health systems are investing considerable resources into the identification of patients at risk of hospital readmission and designing interventions to reduce the rate of hospital readmission. Malnutrition is a known risk factor for hospital readmission. Materials and Methods. All medical patients 65 years of age or older discharged from Memorial Medical Center from January 1, 2012 to March 31, 2012 who had a determination of serum albumin level and total lymphocyte count on hospital admission were studied retrospectively. Admission serum albumin levels and total lymphocyte counts were used to classify the nutritional status of all patients in the study. Patients with a serum albumin less than 3.5 grams/dL and/or a TLC less than 1,500 cells per mm3 were classified as having protein energy malnutrition. The primary outcome investigated in this study was hospital readmission for any reason within 30 days of discharge. Results. The study population included 1,683 hospital discharges with an average age of 79 years. The majority of the patients were female (55.9%) and had a DRG weight of 1.22 (0.68). 219 patients (13%) were readmitted within 30 days of hospital discharge. Protein energy malnutrition was common in this population. Low albumin was found in 973 (58%) patients and a low TLC was found in 1,152 (68%) patients. Low albumin and low TLC was found in 709 (42%) of patients. Kaplan-Meier analysis shows any laboratory evidence of PEM is a significant (p < 0.001) predictor of hospital readmission. Low serum albumin (p < 0.001) and TLC (p = 0.018) show similar trends. Cox proportional-hazards regression analysis showed low serum albumin (Hazard Ratio 3.27, 95% CI [2.30-4.63]) and higher DRG weight (Hazard Ratio 1.19, 95% CI [1.03-1.38]) to be significant independent predictors of hospital readmission within 30 days. Discussion. This study investigated the relationship of PEM to the rate of hospital readmission within 30 days of discharge in patients 65 years of age or older. These results indicate that laboratory markers of PEM can identify patients at risk of hospital readmission within 30 days of discharge. This risk determination is simple and identifies a potentially modifiable risk factor for readmission: protein energy malnutrition.

Project description:ObjectivesTo determine whether high performing hospitals with low 30 day risk standardized readmission rates have a lower proportion of readmissions from specific diagnoses and time periods after admission or instead have a similar distribution of readmission diagnoses and timing to lower performing institutions.DesignRetrospective cohort study.SettingMedicare beneficiaries in the United States.ParticipantsPatients aged 65 and older who were readmitted within 30 days after hospital admission for heart failure, acute myocardial infarction, or pneumonia in 2007-09.Main outcome measuresReadmission diagnoses were classified with a modified version of the Centers for Medicare and Medicaid Services' condition categories, and readmission timing was classified by day (0-30) after hospital discharge. Hospital 30 day risk standardized readmission rates over the three years of study were calculated with public reporting methods of the US federal government, and hospitals were categorized with bootstrap analysis as having high, average, or low readmission performance for each index condition. High and low performing hospitals had ≥ 95% probability of having an interval estimate respectively less than or greater than the national 30 day readmission rate over the three year period of study. All remaining hospitals were considered average performers.ResultsFor readmissions in the 30 days after the index admission, there were 320,003 after 1,291,211 admissions for heart failure (4041 hospitals), 102,536 after 517,827 admissions for acute myocardial infarction (2378 hospitals), and 208,438 after 1,135,932 admissions for pneumonia (4283 hospitals). The distribution of readmissions by diagnosis was similar across categories of hospital performance for all three conditions. High performing hospitals had fewer readmissions for all common diagnoses. Median time to readmission was similar by hospital performance for heart failure and acute myocardial infarction, though was 1.4 days longer among high versus low performing hospitals for pneumonia (P<0.001). Findings were unchanged after adjustment for other hospital characteristics potentially associated with readmission patterns.ConclusionsHigh performing hospitals have proportionately fewer 30 day readmissions without differences in readmission diagnoses and timing, suggesting the possible benefit of strategies that lower risk of readmission globally rather than for specific diagnoses or time periods after hospital stay.

