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Hospital Readmission From Post-Acute Care Facilities: Risk Factors, Timing, and Outcomes.

ABSTRACT: Hospital discharges to post-acute care (PAC) facilities have increased rapidly. This increase may lead to more hospital readmissions from PAC facilities, which are common and poorly understood. We sought to determine the risk factors and timing for hospital readmission from PAC facilities and evaluate the impact of readmission on patient outcomes.Retrospective analysis of Medicare Current Beneficiary Survey (MCBS) from 2003-2009.The MCBS is a nationally representative survey of beneficiaries matched with claims data.Community-dwelling beneficiaries who were hospitalized and discharged to a PAC facility for rehabilitation.Potential readmission risk factors included patient demographics, health utilization, active medical conditions at time of PAC admission, and PAC characteristics.Hospital readmission during the PAC stay, return to community residence, and all-cause mortality.Of 3246 acute hospitalizations followed by PAC facility stays, 739 (22.8%) included at least 1 hospital readmission. The strongest risk factors for readmission included impaired functional status (HR 4.78, 95% CI 3.21-7.10), markers of increased acuity such as need for intravenous medications in PAC (1.63, 1.39-1.92), and for-profit PAC ownership (1.43, 1.21-1.69). Readmitted patients had a higher mortality rate at both 30 days (18.9% vs 8.6%, P < .001) and 100 days (39.9% vs 14.5%, P < .001) even after adjusting for age, comorbidities, and prior health care utilization (30 days: OR 2.01, 95% CI 1.60-2.54; 100 days: OR 3.79, 95% CI 3.13-4.59).Hospital readmission from PAC facilities is common and associated with a high mortality rate. Readmission risk factors may signify inadequate transitional care processes or a mismatch between patient needs and PAC resources.

SUBMITTER: Burke RE

PROVIDER: S-EPMC4847128 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Hospital Readmission From Post-Acute Care Facilities: Risk Factors, Timing, and Outcomes.

Burke Robert E RE Whitfield Emily A EA Hittle David D Min Sung-joon SJ Levy Cari C Prochazka Allan V AV Coleman Eric A EA Schwartz Robert R Ginde Adit A AA

Journal of the American Medical Directors Association 20151220 3

<h4>Objectives</h4>Hospital discharges to post-acute care (PAC) facilities have increased rapidly. This increase may lead to more hospital readmissions from PAC facilities, which are common and poorly understood. We sought to determine the risk factors and timing for hospital readmission from PAC facilities and evaluate the impact of readmission on patient outcomes.<h4>Design</h4>Retrospective analysis of Medicare Current Beneficiary Survey (MCBS) from 2003-2009.<h4>Setting</h4>The MCBS is a nat ...[more]

PMID: 26715357

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Project description:BackgroundMethods used to categorize functional status to predict health outcomes across post-acute care settings vary significantly.ObjectivesWe compared three methods that categorize functional status to predict 30-day and 90-day hospital readmission across inpatient rehabilitation facilities (IRF), skilled nursing facilities (SNF) and home health agencies (HHA).Research designRetrospective analysis of 2013-2014 Medicare claims data (N = 740,530). Data were randomly split into two subsets using a 1:1 ratio. We used half of the cohort (development subset) to develop functional status categories for three methods, and then used the rest (testing subset) to compare outcome prediction. Three methods to generate functional categories were labeled as: Method I, percentile based on proportional distribution; Method II, percentile based on change score distribution; and Method III, functional staging categories based on Rasch person strata. We used six differentiation and classification statistics to determine the optimal method of generating functional categories.SettingIRF, SNF and HHA.SubjectsWe included 130,670 (17.7%) Medicare beneficiaries with stroke, 498,576 (67.3%) with lower extremity joint replacement and 111,284 (15.0%) with hip and femur fracture.MeasuresUnplanned 30-day and 90-day hospital readmission.ResultsFor all impairment conditions, Method III best predicted 30-day and 90-day hospital readmission. However, we observed overlapping confidence intervals among some comparisons of three methods. The bootstrapping of 30-day and 90-day hospital readmission predictive models showed the area under curve for Method III was statistically significantly higher than both Method I and Method II (all paired-comparisons, p<.001), using the testing sample.ConclusionsOverall, functional staging was the optimal method to generate functional status categories to predict 30-day and 90-day hospital readmission. To facilitate clinical and scientific use, we suggest the most appropriate method to categorize functional status should be based on the strengths and weaknesses of each method.

