Post-Acute Care Data for Predicting Readmission After Ischemic Stroke: A Nationwide Cohort Analysis Using the Minimum Data Set

A novel risk score predicting 30-day hospital re-admission of patients with acute stroke by machine learning model

BACKGROUND

The 30-day hospital re-admission rate is a quality measure of hospital care to monitor the efficiency of the healthcare system. The hospital re-admission of acute stroke (AS) patients is often associated with higher mortality rates, greater levels of disability and increased healthcare costs. The aim of our study was to identify predictors of unplanned 30-day hospital re-admissions after discharge of AS patients and define an early re-admission risk score (RRS).

METHODS

This observational, retrospective study was performed on AS patients who were discharged between 2014 and 2019. Early re-admission predictors were identified by machine learning models. The performances of these models were assessed by receiver operating characteristic curve analysis.

RESULTS

Of 7599 patients with AS, 3699 patients met the inclusion criteria, and 304 patients (8.22%) were re-admitted within 30 days from discharge. After identifying the predictors of early re-admission by logistic regression analysis, RRS was obtained and consisted of seven variables: hemoglobin level, atrial fibrillation, brain hemorrhage, discharge home, chronic obstructive pulmonary disease, one and more than one hospitalization in the previous year. The cohort of patients was then stratified into three risk categories: low (RRS = 0-1), medium (RRS = 2-3) and high (RRS >3) with re-admission rates of 5%, 8% and 14%, respectively.

CONCLUSIONS

The identification of risk factors for early re-admission after AS and the elaboration of a score to stratify at discharge time the risk of re-admission can provide a tool for clinicians to plan a personalized follow-up and contain healthcare costs.

Stroke Subtype and Risk of Subsequent Hospitalization: The Atherosclerosis Risk in Communities Study

BACKGROUND AND OBJECTIVES

Risk of readmission after stroke differs by stroke (sub)type and etiology, with higher risks reported for hemorrhagic stroke and cardioembolic stroke. We examined the risk and cause of first readmission by stroke subtype over the years post incident stroke.

METHODS

Atherosclerosis Risk in Communities (ARIC) study participants (n = 1,412) with first-ever stroke were followed up for all-cause readmission after incident stroke. Risk of first readmission was examined by stroke subtypes (cardioembolic, thrombotic/lacunar, and hemorrhagic [intracerebral and subarachnoid]) using Cox and Fine-Gray proportional hazards models, adjusting for sociodemographic and cardiometabolic risk factors.

RESULTS

Among 1,412 participants (mean [SD] age 72.4 [9.3] years, 52.1% women, 35.3% Black), 1,143 hospitalizations occurred over 41,849 person-months. Overall, 81% of participants were hospitalized over a maximum of 26.6 years of follow-up (83% of participants with thrombotic/lacunar stroke, 77% of participants with cardioembolic stroke, and 78% of participants with hemorrhagic stroke). Primary cardiovascular and cerebrovascular diagnoses were reported for half of readmissions. Over the entire follow-up period, compared with cardioembolic stroke, readmission risk was lower for thrombotic/lacunar stroke (hazard ratio [HR] 0.82, 95% CI 0.71-0.95) and hemorrhagic stroke (HR 0.74, 95% CI 0.58-0.93) in adjusted Cox proportional hazards models. By contrast, there was no statistically significant difference among subtypes when adjusting for atrial fibrillation and competing risk of death. Compared with cardioembolic stroke, thrombotic/lacunar stroke was associated with lower readmission risk within 1 month (HR 0.66, 95% CI 0.46-0.93) and during 1 month-1 year (HR 0.78, 95% CI 0.62-0.97), and hemorrhagic stroke was associated with lower risk during 1 month-1 year (HR 0.60, 95% CI 0.41-0.87). There was no significant difference between subtypes in readmission risk during later periods.

DISCUSSION

Over 26 years of follow-up, 81% of stroke participants experienced a readmission. Cardiovascular and cerebrovascular diagnoses at readmission were most common across stroke subtypes. Though cardioembolic stroke has previously been reported to confer higher risk of readmission, in this study, the readmission risk was not statistically significantly different between stroke subtypes or over different periods when accounting for the competing risk of death.

Health effects of social connectedness in older adults living in congregate long-term care settings: A systematic review of quantitative and qualitative evidence

BACKGROUND

The overall impact of social connectedness on health outcomes in older adults living in nursing homes and assisted living settings is unknown. Given the unclear health impact of social connectedness for older adults in congregate long-term care settings worldwide, a comprehensive systematic review is required to evaluate the overall relationship between social connectedness and health outcomes for them.

OBJECTIVES

The purpose of this article was to synthesize the literature regarding the health impact of social connectedness among older adults living in nursing homes or assisted living settings.

METHODS

Using PRISMA guidelines, we identified eligible studies from Scopus, MEDLINE, PsycINFO, CINAHL and Cochrane databases (1990-2021). Bias and quality reporting assessment was performed using standardized criteria for cohort, cross sectional and qualitative studies. At each stage, ≥ 2 researchers conducted independent evaluations.

