The price of bouncing back: one-year mortality and payments for acute stroke patients with 30-day bounce-backs

Rapid systematic review of readmissions costs after stroke

BACKGROUND

Stroke readmissions are considered a marker of health quality and may pose a burden to healthcare systems. However, information on the costs of post-stroke readmissions has not been systematically reviewed.

OBJECTIVES

To systematically review information about the costs of hospital readmissions of patients whose primary diagnosis in the index admission was a stroke.

METHODS

A rapid systematic review was performed on studies reporting post-stroke readmission costs in EMBASE, MEDLINE, and Web of Science up to June 2021. Relevant data were extracted and presented by readmission and stroke type. The original study's currency values were converted to 2021 US dollars based on the purchasing power parity for gross domestic product. The reporting quality of each of the included studies was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist.

RESULTS

Forty-four studies were identified. Considerable variability in readmission costs was observed among countries, readmissions, stroke types, and durations of the follow-up period. The UK and the USA were the countries reporting the highest readmission costs. In the first year of follow-up, stroke readmission costs accounted for 2.1-23.4%, of direct costs and 3.3-21% of total costs. Among the included studies, only one identified predictors of readmission costs.

CONCLUSION

Our review showed great variability in readmission costs, mainly due to differences in study design, countries and health services, follow-up duration, and reported readmission data. The results of this study can be used to inform policymakers and healthcare providers about the burden of stroke readmissions. Future studies should not solely focus on improving data standardization but should also prioritize the identification of stroke readmission cost predictors.

An interpretable machine learning approach for predicting 30-day readmission after stroke

BACKGROUND

Stroke is the second leading cause of death worldwide and has a significantly high recurrence rate. We aimed to identify risk factors for stroke recurrence and develop an interpretable machine learning model to predict 30-day readmissions after stroke.

METHODS

Stroke patients deposited in electronic health records (EHRs) in Xuzhou Medical University Hospital between February 1, 2021, and November 30, 2021, were included in the study, and deceased patients were excluded. We extracted 74 features from EHRs, and the top 20 features (chi-2 value) were used to build machine learning models. 80% of the patients were used for pre-training. Subsequently, a 20% holdout dataset was used for verification. The Shapley Additive exPlanations (SHAP) method was used to explore the interpretability of the model.

RESULTS

The cohort included 6,558 patients, of whom the mean (SD) age was 65 (11) years, 3,926 were males (59.86 %), and 132 (2.01 %) were readmitted within 30 days. The area under the receiver operating characteristic curve (AUROC) for the optimized model was 0.80 (95 % CI 0.68-0.80). We used the SHAP method to identify the top 10 risk factors (i.e., severe carotid artery stenosis, weak, homocysteine, glycosylated hemoglobin, sex, lymphocyte percentage, neutrophilic granulocyte percentage, urine glucose, fresh cerebral infarction, and red blood cell count). The AUROC of a model with the 10 features was 0.80 (95 % CI 0.69-0.80) and was not significantly different from that of the model with 20 risk factors.

CONCLUSIONS

Our methods not only showed good performance in predicting 30-day readmissions after stroke but also revealed risk factors that provided valuable insights for treatments.

The Association of Socioeconomic Status and Discharge Destination with 30-Day Readmission after Ischemic Stroke

OBJECTIVES

This study aimed to explore the association of socioeconomic status and discharge destination with 30-day readmission after ischemic stroke.

MATERIALS AND METHODS

We examined 30-day all-cause readmission among patients hospitalized for ischemic stroke in states of Arkansas, Iowa, and Wisconsin in 2016 and 2017 and New York in 2016 using Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases.

RESULTS

Among the 52301 patients included, 51.1% were female. The 30-day readmission rates were 10.2%, 8.2%, 9.3%, 10.4%, 11.6%, and 11.2% for age group 18-34, 35-44, 45-54, 55-64, 65-74, and ≥75 years, respectively (p<0.001). In Generalized Estimating Equation analysis, patients with Medicare and Medicaid insurance were more likely to be readmitted, compared with private insurance, (adjusted Odds Ratio [aOR] 1.37, 95% CI 1.23-1.53; and aOR 1.26, 95% CI 1.09-1.45, respectively). Patients in the bottom quartile of zip code level median household income had higher 30-day readmission rate (12.4%) than those in the 2nd, 3rd and 4th quartile (10.3%, 10.1%, and 10.7%, respectively, p<0.001). Compared with those discharged home with self-care which had the lowest readmission rate (8.4%), patients who left against medical advice had the highest readmission rate (18.6%; aOR 2.23, 95% CI 1.75-2.83), followed by rehabilitation and skilled nursing facilities (13.2%; aOR 1.33, 95% CI 1.22-1.46), and home with home health care (11.3%, aOR 1.18, 95% CI 1.08-1.28).

CONCLUSIONS

Socioeconomic status and discharged destination affect readmission after stroke. These results provide evidence to inform vulnerable patient population as targets for readmission prevention.

Admission and Readmission/Death Patterns in Hospitalized and Non-hospitalized First-Ever-in-a-Lifetime Stroke Patients During the First Year: A Population-Based Incidence Study

Background: Hospitalization and readmission rates after a first-ever-in-a-lifetime stroke (FELS) are considered measures of quality of care and, importantly, may give valuable information to better allocate health-related resources. We aimed to investigate the hospitalization pattern and the unplanned readmissions or death of hospitalized (HospS) and non-hospitalized stroke (NHospS) patients 1 year after a FELS, based on a community register.

Methods: Data about hospitalization and unplanned readmissions and case fatality 1 year after a FELS were retrieved from the population-based register undertaken in Northern Portugal (ACIN2), comprising all FELS in 2009–2011. We used the Kaplan–Meier method to estimate 1-year readmission/death-free survival and Cox proportional hazard models to identify independent factors for readmission/death.

Results: Of the 720 FELS, 35.7% were not hospitalized. Unplanned readmission/death within 1 year occurred in 33.0 and 24.9% of HospS and NHospS patients, respectively. The leading causes of readmission were infections, recurrent stroke, and cardiovascular events. Stroke-related readmissions were observed in more than half of the patients in both groups. Male sex, age, pre- and post-stroke functional status, and diabetes were independent factors of readmission/death within 1 year.

Conclusion: About one-third of stroke patients were not hospitalized, and the readmission/death rate was higher in HospS patients. Still, that readmission/death rate difference was likely due to other factors than hospitalization itself. Our research provides novel information that may help implement targeted health-related policies to reduce the burden of stroke and its complications.