Project description:Hospital readmissions following severe infections are a major economic burden on the health care system and have a negative influence on patients' quality of life. Understanding the risk factors for readmission, particularly the extent to which they could be prevented, is of a great importance. In this study we evaluated potentially preventable risk factors for 60-day readmission in patients surviving hospitalization for complicated urinary tract infection (cUTI). This was a multinational, multicentre retrospective cohort study conducted in Europe and the Middle East. Our cohort included survivors of hospitalization due to cUTI during the years 2013-2014. The primary outcome was 60-day readmission following index hospitalization. Patient characteristics that could have influenced readmission: demographics, infection presentation and management, microbiological and clinical data; were collected via computerized medical records from infection onset up to 60 days after hospital discharge. Overall, 742 patients were included. The cohort median age was 68 years (interquartile range, (IQR) 55-80) and 43.3% (321/742) of patients were males. The all-cause 60-day readmission rate was 20.1% (149/742) and more than half were readmitted for infection [57.1%, (80/140)]. Recurrent cUTI was the most frequent cause for readmission [46.4% (65/140)]. Statistically significant risk factors associated with 60-day readmission in multivariable analysis were: older age (odds ratio (OR) 1.02 for an one-year increment, confidence interval (CI) 1.005-1.03), diabetes mellitus (OR 1.63, 95% CI 1.04-2.55), cancer (OR 1.7, 95% CI 1.05-2.77), previous urinary tract infection (UTI) in the last year (OR 1.8, 95% CI: 1.14-2.83), insertion of an indwelling bladder catheter (OR 1.62, 95% CI 1.07-2.45) and insertion of percutaneous nephrostomy (OR 3.68, 95% CI 1.67-8.13). In conclusion, patients surviving hospitalization for cUTI are frequently re-hospitalized, mostly for recurrent urinary infections associated with a medical condition that necessitated urinary interventions. Interventions to avoid re-admissions should target these patients.

Project description:BackgroundThere is increasing demand for post-acute care services, which is amplified by the COVID-19 pandemic.AimsWe studied the pattern of spatial association between post-acute care services and acute care facilities and evaluated how geographic variability could influence their use.MethodsWe compiled data on CMS-certified acute care and critical access hospitals and post-acute health care services (nursing homes, home health care services, inpatient rehabilitation facilities, long-term care hospitals, and hospice facilities). We used the colocation quotient (CLQ) to measure the magnitude and direction of association (clustering or segregation) between post-acute care providers and hospitals. This metric allows pairwise comparison of categorical data; a value <1 indicates spatial segregation and a value >1 spatial clustering. Unity marks the lack of spatial dependence (random distribution).ResultsWith the exception of nursing homes (CLQ 1.26), all other types of post-acute care providers are spatially segregated from rural critical access hospitals. Long-term care facilities ranked first (had the lowest global CLQ, 0.06), hospice facilities ranked last (had the highest global CLQ estimate, 0.54). Instead, post-acute care services either clustered with (inpatient rehabilitation 2.76, long-term care 2.10, nursing homes 1.37) or were only weakly segregated (home health care 0.86) from acute care hospitals. Home health care (1.44), hospice services (1.46), and nursing homes (1.08) spatially clustered with the same category of services. Results were robust in the sensitivity analysis and we provided illustrative examples of local variation for the states of MA and IA.ConclusionPost-acute care services are isolated from critical access hospitals, and have a clustering pattern with the same category services and acute care hospitals. Such misdistribution of resources may result in both underuse and a substitution effect on the type of post-acute care between rural and urban areas and undermine public health during increasing demand, such as the COVID-19 pandemic.