RESULTS

Of the 7350 articles identified, 25 cohort (follow-up range: 1 month-11 years; with two also contributing to cross sectional), 86 cross sectional, eight qualitative and two mixed methods were eligible. Despite different instruments used, many residents living in nursing homes and assisted living settings had reduced social engagement. Quantitative evidence supports a link between higher social engagement and health outcomes most studied (e.g. depression, quality of life). Few studies evaluated important health outcomes (e.g. cognitive and functional decline). Most cohort studies showed that lack of social connectedness accelerated time to death.

CONCLUSIONS

Social connectedness may be an important modifiable risk factor for adverse health outcomes for older adults living in nursing homes and assisted living facilities. Most studies were cross sectional and focused on quality of life and mental health outcomes. Longitudinal studies suggest that higher social engagement delays time to death. Evidence regarding other health outcomes important to older adults was scant and requires further longitudinal studies.

Rates and reasons for hospital readmission after acute ischemic stroke in a US population-based cohort

Hospital readmissions following stroke are costly and lead to worsened patient outcomes. We examined readmissions rates, diagnoses at readmission, and risk factors associated with readmission following acute ischemic stroke (AIS) in a large United States (US) administrative database. Using the 2019 Nationwide Readmissions Database, we identified adults discharged with AIS (ICD-10-CM I63*) as the principal diagnosis. Survival analysis with Weibull accelerated failure time regression was used to examine variables associated with hospital readmission. In 2019, 273,811 of 285,451 AIS patients survived their initial hospitalization. Of these, 60,831 (22.2%) were readmitted within 2019. Based on Kaplan Meyer analysis, readmission rates were 9.7% within 30 days and 30.5% at 1 year following initial discharge. The most common causes of readmissions were stroke and post stroke sequalae (25.4% of 30-day readmissions, 15.0% of readmissions between 30-364 days), followed by sepsis (10.3% of 30-day readmissions, 9.4% of readmissions between 30-364 days), and acute renal failure (3.2% of 30-day readmissions, 3.0% of readmissions between 30-364 days). After adjusting for multiple patient and hospital-level characteristics, patients at increased risk of readmission were older (71.6 vs. 69.8 years, p<0.001) and had longer initial lengths of stay (7.6 vs. 6.2 day, p<0.001). They more often had modifiable comorbidities, including vascular risk factors (hypertension, diabetes, atrial fibrillation), depression, epilepsy, and drug abuse. Social determinants associated with increased readmission included living in an urban (vs. rural) setting, living in zip-codes with the lowest median income, and having Medicare insurance. All factors were significant at p<0.001. Unplanned hospital readmissions following AIS were high, with the most common reasons for readmission being recurrent stroke and post stroke sequalae, followed by sepsis and acute renal failure. These findings suggest that efforts to reduce readmissions should focus on optimizing secondary stroke and infection prevention, particularly among older socially disadvantaged patients.

Stroke nurse navigator utilization reduces unplanned 30-day readmission in stroke patients treated with thrombolysis

Background

Unplanned 30-day hospital readmissions following a stroke is a serious quality and safety issue in the United States. The transition period between the hospital discharge and ambulatory follow-up is viewed as a vulnerable period in which medication errors and loss of follow-up plans can potentially occur. We sought to determine whether unplanned 30-day readmission in stroke patients treated with thrombolysis can be reduced with the utilization of a stroke nurse navigator team during the transition period.

Methods

We included 447 consecutive stroke patients treated with thrombolysis from an institutional stroke registry between January 2018 and December 2021. The control group consisted of 287 patients before the stroke nurse navigator team implementation between January 2018 and August 2020. The intervention group consisted of 160 patients after the implementation between September 2020 and December 2021. The stroke nurse navigator interventions included medication reviews, hospitalization course review, stroke education, and review of outpatient follow-ups within 3 days following the hospital discharge.

Results

Overall, baseline patient characteristics (age, gender, index admission NIHSS, and pre-admission mRS), stroke risk factors, medication usage, and length of hospital stay were similar in control vs. intervention groups (P > 0.05). Differences included higher mechanical thrombectomy utilization (35.6 vs. 24.7%, P = 0.016), lower pre-admission oral anticoagulant use (1.3 vs. 5.6%, P = 0.025), and less frequent history of stroke/TIA (14.4 vs. 27.5%, P = 0.001) in the implementation group. Based on an unadjusted Kaplan-Meier analysis, 30-day unplanned readmission rates were lower during the implementation period (log-rank P = 0.029). After adjustment for pertinent confounders including age, gender, pre-admission mRS, oral anticoagulant use, and COVID-19 diagnosis, the nurse navigator implementation remained independently associated with lower hazards of unplanned 30-day readmission (adjusted HR 0.48, 95% CI 0.23-0.99, P = 0.046).

Conclusion

The utilization of a stroke nurse navigator team reduced unplanned 30-day readmissions in stroke patients treated with thrombolysis. Further studies are warranted to determine the extent of the results of stroke patients not treated with thrombolysis and to better understand the relationship between resource utilization during the transition period from discharge and quality outcomes in stroke.

Association between the LACE+ index and unplanned 30-day hospital readmissions in hospitalized patients with stroke

Background

The LACE+ index is used to predict unplanned 30-day hospital readmissions, but its utility to predict 30-day readmission in hospitalized patients with stroke is unknown.