Discharge Communication of Dementia-Related Neuropsychiatric Symptoms and Care Management Strategies During Hospital to Skilled Nursing Facility Transitions

Provided the complexity of managing dementia-related neuropsychiatric symptoms (NPS), accurate communication about these symptoms at hospital discharge is critical to facilitating safe and effective transitions, particularly transitions from hospitals to skilled nursing facilities (SNF), which are often poorly managed. Skilled nursing facilities providers have cited undercommunication regarding NPS as a major challenge that contributes to poor outcomes including rehospitalization. This multisite retrospective cohort study identified omission rates for NPS and associated management strategies in discharge communication as compared to medical record documentation in the 72 hours preceding discharge among hospitalized patients with dementia. High rates of omission were found across NPS and management strategies: anxiety (94%), agitation/aggression (77%), hallucinations (85%), 1:1 supervision (90%), high fall risk (89%), use of restraints (91%). Omission rate for new or modified antipsychotic medication was 12.9%. Findings underscore the need for additional research on cross-setting communication regarding care needs of patients with dementia-who often cannot communicate these needs on their own-in facilitating high-quality transitions.

Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing

Background and Purpose: This study aims to determine whether machine learning (ML) and natural language processing (NLP) from electronic health records (EHR) improve the prediction of 30-day readmission after stroke.

Methods: Among index stroke admissions between 2011 and 2016 at an academic medical center, we abstracted discrete data from the EHR on demographics, risk factors, medications, hospital complications, and discharge destination and unstructured textual data from clinician notes. Readmission was defined as any unplanned hospital admission within 30 days of discharge. We developed models to predict two separate outcomes, as follows: (1) 30-day all-cause readmission and (2) 30-day stroke readmission. We compared the performance of logistic regression with advanced ML algorithms. We used several NLP methods to generate additional features from unstructured textual reports. We evaluated the performance of prediction models using a five-fold validation and tested the best model in a held-out test dataset. Areas under the curve (AUCs) were used to compare discrimination of each model.

Results: In a held-out test dataset, advanced ML methods along with NLP features out performed logistic regression for all-cause readmission (AUC, 0.64 vs. 0.58; p < 0.001) and stroke readmission prediction (AUC, 0.62 vs. 0.52; p < 0.001).

Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke. However, further improvement is necessary before being implemented in clinical practice given the weak discrimination.

Who is in the revolving door? Policy and practice implications of recurrent reports to adult protective services

Repeat referral to adult protective services APS (recurrence) is a much-discussed topic among APS agencies as it may indicate ongoing harm, yet there is limited research examining prevalence or causes. This paper provides a foundational investigation of recurrence within a California APS county program. Drawing from thirty-three months of de-identified reports, we used logistic regression to examine the impact of intake report characteristics on repeat referral within one year after baseline case closure. One-fifth of the sample was recurrent (19.9%, n=987/4,958), with self-neglect being the most common type of report to recur (14.3%, n=307/2,141). Overall recurrence was predicted by female gender, older age, living alone, and multiple elder abuse, neglect, and exploitation (ANE) types reported at baseline, and report placed by social service provider, friends, family, landlords, and victim self-reports. Reporters personally related to the victim and social service providers are potential partners in identifying ANE, and alternate intervention approaches may be necessary.

A Machine Learning Approach to Predicting Readmission or Mortality in Patients Hospitalized for Stroke or Transient Ischemic Attack

Readmissions after stroke are not only associated with greater levels of disability and a higher risk of mortality but also increase overall medical costs. Predicting readmission risk and understanding its causes are thus essential for healthcare resource allocation and quality improvement planning. By using machine learning techniques on initial admission data, this study aimed to develop prediction models for readmission or mortality after stroke. During model development, resampling methods were implemented to balance the class distribution. Two-layer nested cross-validation was used to build and evaluate the prediction models. A total of 3422 patients were included for analysis. The 90-day rate of readmission or mortality was 17.6%. This study identified several important predictive factors, including age, prior emergency department visits, pre-stroke functional status, stroke severity, body mass index, consciousness level, and use of a nasogastric tube. The Naïve Bayes model with class weighting to compensate for class imbalance achieved the highest discriminatory capacity in terms of the area under the receiver operating characteristic curve (0.661). Despite having room for improvement, the prediction models could be used for early risk assessment of patients with stroke. Identification of patients at high risk for readmission or mortality immediately after admission has the potential of enabling early discharge planning and transitional care interventions.

Medicare Shared Savings Program and readmission rate among patients with ischemic stroke

OBJECTIVE

Hospitals participating in the Medicare Shared Savings Program (MSSP) share with the Centers for Medicare and Medicaid Services (CMS) the savings generated by reduced cost of care. Our aim was to determine whether MSSP is associated with changes in readmissions and mortality for Medicare patients hospitalized with ischemic stroke, and whether MSSP has a different impact on safety net hospitals (SNHs) compared to non-SNHs.

METHODS

This study was based on the CMS Hospital Compare data for risk-standardized 30-day readmission and mortality rates for Medicare patients hospitalized with ischemic strokes between 2010 and 2017. With a propensity score-matched sample, hospital-level difference-in-difference analysis was used to determine whether MSSP was associated with changes in hospital readmission and mortality as well as to examine the impact of MSSP on SNHs compared to non-SNHs.

RESULTS

MSSP-participating hospitals had slightly greater reductions in readmission rates compared to matched nonparticipating hospitals (difference, 0.25 percentage points; 95% confidence interval [CI], -0.42 to -0.08). Mortality rates decreased among all hospitals, but mortality reduction was not significantly different between MSSP-participating hospitals and matched hospitals (difference, 0.06 percentage points; 95% CI, -0.28 to 0.17). Prior to MSSP, readmission rates in SNHs were higher compared to non-SNHs, but MSSP did not have significantly different impact on hospital readmission and mortality rates for SNHs and non-SNHs.

CONCLUSION

MSSP led to slightly fewer readmissions without increases in mortality for Medicare patients hospitalized with ischemic stroke. Similar reductions in readmission rates were observed in SNHs and non-SNHs participating in MSSP, indicating persistent gaps between SNHs and non-SNHs.

Relationship between early follow-up and readmission within 30 and 90 days after ischemic stroke

OBJECTIVE

To examine whether early follow-up with primary care or neurology is associated with lower all-cause readmissions within 30 and 90 days after acute ischemic stroke admission.

METHODS

We performed a retrospective cohort study of patients who were discharged home after acute ischemic stroke, identified by ICD-9 and ICD-10 codes, using PharMetrics, a nationally representative claims database of insured Americans from 2009 to 2015. The primary predictor was outpatient primary care or neurology follow-up within 30 and 90 days of discharge, and the primary outcome was all-cause 30- and 90-day readmissions. Multivariable Cox models were used with primary care and neurology visits specified as time-dependent covariates, with adjustment for patient demographics, comorbid conditions, and stroke severity measures.