Project description:ImportanceStudies of acute myocardial infarction (AMI) occurring outside the hospital have informed approaches to addressing risk, treatment, and patient outcomes. Similar insights for in-hospital AMI are lacking.ObjectiveTo determine the incidence, risk factors, and outcomes associated with in-hospital AMI.Design, setting, and participantsCohort, nested case-control, and matched cohort study of patients hospitalized in US Veterans Health Administration facilities between July 2007 and September 2009. The incidence of in-hospital AMI was determined from a complete cohort of in-hospital AMI relative to the total number of inpatient admissions. From the in-hospital AMI cohort, detailed medical record review was performed on 687 cases and 687 individually matched controls. Risk factors and outcomes associated with in-hospital AMI were determined from matched comparison of in-hospital AMI cases to hospitalized controls.ExposuresCandidate risk factors for in-hospital AMI included characteristics at the time of admission and in-hospital variables prior to the index date.Main outcomes and measuresIn the determination of the incidence and risk factors associated with in-hospital AMI, the outcome of interest was in-hospital AMI. All-cause mortality was the main outcome of interest following in-hospital AMI.ResultsA total of 5556 patients with in-hospital AMI (mean [SD] age, 73 [10] years; 5456 [98.2%] male) were identified among 1.3 million admissions, with an incidence of 4.27 in-hospital AMI events per 1000 admissions. Independent risk factors associated with in-hospital AMI included intensive care unit setting, history of coronary artery disease, heart rate greater than 100 beats/min, hemoglobin level less than 8 g/dL, and white blood cell count 14 000/μL or greater. Compared with the matched control group, mortality was significantly higher for patients with in-hospital AMI (in-hospital mortality, 26.4% vs 4.2%; 30-day mortality, 33.0% vs 10.0%; 1-year mortality, 59.2% vs 34.4%).Conclusions and relevanceIn-hospital AMI was common and associated with common cardiovascular risk factors and markers of acute illness. Patient outcomes following in-hospital AMI were poor, with 1-year mortality approaching 60%. Further study of in-hospital AMI may yield opportunities to reduce in-hospital AMI risk and improve patient outcomes.

Project description:ObjectiveOlder hospitalized adults with an existing diagnosis of cancer rarely receive cancer treatment after discharge to a skilled nursing facility (SNF). It is unclear to what degree these outcomes may be driven by cumulative effects of previous cancer treatment and their complications vs an absolute functional threshold from which it is not possible to return. We sought to understand post-acute care outcomes of adults newly diagnosed with cancer and explore functional improvement during their SNF stay.DesignRetrospective cohort study, 2011-2013.Setting and participantsSurveillance, Epidemiology, and End Results - Medicare database of patients with new stage II-IV colorectal, pancreatic, bladder, or lung cancer discharged to SNF.MethodsPrimary outcome was time to death after hospital discharge. Covariates include cancer treatment receipt and hospice use. A Minimum Data Set (MDS)-Activities of Daily Living (ADL) score was calculated to measure changes in ADLs during SNF stay. Patient groups of interest were compared descriptively using means and standard deviations for continuous variables and frequencies and percentages for categorical variables. Logistic regression was used to compare patient groups.ResultsA total of 6791 cases were identified. Forty-six percent of patients did not receive treatment or hospice, 25.0% received no treatment but received hospice, 20.8% received treatment but no hospice, and 8.5% received both treatment and hospice. Only 43% of decedents received hospice. Patients who received treatment but not hospice had the best survival. There were limited improvements in MDS-ADL scores in the subset of patients for whom we have complete data. Those with greater functional improvement had improved survival.Conclusions and implicationsThe majority of patients did not receive future cancer treatment or hospice care prior to death. There was limited improvement in MDS-ADL scores raising concern this population might not benefit from the rehabilitative intent of SNFs.

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Hospital Readmission From Post-Acute Care Facilities: Risk Factors, Timing, and Outcomes.

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Hospital Readmission From Post-Acute Care Facilities: Risk Factors, Timing, and Outcomes.

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