Methods

We retrospectively analyzed 1,657 consecutive patients presenting with ischemic or hemorrhagic strokes, included in an institutional stroke registry between January 2018 and August 2020. The primary outcome of interest was unplanned 30-day readmission for any reason after index hospitalization for stroke. The 30-day readmission risk was categorized by LACE+ index to high risk (≥78), medium-to-high risk (59–77), medium risk (29–58), and low risk (≤ 28). Kaplan-Meier analysis, Log rank test, and multivariable Cox regression analysis (with backward elimination) were used to determine whether the LACE+ score was an independent predictor for 30-day unplanned readmission.

Results

The overall 30-day unplanned readmission rate was 11.7% (194/1,657). The median LACE+ score was higher in the 30-day readmission group compared to subjects that had no unplanned 30-day readmission [74 (IQR 67–79) vs. 70 (IQR 62–75); p < 0.001]. On Kaplan-Meier analysis, the high-risk group had the shortest 30-day readmission free survival time as compared to medium and medium-to-high risk groups (p < 0.01, each; statistically significant). On fully adjusted multivariable Cox-regression, the highest LACE+ risk category was independently associated with the unplanned 30-day readmission risk (per point: HR 1.67 95%CI 1.23–2.26, p = 0.001).

Conclusion

Subjects in the high LACE+ index category had a significantly greater unplanned 30-day readmission risk after stroke as compared to lower LACE+ risk groups. This supports the validity of the LACE+ scoring system for predicting unplanned readmission in subjects with stroke. Future studies are warranted to determine whether LACE+ score-based risk stratification can be used to devise early interventions to mitigate the risk for unplanned readmission.

Predicting 30-Day Readmission for Stroke Using Machine Learning Algorithms: A Prospective Cohort Study

Background

Machine learning algorithms for predicting 30-day stroke readmission are rarely discussed. The aims of this study were to identify significant predictors of 30-day readmission after stroke and to compare prediction accuracy and area under the receiver operating characteristic (AUROC) curve in five models: artificial neural network (ANN), K nearest neighbor (KNN), random forest (RF), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX) models.

Methods

The subjects of this prospective cohort study were 1,476 patients with a history of admission for stroke to one of six hospitals between March, 2014, and September, 2019. A training dataset (n = 1,033) was used for model development, and a testing dataset (n = 443) was used for internal validation. Another 167 patients with stroke recruited from October, to December, 2019, were enrolled in the dataset for external validation. A feature importance analysis was also performed to identify the significance of the selected input variables.

Results

For predicting 30-day readmission after stroke, the ANN model had significantly (P &lt; 0.001) higher performance indices compared to the other models. According to the ANN model results, the best predictor of 30-day readmission was PAC followed by nasogastric tube insertion and stroke type (P &lt; 0.05). Using a machine learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients.

Conclusion

Using a machine-learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients. For stroke patients who are candidates for PAC rehabilitation, these predictors have practical applications in educating patients in the expected course of recovery and health outcomes.

TASC (Telehealth After Stroke Care): a study protocol for a randomized controlled feasibility trial of telehealth-enabled multidisciplinary stroke care in an underserved urban setting

Background

Hypertension is the most important modifiable risk factor for recurrent stroke, and blood pressure (BP) reduction is associated with decreased risk of stroke recurrence. However, hypertension remains poorly controlled in many stroke survivors. Black and Hispanic patients have a higher prevalence of uncontrolled BP and higher rates of stroke. Limited access to care contributes to challenges in post-stroke care. Telehealth After Stroke Care (TASC) is a telehealth intervention that integrates remote BP monitoring (RBPM) including nursing telephone support, tailored BP infographics and telehealth video visits with a multidisciplinary team approach including pharmacy to improve post-stroke care and reduce stroke disparities.

Methods

In this pilot trial, 50 acute stroke patients with hypertension will be screened for inclusion prior to hospital discharge and randomized to usual care or TASC. Usual care patients will be seen by a primary care nurse practitioner at 1–2 weeks and a stroke neurologist at 1 and 3 months. In addition to these usual care visits, TASC intervention patients will see a pharmacist at 4 and 8 weeks and will be enrolled in RBPM consisting of home BP monitoring with interval calls by a centralized team of telehealth nurses. As part of RBPM, TASC patients will be provided with a home BP monitoring device and electronic tablet that wirelessly transmits home BP data to the electronic health record. They will also receive tailored BP infographics that help explain their BP readings. The primary outcome will be feasibility including recruitment, adherence to at least one video visit and retention rates. The clinical outcome for consideration in a subsequent trial will be within-patient change in BP from baseline to 3 months after discharge. Secondary outcomes will be medication adherence self-efficacy and satisfaction with post-stroke telehealth, both measured at 3 months. Additional patient reported outcomes will include depression, cognitive function, and socioeconomic determinants. Multidisciplinary team competency and fidelity measures will also be assessed.

Conclusions

Integrated team-based interventions may improve BP control and reduce racial/ethnic disparities in post-stroke care. TASC is a post-acute stroke care model that is novel in providing RBPM with tailored infographics, and a multidisciplinary team approach including pharmacy. Our pilot will determine if such an approach is feasible and effective in enhancing post-stroke BP control and promoting self-efficacy.