RESULTS

The cohort included 14,630 patients. Readmissions within 30 days occurred in 7.3% of patients, and readmissions within 90 days occurred in 13.7% of patients. By 30 days, 59.3% had a primary care visit, and 24.4% had a neurology visit. Primary care follow-up was associated with reduced 30-day readmissions (hazard ratio [HR] 0.84, 95% confidence interval [CI] 0.72-0.98). Primary care follow-up before 90 days did not reach significance (HR 0.92, 95% CI 0.83-1.03). Neurology follow-up was not associated with reduced readmissions within 30 or 90 days (HR 1.05, 95% CI; HR 1.00, 95% CI, respectively).

CONCLUSION

Early outpatient follow-up with primary care is associated with a reduction in 30-day hospital readmissions. Early outpatient follow-up may represent an important opportunity for intervention after acute stroke admissions.

The impact of pre-operative symptoms on carotid endarterectomy Outcomes: Analysis of the ACS-NSQIP carotid endarterectomy database

Carotid artery stenosis accounts for up to 20% of ischemic strokes. Since the 1950 s, one of the primary surgical treatment for this condition is carotid endarterectomy (CEA). Because of improvement of medical therapy for carotid artery atherosclerosis and the increased use of carotid artery stents, CEA is indicated if the risk of stroke and death are low. The goal of this study is to characterize the impact of pre-operative stroke and stroke risk factors on post-operative CEA patient outcomes, using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) Targeted Vascular Module on CEA. Using the Targeted Vascular Module of the ACS-NSQIP, 22,116 patients who underwent CEA were identified from 2011 to 2016. Univariate analysis and multivariable logistic regression analyses were conducted to identify significant risk factors that predispose patients to stroke. Patients with pre-operative stroke comprise 42.1% of the group, with post-operative stroke being the second most common complication (2.1%). Pre-operative stroke patients were also at a higher risk for transient ischemic attacks, post-operative restenosis, post-operative distal embolization, and other complications. Patients with pre-operative risk factors, including stroke or stroke-like symptoms, high risk physiologic factors, high risk anatomic factors, and contralateral internal carotid artery stenosis were at a higher risk of developing post-operative stroke and other complications. Patients with these pre-operative risk factors should be closely monitored for post-operative complications in an effort to improve patient outcomes.

The Impact of Ischaemic Stroke Subtype on 30-day Hospital Readmissions

Background

Stroke aetiology may affect the risk and causes of readmission after ischaemic stroke (IS) and transient ischaemic attack (TIA) due to differences in risk factors, functional outcome, and treatment. We aimed to examine frequencies, causes, and risk of 30-day readmission by stroke subtype, determine predictors of 30-day readmission, and study the impact of 30-day readmissions on one-year mortality.

Methods

All surviving patients admitted with IS or TIA from July 2007 to December 2013 were followed by review of medical records for all unplanned readmissions within 30 days after discharge. Stroke subtype was classified as large-artery atherosclerosis (LAA), cardioembolism (CE), small vessel occlusion (SVO), stroke of other determined aetiology (SOE), or stroke of undetermined aetiology (SUE). Cox regression analyses were performed to assess the risk of 30-day readmission for the stroke subtypes and identify predictors of 30-day readmission, and its impact on one-year mortality.

Results

Of 1874 patients, 200 (10.7%) were readmitted within 30 days [LAA 42/244 (17.2%), CE 75/605 (12.4%), SVO 12/205 (5.9%), SOE 6/32 (18.8%), SUE 65/788 (8.3%)]. The most frequent causes of readmissions were stroke-related event, infection, recurrent stroke/ TIA, and cardiac disease. After adjusting for age, sex, functional outcome, length of stay, and the risk factor burden, patients with LAA and SOE subtype had significantly higher risks of readmission for any cause, recurrent stroke or TIA, and stroke-related events. Predictors of 30-day readmission were higher age, peripheral arterial disease, enteral feeding, and LAA subtype. Thirty-day readmission was an independent predictor of one-year mortality.

Conclusions

Patients with LAA or SOE have a high risk of 30-day readmission, possibly caused by an increased risk of recurrent stroke and stroke-related events. Awareness of the risk of readmission for different causes and appropriate handling according to stroke subtype may be useful for preventing some readmissions after stroke.

Risk factors associated with 31-day unplanned readmission in 50,912 discharged patients after stroke in China

BACKGROUND

Unplanned readmission within 31 days of discharge after stroke is a useful indicator for monitoring quality of hospital care. We evaluated the risk factors associated with 31-day unplanned readmission of stroke patients in China.

METHODS

We identified 50,912 patients from 375 hospitals in 29 provinces, municipalities or autonomous districts across China who experienced an unplanned readmission after stroke between 2015 and 2016, and extracted data from the inpatients' cover sheet data from the Medical Record Monitoring Database. Patients were grouped into readmission within 31 days or beyond for analysis. Chi-squared test was used to analyze demographic information, health system and clinical process-related factors according to the data type. Multilevel logistic modeling was used to examine the effects of patient (level 1) and hospital (level 2) characteristics on an unplanned readmission ≤31 days.

RESULTS

Among 50,912 patients, 14,664 (28.8%) were readmitted within 31 days after discharge. The commonest cause of readmissions were recurrent stroke (34.8%), hypertension (22.94%), cardio/cerebrovascular disease (13.26%) and diabetes/diabetic complications (7.34%). Higher risks of unplanned readmissions were associated with diabetes (OR = 1.089, P = 0.001), use of clinical pathways (OR = 1.174, P < 0.001), and being discharged without doctor's advice (OR = 1.485, P < 0.001). Lower risks were associated with basic medical insurances (OR ranging from 0.225 to 0.716, P < 0.001) and commercial medical insurance (OR = 0.636, P = 0.021), compared to self-paying for medical services. And patients aged 50 years old and above (OR ranging from 0.650 to 0.985, P < 0.05), with haemorrhagic stroke (OR = 0.467, P < 0.001), with length of stay more than 7 days in hospital (OR ranging from 0.082 to 0.566, P < 0.001), also had lower risks.

CONCLUSIONS

Age, type of stroke, medical insurance status, type of discharge, use of clinical pathways, length of hospital stay and comorbidities were the most influential factors for readmission within 31 days.