Project description:ObjectiveTo examine the association of hospital volume (a marker of quality of care) with hospital readmission rates.DesignRetrospective cross-sectional study.Setting4651 US acute care hospitals.Study data6,916,644 adult discharges, excluding patients receiving psychiatric or medical cancer treatment.Main outcome measuresWe used Medicare fee-for-service data from 1 July 2011 to 30 June 2012 to calculate observed-to-expected, unplanned, 30 day, standardized readmission rates for hospitals and for specialty cohorts medicine, surgery/gynecology, cardiorespiratory, cardiovascular, and neurology. We assessed the association of hospital volume by quintiles with 30 day, standardized readmission rates, with and without adjustment for hospital characteristics (safety net status, teaching status, geographic region, urban/rural status, nurse to bed ratio, ownership, and cardiac procedure capability. We also examined associations with the composite outcome of 30 day, standardized readmission or mortality rates.ResultsMean 30 day, standardized readmission rate among the fifth of hospitals with the lowest volume was 14.7 (standard deviation 5.3) compared with 15.9 (1.7) among the fifth of hospitals with the highest volume (P<0.001). We observed the same pattern of lower readmission rates in the lowest versus highest volume hospitals in the specialty cohorts for medicine (16.6 v 17.4, P<0.001), cardiorespiratory (18.5 v 20.5, P<0.001), and neurology (13.2 v 14.0, p=0.01) cohorts; the cardiovascular cohort, however, had an inverse association (14.6 v 13.7, P<0.001). These associations remained after adjustment for hospital characteristics except in the cardiovascular cohort, which became non-significant, and the surgery/gynecology cohort, in which the lowest volume fifth of hospitals had significantly higher standardized readmission rates than the highest volume fifth (difference 0.63 percentage points (95% confidence interval 0.10 to 1.17), P=0.02). Mean 30 day, standardized mortality or readmission rate was not significantly different between highest and lowest volume fifths (20.4 v 20.2, P=0.19) and was highest in the middle fifth of hospitals (range 20.6-20.8).ConclusionsStandardized readmission rates are lowest in the lowest volume hospitals-opposite from the typical association of greater hospital volume with better outcomes. This association was independent of hospital characteristics and was only partially attenuated by examining mortality and readmission together. Our findings suggest that readmissions are associated with different aspects of care than mortality or complications.

Project description:OBJECTIVE:To assess how well the LACE index and its constituent elements predict 30-day hospital readmission, and to determine whether other combinations of clinical or sociodemographic variables may enhance prognostic capability. DESIGN:Retrospective cohort study with split sample design for model validation. SETTING:One large hospital Trust in the West Midlands. PARTICIPANTS:All alive-discharge adult inpatient episodes between 1 January 2013 and 31 December 2014. DATA SOURCES:Anonymised data for each inpatient episode were obtained from the hospital information system. These included age at index admission, gender, ethnicity, admission/discharge date, length of stay, treatment specialty, admission type and source, discharge destination, comorbidities, number of accident and emergency (A&E) visits in the 6?months before the index admission and whether a patient was readmitted within 30 days of index discharge. OUTCOME MEASURES:Clinical and patient characteristics of readmission versus non-readmission episodes, proportion of readmission episodes at each LACE score, regression modelling of variables associated with readmission to assess the effectiveness of LACE and other variable combinations to predict 30-day readmission. RESULTS:The training cohort included data on 91?922 patient episodes. Increasing LACE score and each of its individual components were independent predictors of readmission (area under the receiver operating characteristic curve (AUC) 0.773; 95%?CI 0.768 to 0.779 for LACE; AUC 0.806; 95%?CI 0.801 to 0.812 for the four LACE components). A LACE score of 11 was most effective at distinguishing between higher and lower risk patients. However, only 25% of readmission episodes occurred in the higher scoring group. A model combining A&E visits and hospital episodes per patient in the previous year was more effective at predicting readmission (AUC 0.815; 95%?CI 0.810 to 0.819). CONCLUSIONS:Although LACE shows good discriminatory power in statistical terms, it may have little added value over and above clinical judgement in predicting a patient's risk of hospital readmission.