Trial registration

ClinicalTrials.gov NCT04640519

Supplementary Information

The online version contains supplementary material available at 10.1186/s40814-022-01025-z.

High Risk Features Contributing to 30-Day Readmission After Acute Ischemic Stroke: A Single Center Retrospective Case-Control Study

BACKGROUND AND PURPOSE

Risk of 30-day stroke readmission has been attributed to medical comorbidities, stroke severity, and hospitalization metrics. The leading etiologies appear to vary across institutions and remain a moving target. We hypothesized that patients with increased medical complexity have higher odds of 30-day readmission and the immediate time after discharge may be most vulnerable. We aimed to characterize patients with 30-day readmission after acute ischemic stroke (IS) and identify predictors of post-IS readmission.

METHODS

We performed a retrospective case-control study analyzing post-IS 30-day readmission between January 2016-December 2019 using data from Mount Sinai Hospital's Get With The Guidelines database. We performed chi square analyses and multivariate adjusted logistic regression model including age, sex, coronary artery disease (CAD), renal insufficiency (RI), history of prior stroke or TIA, length of stay (LOS) > 7, and NIHSS ≥ 5.

RESULTS

6.7% (n = 115) of 1,706 IS encounters had 30-day readmission. The 115 cases were compared to 1,591 controls without 30-day readmission. In our adjusted model, CAD (OR = 1.7, p = 0.01), history of prior stroke or TIA (OR = 1.6, p = 0.01), LOS >7 (OR = 1.7, p = 0.02), and NIHSS ≥ 5 (OR = 4.5, p < 0.001) predicted 30-day readmission. 65% (n = 75) of readmitted patients had readmission within 14 days post-discharge.

CONCLUSIONS

Patients with post-IS 30-day readmission were more likely to have complex medical comorbidities and history of stroke or TIA compared to controls. Patients with more severe stroke and longer LOS may benefit from individualized transition of care plans and closer follow up during the vulnerable 30-day post-stroke period.

Social connection and physical health outcomes among long-term care home residents: a scoping review

BACKGROUND

Social connection is recognized as an important determinant of health and well-being. The negative health impacts of poor social connection have been reported in research in older adults, however, less is known about the health impacts for those living in long-term care (LTC) homes. This review seeks to identify and summarize existing research to address the question: what is known from the literature about the association between social connection and physical health outcomes for people living in LTC homes?

METHODS

A scoping review guided by the Arksey & O'Malley framework was conducted. Articles were included if they examined the association between social connection and a physical health outcome in a population of LTC residents.

RESULTS

Thirty-four studies were included in this review. The most commonly studied aspects of social connection were social engagement (n = 14; 41%) and social support (n = 10; 29%). A range of physical health outcomes were assessed, including mortality, self-rated health, sleep, fatigue, nutrition, hydration, stress, frailty and others. Findings generally support the positive impact of social connection for physical health among LTC residents. However, most of the studies were cross-sectional (n = 21; 62%) and, of the eleven cohort studies, most (n = 8; 73%) assessed mortality as the outcome. 47% (n = 16) were published from 2015 onwards.

CONCLUSIONS

Research has reported positive associations between social connection and a range of physical health outcomes among LTC residents. These findings suggest an important role for social connection in promoting physical health. However, further research is needed to consider the influence of different aspects of social connection over time and in different populations within LTC homes as well as the mechanisms underlying the relationship with health.

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.

Remote Longitudinal Inpatient Acute Stroke Care Via Telestroke

OBJECTIVES

While telestroke 'hub-and-spoke' systems are a well-established model for improving acute stroke care at spoke facilities, utility beyond the hyperacute phase is unknown. In patients receiving intravenous thrombolysis via telemedicine, care at spoke facilities has been shown to be associated with longer length of stay and worse outcomes. We sought to explore the impact of ongoing stroke care by a vascular neurologist via telemedicine compared to care provided by local neurologists.

METHODS

A network spoke facility protocol was revised to pilot telestroke consultation with a hub vascular neurologist for all patients presenting to the emergency department with ischemic stroke or transient ischemic attack regardless of time since onset or severity. Subsequent telestroke rounds were performed for patients who received initial telestroke consultation. Key outcome measures were length of stay, 30-day readmission and mortality and 90-day mRS. Results during the pilot (post-cohort) were compared to the same hospital's previous outcomes (pre-cohort).

RESULTS

Of 257 enrolled patients, 67% were in the post-cohort. Forty percent (69) of the post-cohort received an initial telestroke consult. In spoke-retained patients followed by telestroke rounds (55), median length of stay decreased by 0.8 days (P = 0.01). Readmission and mortality rates did not differ significantly between groups (19.5 vs. 9.1%, P = 0.14 and 3.9 vs. 3.6%, P = 1, respectively). The favorable functional outcome rate was similar between groups (47.3% vs 65.9%, P = 0.50).

CONCLUSIONS

Longitudinal stroke care via telestroke may be economically viable through length of stay reduction. Randomized prospective studies are needed to confirm our findings and further investigate this model's potential benefits.