Patient and hospital factors associated with 30-day unplanned readmission in patients with stroke

Stroke is frequently associated with readmission; moreover, readmission is regarded as an important indicator of the quality of stroke care. Thus, we investigated factors associated with 30-day readmission in patients with stroke in South Korea. We used claims data from 2013 for stroke (I60–I62) patients (n=44 729) in 94 hospitals and classified unplanned readmission according to the Centers for Medicare and Medicaid guidelines. We used multilevel models to investigate patient (age, gender, type of insurance, admission via emergency room, length of stay, type of stroke, Elixhauser Index Score) and hospital (stroke care quality grade, location of hospital, type of hospital, number of doctors and nurses per 100 beds) factors associated with readmission within 30 days of discharge. Among the 44 729 patients admitted due to stroke, 9.2% (n=4124) were readmitted to hospital and 7.6% (n=3379) had unplanned readmissions. Regarding patient characteristics, medical aid and longer hospital stay were associated with 30-day readmission rate. Among hospital factors, patients admitted to a low-grade hospital or a non-capital area hospital were more likely to be readmitted within 30 days of discharge. We identified patient and hospital factors associated with 30-day readmission among stroke patients. In particular, patients admitted to hospitals with higher quality stroke care showed lower readmission rates.

Association Between Early Outpatient Visits and Readmissions After Ischemic Stroke

BACKGROUND

Reducing hospital readmission is an important goal to optimize poststroke care and reduce costs. Early outpatient follow-up may represent one important strategy to reduce readmissions. We examined the association between time to first outpatient contact and readmission to inform postdischarge transitions.

METHODS AND RESULTS

We performed a retrospective cohort study of all Medicare fee-for-service patients discharged home after an acute ischemic stroke in 2012 identified by the InternationalClassification of Diseases, Ninth Revision, Clinical Modification codes. Our primary predictor variable was whether patients had a primary care or neurology visit within 30 days of discharge. Our primary outcome variable was all-cause 30-day hospital readmission. We used separate multivariable Cox models with primary care and neurology visits specified as time-dependent covariates, adjusted for numerous patient- and systems-level factors. The cohort included 78 345 patients. Sixty-one percent and 16% of patients, respectively, had a primary care and neurology visit within 30 days of discharge. Visits occurred a median (interquartile range) 7 (4-13) and 15 (5-22) days after discharge for primary care and neurology, respectively. Thirty-day readmission occurred in 9.4% of patients. Readmissions occurred a median 14 (interquartile range, 7-21) days after discharge. Patients who had a primary care visit within 30 days of discharge had a slightly lower adjusted hazard of readmission than those who did not (hazard ratio, 0.98; 95% confidence interval, 0.97-0.98). The association was nearly identical for 30-day neurology visits (hazard ratio, 0.98; 95% confidence interval, 0.97-0.98).

CONCLUSIONS

Thirty-day outpatient follow-up was associated with a small reduction in hospital readmission among elderly patients with stroke discharged home. Further work should assess how outpatient care may be improved to further reduce readmissions.

[Severity assessment strategies based on administrative data using stroke as an example]

BACKGROUND AND OBJECTIVES

Information on disease severity is relevant for many studies with claims data in health service research, but only limited information is available in routine data. Stroke serves as an example to analyse whether the combination of different information in claims data can provide insight into the severity of a disease.

METHOD

As a first step, a literature search was conducted. Strategies to assess the severity of a disease by means of routine data were examined with regard to approval and applicability to German sickness fund data. In order to apply and extend the identified procedures, the statutory health insurance sample AOK Hessen/KV Hessen (VSH) served as data source. It is an 18.75 % random sample of persons insured by the AOK Hessen, with 2013 being the most recent year. Stroke patients were identified by the ICD-10 GM code I63 and I64. Patients with said diagnoses being coded as a hospital discharge diagnosis in 2012 were included due to an acute event in 2012 (n=944). The follow-up time was one year.

RESULTS

Ten studies covering seven different methods to assess stroke severity were identified. Codes for coma (4.2 % of stroke patients in the SHI sample) as well as coma and/or the application of a PEG tube (9.8 % of the stroke patients) were applied as a proxy for disease severity of acute cases. Taking age, sex and comorbidity into consideration, patients in a coma show a significantly increased risk of mortality compared to those without coma. Three operationalisations were chosen as possible proxies for disease severity of stroke in the further course of disease: i) sequelae (hemiplegia, neurological neglect), ii) duration of the index inpatient stay, and iii) nursing care/ care level 3 for the first time after stroke. The latter proxy has the highest explanatory value for SHI costs.

CONCLUSION

The studies identified use many variables mainly based on hospital information in order to describe disease severity. With the exception of coma, these proxies were neither validated nor did the authors provide more detailed grounds for their use. An identified score for stroke severity could not be applied to SHI data. To develop a comparable score requires a linkage of clinical and administrative data. Since routine data include information from all sectors of care, it should be explored whether these data (for example, the patients' care needs) are suitable to assess disease severity. For validation, separate databases and, optimally, primary patient data are necessary.

Community Use of Physical and Occupational Therapy After Stroke and Risk of Hospital Readmission

OBJECTIVES

To determine whether receipt of therapy and number and timing of therapy visits decreased hospital readmission risk in stroke survivors discharged home.

DESIGN

Retrospective cohort analysis of Medicare claims (2010-2013).

SETTING

Acute care hospital and community.

PARTICIPANTS

Patients hospitalized for stroke who were discharged home and survived the first 30 days (N=23,413; mean age ± SD, 77.6±7.5y).

INTERVENTIONS

Physical and occupational therapist use in the home and/or outpatient setting in the first 30 days after discharge (any use, number of visits, and days to first visit).

MAIN OUTCOME MEASURES

Hospital readmission 30 to 60 days after discharge. Covariates included demographic characteristics, proxy variables for functional status, hospitalization characteristics, comorbidities, and prior health care use. Multivariate logistic regression analyses were conducted to examine the relation between therapist use and readmission.

RESULTS

During the first 30 days after discharge, 31% of patients saw a therapist in the home, 11% saw a therapist in an outpatient setting, and 59% did not see a therapist. Relative to patients who had no therapist contact, those who saw an outpatient therapist were less likely to be readmitted to the hospital (odds ratio, 0.73; 95% confidence interval, 0.59-0.90). Although the point estimates did not reach statistical significance, there was some suggestion that the greater the number of therapist visits in the home and the sooner the visits started, the lower the risk of hospital readmission.

CONCLUSIONS

After controlling for observable demographic-, clinical-, and health-related differences, we found that individuals who received outpatient therapy in the first 30 days after discharge home after stroke were less likely to be readmitted to the hospital in the subsequent 30 days, relative to those who received no therapy.

Real-world costs of ischemic stroke by discharge status

OBJECTIVE

The objective of this study was to estimate the acute healthcare costs of ischemic stroke during hospitalization and the quarterly all-cause healthcare costs for the first year after discharge by discharge status.