Project description:ObjectivesTo determine the strength and nature of the association between delirium and incident dementia in a population of older adult patients without dementia at baseline.DesignRetrospective cohort study using large scale hospital administrative data.SettingPublic and private hospitals in New South Wales, Australia between July 2001 and March 2020.ParticipantsData were extracted for 650 590 hospital patients aged ≥65 years. Diagnoses of dementia and delirium were identified from ICD-10 (international classification of diseases, 10th revision) codes. Patients with dementia at baseline were excluded. Delirium-no delirium pairs were identified by matching personal and clinical characteristics, and were followed for more than five years.Main outcome measuresCox proportional hazards models and Fine-Gray hazard models were used to estimate the associations of delirium with death and incident dementia, respectively. Delirium-outcome dose-response associations were quantified, all analyses were performed in men and women separately, and sensitivity analyses were conducted.ResultsThe study included 55 211 matched pairs (48% men, mean age 83.4 years, standard deviation 6.5 years). Collectively, 58% (n=63 929) of patients died and 17% (n=19 117) had a newly reported dementia diagnosis during 5.25 years of follow-up. Patients with delirium had 39% higher risk of death (hazard ratio 1.39, 95% confidence interval 1.37 to 1.41) and three times higher risk of incident dementia (subdistribution hazard ratio 3.00, 95% confidence interval 2.91 to 3.10) than patients without delirium. The association with dementia was stronger in men (P=0.004). Each additional episode of delirium was associated with a 20% increased risk of dementia (subdistribution hazard ratio 1.20, 95% confidence interval 1.18 to 1.23).ConclusionsThe study findings suggest delirium was a strong risk factor for death and incident dementia among older adult patients. The data support a causal interpretation of the association between delirium and dementia. The clinical implications of delirium as a potentially modifiable risk factor for dementia are substantial.

Project description:BackgroundHigh adiposity is associated with higher risks for a variety of adverse health outcomes, including higher rates of age-adjusted mortality and increased morbidity. This has important implications for the management of healthcare systems, since the endocrinal, cardiometabolic and other changes associated with increased adiposity may be associated with substantial healthcare costs.MethodsWe studied the association between various measures of adiposity and inpatient hospital costs through record linkage between UK Biobank and records of inpatient care in England and Wales. UK Biobank is a large prospective cohort study that aimed to recruit men and women aged between 40 and 69 from 2006 to 2010. We applied generalised linear models to cost per person year to estimate the marginal effect of adiposity, and average adjusted predicted costs of adiposity.ResultsValid cost and body mass index (BMI) data from 457,689 participants were available for inferential analysis. Some 54.4% of individuals included in the analysis sample had positive inpatient healthcare costs over the period of follow-up. Median hospital costs per person-year of follow-up were £89, compared to mean costs of £481. Mean BMI overall was 27.4 kg/m2 (standard deviation 4.8). The marginal effect of a unit increase in BMI was £13.61 (99% confidence interval £12.60-£14.63) per person-year of follow up. The marginal effect of a standard deviation increase in BMI was £69.20 (99% confidence interval £64.98-£73.42). The marginal effect of becoming obese was £136.35 (99% confidence interval £124.62-£148.08). Average adjusted predicted inpatient hospital costs increased almost linearly when modelled using continuous measure of adiposity. Sensitivity analysis of different scenarios did not substantially change these conclusions, although there was some evidence of attenuation of the effects of adiposity when controlling for waist-hip ratios, and when individuals who self-reported any pre-existing conditions were excluded from analysis.ConclusionsHigher adiposity is associated with higher inpatient hospital costs. Further scrutiny using causal inferential methods is warranted to establish if further public health investments are required to manage the large healthcare costs observationally associated with overweight and obesity.

Dataset Information

Association between Inpatient Delirium and Hospital Readmission in Patients ? 65 Years of Age: A Retrospective Cohort Study.

Publications

Association between Inpatient Delirium and Hospital Readmission in Patients ≥ 65 Years of Age: A Retrospective Cohort Study.

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