Medicaid Expansion and Racial/Ethnic Differences in Readmission After Acute Ischemic Stroke

To examine whether rates of 30-day readmission after acute ischemic stroke changed differentially between Medicaid expansion and non-expansion states, and whether race/ethnicity moderated this change, we conducted a difference-in-differences analysis using 6 state inpatient databases (AR, FL, GA, MD, NM, and WA) from the Healthcare Cost and Utilization Project. Analysis included all patients aged 19-64 hospitalized in 2012–2015 with a principal diagnosis of ischemic stroke and a primary payer of Medicaid, self-pay, or no charge, who resided in the state where admitted and were discharged alive (N=28 330). No association was detected between Medicaid expansion and readmission overall, but there was evidence of moderation by race/ethnicity. The predicted probability of all-cause readmission among non-Hispanic White patients rose an estimated 2.6 percentage points (or 39%) in expansion states but not in non-expansion states, whereas it increased by 1.5 percentage points (or 23%) for non-White and Hispanic patients in non-expansion states. Therefore, Medicaid expansion was associated with a rise in readmission probability that was 4.0 percentage points higher for non-Hispanic Whites compared to other racial/ethnic groups, after adjustment for covariates. Similar trends were observed when unplanned and potentially preventable readmissions were isolated. Among low-income stroke survivors, we found evidence that 2 years of Medicaid expansion promoted rehospitalization, but only for White patients. Future studies should verify these findings over a longer follow-up period.

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.

Characteristics of Acute Stroke Patients Readmitted to Inpatient Rehabilitation Facilities: A Cohort Study

BACKGROUND

Reducing acute care readmissions from inpatient rehabilitation facilities (IRFs) is a healthcare reform goal. Stroke patients have higher acute readmission rates and persistent impairments, warranting second IRF hospitalization consideration.

OBJECTIVE

To provide evidence-based information to justify IRF readmission for patients with post-stroke impairments.

MAIN OUTCOME MEASURE

Variables that increase the likelihood of a second IRF hospitalization.

DESIGN

Retrospective cohort study.

SETTING

Seven-center rehabilitation network.

PARTICIPANTS

Stroke patients, readmitted to acute care, who returned or did not return to an in-network IRF between 1 October 2014-31 December 2017(n = 380).

INTERVENTIONS

Univariable analyses (Returned/Did Not Return to IRF) described demographics, stroke type and risk factors. Between group differences in readmission causes, motor impairments and functional independence measure (FIM) scores were examined. Return to IRF logistic regression model included variables with P < .1. Odds ratio and 95% CI were calculated; Relative risk was calculated for categorical variables. P < .05 equaled statistical significance.

RESULTS

One hundred ninety-two stroke patients returned to IRF, 188 did not. Returned to IRF patients were younger (60.6 vs. 66 years; P < .001), sustained hemorrhagic strokes (22.4 vs. 14.2%; P = .01), had lower cardiac disease prevalence (41.7 vs. 55.3%; P = .008) or non-Medicare insurance (59.9 vs. 39.4%; P < .001). Did Not Return to IRF patients had higher admission and discharge motor and total FIM scores. Per point decrease in discharge FIM, second IRF hospitalization odds increased 4% (OR 1.04; 95% CI 1.01-1.07; P = .02). Hemorrhagic stroke patients had 33% increased odds or a 15% higher relative risk of second IRF hospitalization than patients with ischemic stroke [OR 1.33; 95% CI 1.21-1.47; RR 1.15; 95% CI 1.1-1.2; P < .001]. Non-Medicare insurance was associated with 39% increased odds or a 20% higher relative risk of second IRF hospitalization than Medicare [OR 1.39; 95% CI 1.01-1.92; RR 1.2, 95% CI 1.006-1.404; P = .04).

CONCLUSIONS

Hemorrhagic stroke, non-Medicare insurance or lower discharge FIM score during the first IRF hospitalization predict a second IRF stay. Further work is needed to establish the validity of within IRF stay readmission measures.

Predictors, causes and outcome of 30-day readmission among acute ischemic stroke

BACKGROUND AND PURPOSE

Readmission within 30 days of index acute ischemic stroke (AIS) after hospitalization increases the burden on patients and healthcare expense. The purpose of our study was to investigate predictors and causes of 30-day readmission after AIS and investigate hospitalization expenses, length of stay (LOS) and in-hospital mortality of 30-day readmission.

METHODS

This is a multicenter retrospective study. AIS were captured by International Classification of Diseases, Tenth Revision (ICD-10) diagnosis codes, patients with readmitted within 30 days after discharge were identified as readmission group. Multivariable logistic regression was used to identify independent predictors of 30-day readmissions. Hospitalization expenses, LOS and in-hospital mortality were compared for index admission and readmission.

RESULTS

We identified 2371 patients with AIS, 176 patients died before discharge, 504(23.0%) patients were admitted within 30 days. Older age, prior stroke, non-neurology floor during index admission, indwelling urinary catheter and diabetes were independently associated with increased risk of 30-day readmission (P<0.05). The most common causes for 30-day readmission were infection (28.8%) and recurrent stroke and TIA (22.8%). Patients with 30-day readmission have longer LOS and higher hospitalization expenses on readmission compared with the mean of these metrics on index admission (P<0.001). The in-hospital mortality after a within 30-day readmission was higher than index admission (13.1% vs 8.0%; OR 1.88, 95% CI 2.5-5.3; P<0.001).