METHODS

Adult patients with a hospitalization with a diagnosis of ischemic stroke (ICD-9-CM: 434.xx or 436.xx) between 1 January 2006 and 31 March 2015 were identified from a large US commercial claims database. Patients were classified into three cohorts based on their discharge status from the first stroke hospitalization, i.e. dead at discharge, discharged with disability, or discharged without disability. Third-party (medical and pharmacy) and out-of-pocket costs were adjusted to 2015 USD.

RESULTS

A total of 7919 patients dead at discharge, 45,695 patients discharged with disability, and 153,778 patients discharged without disability were included in this analysis. The overall average age was 59.7 years and 52.3% were male. During hospitalization, mean total costs (third-party and out-of-pocket) were $68,370 for patients dead at discharge, $73,903 for patients discharged with disability, and $24,448 for patients discharged without disability (p < .001 for each pairwise comparison); mean third-party costs were $63,605 for patients dead at discharge, $67,861 for patients discharged with disability and $19,267 for patients discharged without disability (p < .001 for each pairwise comparison). During the first year after discharge, mean total costs for patients discharged with disability vs. without disability were $46,850 vs. $30,132 (p < .001). Mean third-party costs for patients discharged with disability vs. without disability were $19,116 vs. $10,976 during the first quarter after discharge, $10,236 vs. $6926 during the second quarter, $8241 vs. $5810 during the third quarter, and $6875 vs. $5292 during the fourth quarter (p < .001 for each quarter).

CONCLUSION

The results demonstrated the high economic burden of ischemic stroke, especially among patients discharged with disability with the highest costs incurred during the inpatient stays.

Patient-level costs of major cardiovascular conditions: a review of the international literature

OBJECTIVE

Robust cost estimates of cardiovascular (CV) events are required for assessing health care interventions aimed at reducing the economic burden of major adverse CV events. This review synthesizes international cost estimates of CV events.

METHODS

MEDLINE database was searched electronically for English language studies published during 2007-2012, with cost estimates for CV events of interest - unstable angina, myocardial infarction, heart failure, stroke, and CV revascularization. Included studies provided at least one estimate of patient-level direct costs in adults for any identified country. Information on study characteristics and cost estimates were collected. All costs were adjusted for inflation to 2013 values.

RESULTS

Across the 114 studies included, the average cost was US $6,466 for unstable angina, $11,664 for acute myocardial infarction, $11,686 for acute heart failure, $11,635 for acute ischemic stroke, $37,611 for coronary artery bypass graft, and $13,501 for percutaneous coronary intervention. The ranges for cost estimates varied widely across countries with US cost estimate being at least twice as high as European Union costs for some conditions. Few studies were found on populations outside the US and European Union.

CONCLUSION

This review showed wide variation in the cost of CV events within and across countries, while showcasing the continuing economic burden of CV disease. The variability in costs was primarily attributable to differences in study population, costing methodologies, and reporting differences. Reliable cost estimates for assessing economic value of interventions in CV disease are needed.

Ethnic Comparison of 30-Day Potentially Preventable Readmissions After Stroke in Hawaii

BACKGROUND AND PURPOSE

Ethnic disparities in readmission after stroke have been inadequately studied. We sought to compare potentially preventable readmissions (PPR) among a multiethnic population in Hawaii.

METHODS

Hospitalization data in Hawaii from 2007 to 2012 were assessed to compare ethnic differences in 30-day PPR after stroke-related hospitalizations. Multivariable models using logistic regression were performed to assess the impact of ethnicity on 30-day PPR after controlling for age group (<65 and ≥65 years), sex, insurance, county of residence, substance use, history of mental illness, and Charlson Comorbidity Index.

RESULTS

Thirty-day PPR was seen in 840 (8.4%) of 10 050 any stroke-related hospitalizations, 712 (8.7%) of 8161 ischemic stroke hospitalizations, and 128 (6.8%) of 1889 hemorrhagic stroke hospitalizations. In the multivariable models, only the Chinese ethnicity, compared with whites, was associated with 30-day PPR after any stroke hospitalizations (odds ratio [OR] [95% confidence interval {CI}], 1.40 [1.05-1.88]) and ischemic stroke hospitalizations (OR, 1.42 [CI, 1.04-1.96]). When considering only one hospitalization per individual, the impact of Chinese ethnicity on PPR after any stroke hospitalization (OR, 1.22 [CI, 0.89-1.68]) and ischemic stroke hospitalization (OR, 1.21 [CI, 0.86-1.71]) was attenuated. Other factors associated with 30-day PPR after any stroke hospitalizations were Charlson Comorbidity Index (per unit increase) (OR, 1.21 [CI, 1.18-1.24]), Medicaid (OR, 1.42 [CI, 1.07-1.88]), Hawaii county (OR, 0.78 [CI, 0.62-0.97]), and mental illness (OR, 1.37 [CI, 1.10-1.70]).

CONCLUSIONS

In Hawaii, Chinese may have a higher risk of 30-day PPR after stroke compared with whites. However, this seems to be driven by the high number of repeated PPR within the Chinese ethnic group.

A population-based study for 30-d hospital readmissions after acute ischemic stroke

OBJECTIVE

To determine post-stroke 30-d readmission rate, its predictors, its impact on mortality and to identify potentially preventable causes of post-stroke 30-d readmission in a population-based study.

PATIENTS AND METHODS

We identified all acute ischemic strokes (AIS) using the International Classification of Diseases 9th revision codes (433.x1, 434.xx and 436) via the Rochester Epidemiology Project (REP) between January 2007 and December 2011. Acute stroke care in Olmsted County is provided by two medical centers, Saint Marys Hospital and Olmsted Medical Center Hospital. All readmissions to these two hospitals were accounted for this study. Thirty-day readmission data was abstracted through manual chart review. The REP linkage database was used to identify the status (living/dead) of all patients at last follow up.

RESULTS

Forty-one (7.6%, 95% CI 5.7%-10.2%) of total 537 AIS patients were readmitted 30-d post-stroke. In a multivariable logistic regression model, discharge to nursing home following index stroke (OR: 0.29, 95% CI 0.08-0.84) was an independent negative predictor of unplanned 30-d readmission. In a subgroup of patients with dementia, being married at time of index stroke was found to be a negative predictor of readmission (OR: 0.10, 95% CI 0.005-0.58). Only 2.8% of the patients had potentially preventable readmissions. Hospital readmission had no significant impact on patient's short-term (three months) or long-term (one or two years) mortality (p > 0.05).

CONCLUSION

Post-stroke 30-d readmission rate is low in AIS patients from Olmsted County. Further research is needed in regarding discharge checklists, protocols and stroke transitional programs to reduce potentially preventable readmissions.