CONCLUSIONS

Older age, stroke severity, prior stroke, diabetes, indwelling urinary catheter and admission to non-neurology floor during index admission were associated with 30-day readmission. 30-readmission after AIS increased hospitalization expenses, LOS and in-hospital mortality.

Patient Factors Associated With Attendance at a Comprehensive Postacute Stroke Visit: Insight From the Vanguard Site

Objective

To understand the patient-influenced activities and characteristics associated with return to a single postacute care transitional care clinic visit in a cohort of patients cared for at the test health system site of the larger Comprehensive Post-Acute Stroke Services (COMPASS) cluster randomized trial.

Design

Retrospective cohort.

Setting

A large health system.

Participants

Patients discharged directly home between June 2016 and June 2018 after sustaining a stroke who did not receive formal inpatient rehabilitation services while being cared for in a single comprehensive stroke center, defined as a center that meet standards to rapidly diagnose and treat the most complex stroke cases.

Interventions

Study participants had the opportunity to participate in a (1) 2-day call, (2) comprehensive care transitions clinic visit, and (3) individualized care plan.

Main Outcome Measures

Patient participation in a single postacute care comprehensive care transitions visit, ideally completed within 7-14 calendar days post discharge vs not attending this visit. Care transition visits are where the responsibility for preventive care, other services, and posthospital follow-up are transitioned to outpatient providers.

Results

Among 1300 eligible patients (mean age 64.8 years; 45% female; 25.4% nonwhite; 9.7% uninsured), 95.7% had follow-up clinic visits scheduled before discharge, 22.6% received home health referrals before discharge, 60.2% completed the 2-day call, and 63.2% attended the COMPASS visit. Among attendees, 33.2% attended by day 14, 71.3% attended within 30 days, and 28.7% attended after day 30. The median driving distance to the COMPASS visit was 45.9 miles or 73.9 km. Odds of visit attendance were higher if COMPASS 2-day follow up calls were completed, if follow-up clinic appointments were scheduled before discharge, if the patient had a primary care provider, and if the patients experienced a stroke vs a transient ischemic attack. Additionally, when we used the number of referrals at hospital discharge for different types of outpatient therapy as a surrogate marker of poststroke impairment, patients having no therapy referrals (milder to no impairments) had lower odds of attending the COMPASS visit than those with 1 therapy referral. Likewise, those with more than 1 referral were also less likely to attend the COMPASS visit.

Conclusions

This analysis highlights that scheduling visits at discharge and completing timely telephone follow-up shortly after discharge may lead to greater adherence to in-person clinic follow-up after stroke.

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.

Post-acute care referral in United States of America: a multiregional study of factors associated with referral destination in a cohort of patients with coronary artery bypass graft or valve replacement

Background

The use of post-acute care (PAC) for cardiovascular conditions is highly variable across geographical regions. Although PAC benefits include lower readmission rates, better clinical outcomes, and lower mortality, referral patterns vary widely, raising concerns about substandard care and inflated costs. The objective of this study is to identify factors associated with PAC referral decisions at acute care discharge.

Methods

This study is a retrospective Electronic Health Records (EHR) based review of a cohort of patients with coronary artery bypass graft (CABG) and valve replacement (VR). EHR records were extracted from the Cerner Health-Facts Data warehouse and covered 49 hospitals in the United States of America (U.S.) from January 2010 to December 2015. Multinomial logistic regression was used to identify associations of 29 variables comprising patient characteristics, hospital profiles, and patient conditions at discharge.

Results

The cohort had 14,224 patients with mean age 63.5 years, with 10,234 (71.9%) male and 11,946 (84%) Caucasian, with 5827 (40.96%) being discharged to home without additional care (Home), 5226 (36.74%) to home health care (HHC), 1721 (12.10%) to skilled nursing facilities (SNF), 1168 (8.22%) to inpatient rehabilitation facilities (IRF), 164 (1.15%) to long term care hospitals (LTCH), and 118 (0.83%) to other locations. Census division, hospital size, teaching hospital status, gender, age, marital status, length of stay, and Charlson comorbidity index were identified as highly significant variables (p- values < 0.001) that influence the PAC referral decision. Overall model accuracy was 62.6%, and multiclass Area Under the Curve (AUC) values were for Home: 0.72; HHC: 0.72; SNF: 0.58; IRF: 0.53; LTCH: 0.52, and others: 0.46.

Conclusions

Census location of the acute care hospital was highly associated with PAC referral practices, as was hospital capacity, with larger hospitals referring patients to PAC at a greater rate than smaller hospitals. Race and gender were also statistically significant, with Asians, Hispanics, and Native Americans being less likely to be referred to PAC compared to Caucasians, and female patients being more likely to be referred than males. Additional analysis indicated that PAC referral practices are also influenced by the mix of PAC services offered in each region.