Depression but not anxiety predicts recurrent cerebrovascular events

OBJECTIVES

Depression and anxiety after stroke occur frequently and have been suggested to have negative influence on functional outcomes. However, the effect of emotional symptoms on stroke recurrence is uncertain. The aim of this study was to define the effect of emotional symptoms on recurrent cerebrovascular events in patients with ischemic stroke.

MATERIALS AND METHODS

This was a hospital-based cohort study including patients with ischemic stroke who participated in a Community Stroke Care Program that provided secondary stroke prevention strategies during 6 months transition period after discharge. We examined the association between depression and anxiety and the risk of recurrent cerebrovascular events using logistic regression model. Depression and anxiety were defined as a score of 7 or more in Hospital Anxiety and Depression Scale at 2 weeks after discharge. Recurrent cerebrovascular events comprised any recurrent stroke and transient ischemic attack (TIA) occurring during 6 months after discharge.

RESULTS

Among 182 patients, 29 (15.9%) were depressed and 41 (22.5%) had anxiety symptoms. During the follow-up period, 9 patients experienced recurrent cerebrovascular events (5 of stroke and 4 of TIA). Depression was associated with recurrent cerebrovascular events at 6 months after adjustment for age, sex, and stroke severity (OR 5.22, 95% CI 1.08-25.12; P = 0.04), whereas anxiety was not (OR 0.98, 95% CI 0.2-4.92; P = 0.982).

CONCLUSIONS

Depression occurring early after stroke was associated with the increased risk of recurrent cerebrovascular events in ischemic stroke survivors. Care plan to detect and manage depression should be implemented to prevent recurrent stroke.

Exploring the predictors of early readmission to psychiatric hospital

BACKGROUND

Aims of this study are to explore the associations of readmission to psychiatric hospital over time, to develop a statistical model for early readmission to psychiatric hospital and to assess the feasibility of predicting early readmission.

METHOD

The sample comprised 7891 general psychiatric discharges in South London, taken from a large anonymised repository of electronic patient records. We initially explored time to readmission using Cox regression - this included investigation of time-dependent effects. Subsequently, we used logistic regression to create a predictive model for 90-day readmission. We investigated the effect on readmission of a set of variables that included demographic variables, diagnosis and legal status during the index admission, previous service use, housing variables and individual item scores on the Health of the Nation Outcome Scales (HoNOS) at admission and at discharge.

RESULTS

Fifteen per cent of those discharged were readmitted within 90 days. Cox regression demonstrated that the estimated baseline hazard of readmission declined steeply after discharge and that the effects of several predictors, especially diagnosis, changed over time - most notably, personality disorder was associated with increased readmission relative to schizophrenia at the time of discharge, but did not significantly differ by 1-year postdischarge. In the logistic regression, increased readmission was associated with personality disorder diagnosis; shorter length of the index admission (excepting zero length admissions); number of discharges in the preceding 2 years; and having a high score at discharge on the HoNOS overactive and aggressive behaviour item, cognitive problems item or hallucinations and delusions items. Detention under Section 3 or a forensic section of the Mental Health Act during the index admission was associated with reduced readmission. The coefficient of discrimination for the logistic regression, which is equivalent to r 2, was 0.04 and the estimated area under the receiver operating curve was 0.65.

CONCLUSIONS

The association found between early readmission and personality disorder diagnosis merits further investigation, as does the possible trade-off between reduction in length of stay and increased readmission. Other novel findings such as the associations found with HoNOS item scores also merit replication. As with previous studies, we found that the rate of readmission declines steeply after hospital discharge, so that the period immediately subsequent to discharge is a period of comparatively high risk. However, prediction of early readmission within this high-risk group remains challenging - it seems most likely that many unmeasured influences operate subsequent to the time of discharge.

Association of Rehabilitation Intensity for Stroke and Risk of Hospital Readmission

BACKGROUND

Little is known about the use of rehabilitation in the acute care setting and its impact on hospital readmissions.

OBJECTIVE

The objective of this study was to examine the association between the intensity of rehabilitation services received during the acute care stay for stroke and the risk of 30-day and 90-day hospital readmission.

DESIGN

A retrospective cohort analysis of all acute care hospitals in Arkansas and Florida was conducted.

METHODS

Patients (N=64,065) who were admitted for an incident stroke in 2009 or 2010 were included. Rehabilitation intensity was categorized as none, low, medium-low, medium-high, or high based on the sum and distribution of physical therapy, occupational therapy, and speech therapy charges within each hospital. Cox proportional hazards regression was used to estimate hazard ratios, controlling for demographic characteristics, illness severity, comorbidities, hospital variables, and state.

RESULTS

Relative to participants who received the lowest intensity therapy, those who received higher-intensity therapy had a decreased risk of 30-day readmission. The risk was lowest for the highest-intensity group (hazard ratio=0.86; 95% confidence interval=0.79, 0.93). Individuals who received no therapy were at an increased risk of hospital readmission relative to those who received low-intensity therapy (hazard ratio=1.30; 95% confidence interval=1.22, 1.40). The findings were similar, but with smaller effects, for 90-day readmission. Furthermore, patients who received higher-intensity therapy had more comorbidities and greater illness severity relative to those who received lower-intensity therapy.

LIMITATIONS

The results of the study are limited in scope and generalizability. Also, the study may not have adequately accounted for all potentially important covariates.

CONCLUSIONS

Receipt of and intensity of rehabilitation therapy in the acute care of stroke is associated with a decreased risk of hospital readmission.

Predictors of 30-day hospital readmission following ischemic and hemorrhagic stroke

Stroke patients have a high rate of 30-day readmission. Understanding the characteristics of patients at high risk of readmission is critical. A retrospective case-control study was designed to determine factors associated with 30-day readmission after stroke. A total of 79 cases with acute ischemic or hemorrhagic strokes readmitted to the same hospital within 30 days were compared with 86 frequency-matched controls. Readmitted patients were more likely to have had ≥2 hospitalizations in the year prior to stroke (21.5% vs 2.3% in controls, P < .001), and in the multivariate model, admission National Institutes of Health Stroke Score (NIHSS; odds ratio [OR] = 1.072; 95% confidence interval [CI] = 1.021-1.126 per 1 point increase; P = .005), prior hospitalizations (OR = 2.205; 95% CI = 1.426-3.412 per admission; P < .001), and absence of hyperlipidemia (OR = 0.444; 95% CI = 0.221-0.894; P = .023) were independently associated with readmission. The research team concludes that admission NIHSS and frequent prior hospitalizations are associated with 30-day readmission after stroke. If validated, these characteristics identify high-risk patients and focus efforts to reduce readmission.

Predictors of 30-day readmission after intracerebral hemorrhage: a single-center approach for identifying potentially modifiable associations with readmission

OBJECTIVE

To determine whether patient's demographics or severity of illness predict hospital readmission within 30 days following spontaneous intracerebral hemorrhage, to identify readmission associations that may be modifiable at the single-center level, and to determine the impact of readmission on outcomes.

DESIGN

We collected demographic, clinical, and hospital course data for consecutive patients with spontaneous intracerebral hemorrhage enrolled in an observational study. Readmission within 30 days was determined retrospectively by an automated query with manual confirmation. We identified the reason for readmission and tested for associations between readmission and functional outcomes using modified Rankin Scale (a validated functional outcome measure from 0, no symptoms, to 6, death) scores before intracerebral hemorrhage and at 14 days, 28 days, and 3 months after intracerebral hemorrhage.

SETTING

Neurologic ICU of a tertiary care hospital.

PATIENTS

Critically ill patients with spontaneous intracerebral hemorrhage.

INTERVENTIONS

Patients received standard critical care management for intracerebral hemorrhage.

MEASUREMENTS AND MAIN RESULTS

Of 246 patients (mean age, 65 yr; 51% female), 193 patients (78%) survived to discharge. Of these, 22 patients (11%) were readmitted at a median of 9 days (interquartile range, 4-15 d). The most common readmission diagnoses were infections after discharge (n = 10) and vascular events (n = 6). Age, history of stroke and hypertension, severity of neurologic deficit at admission, Acute Physiology and Chronic Health Evaluation score, ICU and hospital length of stay, ventilator-free days, days febrile, and surgical procedures were not predictors of readmission. History of coronary artery disease was associated with readmission (p = 0.03). Readmitted patients had similar modified Rankin Scale and severity of neurologic deficit at 14 days but higher (worse) modified Rankin Scale scores at 3 months (median [interquartile range], 5 [3-6] vs 3 [1-4]; p = 0.01).

CONCLUSIONS

Severity of illness and hospital complications were not associated with 30-day readmission. The most common indication for readmission was infection after discharge, and readmission was associated with worse functional outcomes at 3 months. Preventing readmission after intracerebral hemorrhage may depend primarily on optimizing care after discharge and may improve functional outcomes at 3 months.

Avoidable 30-day readmissions among patients with stroke and other cerebrovascular disease

BACKGROUND

There are limited data on factors associated with 30-day readmissions and the frequency of avoidable readmissions among patients with stroke and other cerebrovascular disease.

METHODS

University HealthSystem Consortium (UHC) database records were used to identify patients discharged with a diagnosis of stroke or other cerebrovascular disease at a university hospital from January 1, 2007 to December 31, 2009 and readmitted within 30 days to the index hospital. Logistic regression models were used to identify patient and clinical characteristics associated with 30-day readmission. Two neurologists performed chart reviews on readmissions to identify avoidable cases.

RESULTS

Of 2706 patients discharged during the study period, 174 patients had 178 readmissions (6.4%) within 30 days. The only factor associated with 30-day readmission was the index length of stay >10 days (vs <5 days; odds ratio [OR] 2.3, 95% CI [1.4, 3.7]). Of 174 patients readmitted within 30 days (median time to readmission 10 days), 92 (53%) were considered avoidable readmissions including 38 (41%) readmitted for elective procedures within 30 days of discharge, 27 (29%) readmitted after inadequate outpatient care coordination, 15 (16%) readmitted after incomplete initial evaluations, 8 (9%) readmitted due to delayed palliative care consultation, and 4 (4%) readmitted after being discharged with inadequate discharge instructions. Only 5% of the readmitted patients had outpatient follow-up recommended within 1 week.

CONCLUSIONS

More than half of the 30-day readmissions were considered avoidable. Coordinated timing of elective procedures and earlier outpatient follow-up may prevent the majority of avoidable readmissions among patients with stroke and other cerebrovascular disease.

The consequences of poor communication during transitions from hospital to skilled nursing facility: a qualitative study

OBJECTIVES

To examine how skilled nursing facility (SNF) nurses transition the care of individuals admitted from hospitals, the barriers they experience, and the outcomes associated with variation in the quality of transitions.

DESIGN

Qualitative study using grounded dimensional analysis, focus groups, and in-depth interviews.

SETTING

Five Wisconsin SNFs.

PARTICIPANTS

Twenty-seven registered nurses.

MEASUREMENTS

Semistructured questions guided the focus group and individual interviews.

RESULTS

SNF nurses rely heavily on written hospital discharge communication to transition individuals into the SNF effectively. Nurses cited multiple inadequacies of hospital discharge information, including regular problems with medication orders (including the lack of opioid prescriptions for pain), little psychosocial or functional history, and inaccurate information regarding current health status. These communication inadequacies necessitated repeated telephone clarifications, created care delays (including delays in pain control), increased SNF staff stress, frustrated individuals and family members, contributed directly to negative SNF facility image, and increased risk of rehospitalization. SNF nurses identified a specific list of information and components that they need to facilitate a safe, high-quality transition.

CONCLUSION

Nurses note multiple deficiencies in hospital-to-SNF transitions, with poor quality discharge communication being identified as the major barrier to safe and effective transitions. This information should be used to refine and support the dissemination of evidence-based interventions that support transitions of care, including the Interventions to Reduce Acute Care Transfers program.

Death and rehospitalization after transient ischemic attack or acute ischemic stroke: one-year outcomes from the adherence evaluation of acute ischemic stroke-longitudinal registry

BACKGROUND

Longitudinal data directly comparing the rates of death and rehospitalization of patients discharged after transient ischemic attack (TIA) versus acute ischemic stroke (AIS) are lacking.

METHODS

Data were analyzed from 2802 patients (TIA n = 552; AIS n = 2250) admitted to 100 U.S. hospitals participating in the Get With The Guidelines-Stroke and the Adherence Evaluation of Acute Ischemic Stroke-Longitudinal registry. The primary composite outcome was the adjusted rate of all-cause death and rehospitalization over 1 year after discharge. Four additional single or combined outcomes were explored.

RESULTS

Compared with AIS, TIA patients were older (median 69 v 66 years; P = .007) and more likely female (53.3% v 44.2%; P < .0001). Secondary prevention medication use after hospital discharge was less intensive after TIA, with underuse for both conditions. All-cause death or rehospitalization at 1 year was similar for TIA and AIS patients (37.7% v 34.6%; P = .271); the frequency for TIA patients was higher after covariate adjustment (hazard ratio [HR] 1.19; 95% confidence interval [CI] 1.01-1.41). One-year all-cause mortality was similar among those with TIA compared to AIS patients (3.8% v 5.7%; P = .071; adjusted HR 0.86; 95% CI 0.52-1.42). All-cause rehospitalizations were higher for TIA compared to AIS patients (36.4% v 33.0%; P = .186; adjusted HR 1.20; 95% CI 1.02-1.42), but similar for stroke rehospitalizations (10.1% v 7.4%; P = .037; adjusted HR 1.38, 95% CI 0.997-1.92).