Prediction Model of Early Return to Hospital after Discharge Following Acute Ischemic Stroke

BACKGROUND

Reducing hospital readmissions for stroke remains a significant challenge to improve outcomes and decrease healthcare costs.

METHODS

We analyzed 10,034 adult patients with ischemic stroke, presented within 24 hours of onset from a hospital-based stroke registry. The risk factors for early return to hospital after discharge were analyzed using multivariate logistic regression and classification and regression tree (CART) analyses.

RESULTS

Among the study population, 277 (2.8%) had 3-day Emergency Department (ED) reattendance, 534 (5.3%) had 14-day readmission, and 932 (9.3%) had 30-day readmission. Multivariate logistic regression revealed that age, nasogastric tube feeding, indwelling urinary catheter, healthcare utilization behaviour, and stroke severity were major and common risk factors for an early return to the hospital after discharge. CART analysis identified nasogastric tube feeding and length of stay for 72-hour ED reattendance, Barthel Index (BI) score, total length of stay in the Year Preceding the index admission (YLOS), indwelling urinary catheter, and age for 14-day readmission, and nasogastric tube feeding, BI score, YLOS, and number of inpatient visits in the year preceding the index admission for 30-day readmission as important factors to classify the patients into subgroups.

CONCLUSION

Although CART analysis did not improve the prediction of an early return to the hospital after stroke compared with logistic regression models, decision rules generated by CART can easily be interpreted and applied in clinical practice.

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.

Hospital use in survivors of transient ischaemic attack compared with survivors of stroke in central China: a nested case-control study

OBJECTIVES

There is a lack of knowledge regarding post-discharge hospitalisation utilisation after transient ischaemic attack (TIA) in China. The aim of this study is to quantify rehospitalisation use in survivors of TIA compared with their own previous hospital use and matched survivors of stroke.

DESIGN

Nested case-control study of electronic medical records datasets.

SETTING

958 hospitals in Henan, China, from July 2012 to December 2015.

PARTICIPANTS

In total, 4823 survivors of stroke were matched to the TIA cohort (average age: 64.5 years; proportion of men: 48.4%) at a 1:1 ratio. All subjects with an onset of stroke/TIA were recorded with a 1-year look-back and follow-up.

OUTCOME MEASURES

Adjusted difference-in-differences (DID) values in 1-year hospital lengths of stay (LOSs) and readmission within 7, 30 and 90 days.

RESULTS

There was an increase in hospital admissions in survivors of TIA in the year after the index hospitalisation compared with the prior year. Of the 2449 rehospitalisation events that occurred during the first year after TIA, stroke (20.6%) was the most common reason for rehospitalisation. There was no difference in the stroke-specific readmission rates between the TIA and stroke cohorts (p=0.198). The TIA cohort had fewer readmissions within 30 days and 90 days after all-cause discharge compared with the controls. The corresponding covariate-adjusted DID values were -3.5 percentage points (95% CI -5.3 to -1.8) and -4.5 (95% CI -6.5 to -2.4), respectively. A similar trend was observed in the 1-year LOS. In the stratified analysis, the DID reductions were not significant in patients with more comorbidities or in rural patients.

CONCLUSIONS

Compared with survivors of stroke, survivors of TIA use fewer hospital resources up to 1 year post-discharge. Greater attention to TIAs among patients with more comorbidities and rural patients may provide an opportunity to reduce hospital use.

One-year versus five-year hospital readmission after ischemic stroke and TIA

Background

The burden of hospital readmission after stroke is substantial, but little knowledge exists on factors associated with long-term readmission after stroke. In a cohort comprising patients with ischemic stroke and transient ischemic attack (TIA), we examined and compared factors associated with readmission within 1 year and first readmission during year 2–5.

Methods

Patients with ischemic stroke or TIA who were discharged alive between July 2007 and October 2012, were followed for 5 years by review of medical charts. The timing and primary cause of the first unplanned readmission were registered. Cox regression was used to identify independent risk factors for readmission within 1 year and first readmission during year 2–5 after discharge.

Results

The cohort included 1453 patients, of whom 568 (39.1%) were readmitted within 1 year. Of the 830 patients that were alive and without readmission 1 year after discharge, 439 (52.9%) were readmitted within 5 years. Patients readmitted within 1 year were older, had more severe strokes, poorer functional outcome, and a higher occurrence of complications during index admission than patients readmitted during year 2–5. Cardiovascular comorbidity and secondary preventive treatment did not differ between the two groups of readmitted patients. Higher age, poorer functional outcome, coronary artery disease and hypertension were independently associated with readmission within both 1 year and during year 2–5. Peripheral artery disease was independently associated with readmission within 1 year, and atrial fibrillation was associated with readmission during year 2–5.

Conclusions

More than half of all patients who survived the first year after stroke without any readmissions were readmitted within 5 years. Patients readmitted within 1 year and between years 2–5 shared many risk factors for readmission, but they differed in age, functional outcome and occurrence of complications during the index admission.

Association between medication-related adverse events and non-elective readmission in acute ischemic stroke

Background

There is limited data on the effects of medication-related adverse events occurring during inpatient stays for stroke. The objectives of our study were to characterize reasons for acute readmission after acute ischemic stroke (AIS) and determine if medication-related adverse events occuring during AIS hospitalization were associated with 30-day readmission. Secondary objectives examined whether demographic, clinical, and hospital characterisitcs were associated with post-AIS readmission.