CONCLUSIONS

Patients with TIA have similar or worse 12-month postdischarge risk of death or rehospitalization as compared with those with AIS. Outcomes after TIA and AIS might be improved with better adherence to secondary preventive guidelines.

Identifying risk of hospital readmission among Medicare aged patients: an approach using routinely collected data

Readmission provisions in the Patient Protection and Affordable Care Act of March 2010 have created urgent fiscal accountability requirements for hospitals, dependent upon a better understanding of their specific populations, along with development of mechanisms to easily identify these at-risk patients. Readmissions are disruptive and costly to both patients and the health care system. Effectively addressing hospital readmissions among Medicare aged patients offers promising targets for resources aimed at improved quality of care for older patients. Routinely collected data, accessible via electronic medical records, were examined using logistic models of sociodemographic, clinical, and utilization factors to identify predictors among patients who required rehospitalization within 30 days. Specific comorbidities and discharge care orders in this urban, nonprofit hospital had significantly greater odds of predicting a Medicare aged patient's risk of readmission within 30 days.

Why do ischemic stroke and transient ischemic attack patients get readmitted?

OBJECTIVE

Readmission is an important indicator for the quality of healthcare services. The authors examined the reasons for 30-day readmission among urban stroke patients, and their clinical consequences.

METHODS

Consecutive patients admitted to a JCAHO certified primary stroke center with ischemic stroke or transient ischemic attacks (TIA) were included. Demographics, TOAST mechanism, risk factors, treatments administered and discharge destination were collected. Charts were reviewed for readmissions up to 30 days from discharge. Reasons for readmission and outcomes in terms of disability and discharge destination were determined.

RESULTS

Two hundred sixty-five patients (50.9% male; 79.6%African American; mean age 60.9 years) were included. There were 205(77.4%) strokes and 60(22.6%) TIAs. Thirteen (5%) patients died during their first admission. Of the remaining 252 patients, 25 (9.9%) were readmitted within 30 days. The reason for readmission was neurological in 8/25 patients (32%; 3 ischemic strokes, 1 hemorrhagic stroke and 4 TIAs); and non-neurological in 17/25 patients (68%). The frequent non-neurological reasons were infections (6/25), electrolyte disturbances (3/25) and trauma related to falls (2/25). Patients with coronary artery disease were more likely to be readmitted (45.5% vs. 14.7%; p=0.001) An NIH stroke scale ≥10 predicted readmission (50.0% vs. 25.4% for NIHSS<10; p value 0.02). Patients discharged home or to acute rehabilitation units were less likely to be readmitted than those discharged to subacute rehabilitation units or nursing homes (8.2% vs. 23.8%; p value=0.01).

INTERPRETATION

Disabling strokes are more likely to be readmitted. The reason is often non-neurological, and sometimes preventable. Physicians should review cases that return within 30 days and determine best practices that prevent readmission.

Discharge destination's effect on bounce-back risk in Black, White, and Hispanic acute ischemic stroke patients

OBJECTIVE

To determine whether racial and ethnic effects on bounce-back risk (ie, movement to settings of higher care intensity within 30 d of hospital discharge) in acute stroke patients vary depending on initial posthospital discharge destination.

DESIGN

Retrospective analysis of administrative data.

SETTING

Four hundred twenty-two hospitals, southern/eastern United States.

PARTICIPANTS

All Medicare beneficiaries 65 years or more with hospitalization for acute ischemic stroke within one of the 422 target hospitals during the years 1999 or 2000 (N=63,679).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Adjusted predicted probabilities for discharge to and for bouncing back from each initial discharge site (ie, home, home with home health care, skilled nursing facility [SNF], or rehabilitation center) by race (ie, black, white, and Hispanic). Models included sociodemographics, comorbidities, stroke severity, and length of stay.

RESULTS

Blacks and Hispanics were significantly more likely to be discharged to home health care (blacks=21% [95% confidence interval (CI), 19.9-22.8], Hispanic=19% [17.1-21.7] vs whites=16% [15.5-16.8]) and less likely to be discharged to SNFs (blacks=26% [95% CI, 23.6-29.3], Hispanics=28% [25.4-31.6] vs whites=33% [31.8-35.1]) than whites. However, blacks and Hispanics were significantly more likely to bounce back when discharged to SNFs than whites (blacks=26% [95% CI, 24.2-28.6], Hispanics=28% [24-32.6] vs whites=21% [20.3-21.9]). Hispanics had a lower risk of bouncing back when discharged home than either blacks or whites (Hispanics=14% [95% CI, 11.3-17] vs blacks=20% [18.4-22.2], whites=18% [16.8-18.3]). Patients discharged to home health care or rehabilitation centers demonstrated no significant differences in bounce-back risk.

CONCLUSIONS

Racial/ethnic bounce-back risk differs depending on initial discharge destination. Additional research is needed to fully understand this variation in effect.

Senescent swallowing: impact, strategies, and interventions

The risk for disordered oropharyngeal swallowing (dysphagia) increases with age. Loss of swallowing function can have devastating health implications, including dehydration, malnutrition, pneumonia, and reduced quality of life. Age-related changes increase risk for dysphagia. First, natural, healthy aging takes its toll on head and neck anatomy and physiologic and neural mechanisms underpinning swallowing function. This progression of change contributes to alterations in the swallowing in healthy older adults and is termed presbyphagia, naturally diminishing functional reserve. Second, disease prevalence increases with age, and dysphagia is a comorbidity of many age-related diseases and/or their treatments. Sensory changes, medication, sarcopenia, and age-related diseases are discussed herein. Recent findings that health complications are associated with dysphagia are presented. Nutrient requirements, fluid intake, and nutrition assessment for older adults are reviewed relative to dysphagia. Dysphagia screening and the pros and cons of tube feeding as a solution are discussed. Optimal intervention strategies for elders with dysphagia ranging from compensatory interventions to more rigorous exercise approaches are presented. Compelling evidence of improved functional swallowing and eating outcomes resulting from active rehabilitation focusing on increasing strength of head and neck musculature is provided. In summary, although oropharyngeal dysphagia may be life threatening, so are some of the traditional alternatives, particularly for frail, elderly patients. Although the state of the evidence calls for more research, this review indicates that the behavioral, dietary, and environmental modifications emerging in this past decade are compassionate, promising, and, in many cases, preferred alternatives to the always present option of tube feeding.