Methods

We used the Nationwide Readmission Database to identify index AIS hospitalizations in the United States between January and November 2014. Inpatient records were screened for diagnostic and external causes of injury codes indicative of medication-related adverse events, including adverse effects of prescribed drugs, unintentional overdosing, and medication errors. Nationally representative estimates of AIS hospitalizations, medication-related adverse events, and acute non-elective readmissions were computed using survey weighting methods. Adjusted odds of readmission for medication-related adverse events and select characteristics were estimated using unconditional logistic regression.

Results

We identified 439,682 individuals who were hospitalized with AIS, 4.7% of whom experienced a medication-related adverse event. Overall, 10.7% of hospitalized individuals with AIS were readmitted within 30 days of discharge. Reasons for readmission were consistent with those observed among older adults. Inpatients who experienced medication-related adverse events had significantly greater odds of being readmitted within 30 days (adjusted odds ratio (AOR): 1.22; 95% CI: 1.14–1.30). Medication-related adverse events were associated with readmission for non-AIS conditions (AOR, 1.26; 95% CI: 1.17–1.35), but not with readmission for AIS (AOR, 0.91; 95% CI: 0.75–1.10). Several factors, including but not limited to being younger than 40 years (AOR, 1.12; 95% CI: 1.00–1.26), Medicare insurance coverage (AOR, 1.33; 95% CI: 1.26–1.40), length of stay greater than 1 week (AOR, 1.38; 95% CI: 1.33–1.42), having 7 or more comorbidites (AOR, 2.20; 95% CI: 2.08–2.34), and receiving care at a for-profit hospital (AOR, 1.20; 95% CI: 1.12–1.29), were identified as being associated with all-cause 30-day readmission.

Conclusions

In this nationally representative sample of AIS hospitalizations, medication-related adverse events were positively associated with 30-day readmission for non-AIS causes. Future studies are necessary to determine whether medication-related adverse events and readmissions in AIS are avoidable.

Hospital readmissions after spontaneous intracerebral hemorrhage in Southern Portugal

OBJECTIVES

Spontaneous intracerebral hemorrhage (SICH) survivors are at risk of hospital readmissions. Data on readmissions after SICH is scarce. We aimed to study the frequency and predictors of readmissions after SICH in Algarve, Portugal.

PATIENTS AND METHODS

Retrospective study of a community representative cohort of SICH survivors (2009-2015). The first unplanned readmission in the first year after discharge was the outcome. Cox regression analysis was performed to identify predictors of 1-year readmission.

RESULTS

Of the 357 SICH survivors followed, 116 (32.5%) were readmitted within the first-year. Sixty-seven (18.8%) of the survivors were early readmitted (<90 days), corresponding to 57.8% or all readmissions. Common causes were pneumonia, endocrine/nutritional/metabolic and cardiovascular complications. The risk of readmission was increased by prior to index SICH history of ≥ 3 previous emergency department visits (hazards ratio (HR) = 2.663 (1.770-4.007); P < 0.001), pneumonia during index hospitalization (HR = 2.910 (1.844-4.592); P < 0.001) and reduced in patients discharge home (HR = 0.681 (0.366-0.976); P = 0.048).

CONCLUSIONS

The rate of readmissions after SICH is high, predictors are identifiable and causes are potentially preventable. Improvement of care can potentially reduce this burden.

High Mortality among 30-Day Readmission after Stroke: Predictors and Etiologies of Readmission

BACKGROUND

Although some risk factors for stroke readmission have been reported, the mortality risk is unclear. We sought to evaluate etiologies and predictors of 30-day readmissions and determine the associated mortality risk.

METHODS

This is a retrospective case-control study evaluating 1,544 patients admitted for stroke (hemorrhagic, ischemic, or TIA) from January 2013 to December 2014. Of these, 134 patients readmitted within 30 days were identified as cases; 1,418 other patients, with no readmissions were identified as controls. Patients readmitted for hospice or elective surgery were excluded. An additional 248 patients deceased on index admission were included for only a comparison of mortality rates. Factors explored included socio-demographic characteristics, clinical comorbidities, stroke characteristics, and length of stay. Chi-square test of proportions and multivariable logistic regression were used to identify independent predictors of 30-day stroke readmissions. Mortality rates were compared for index admission and readmission and among readmission diagnoses.

RESULTS

Among the 1,544 patients in the main analysis, 67% of index stroke admissions were ischemic, 22% hemorrhagic, and 11% TIA. The 30-day readmission rate was 8.7%. The most common etiologies for readmission were infection (30%), recurrent stroke and TIA (20%), and cardiac complications (14%). Significantly higher proportion of those readmitted for recurrent strokes and TIAs presented within the first week (p = 0.039) and had a shorter index admission length of stay (p = 0.027). Risk factors for 30-day readmission included age >75 (p = 0.02), living in a facility prior to index stroke (p = 0.01), history of prior stroke (p = 0.03), diabetes (p = 0.03), chronic heart failure (p ≤ 0.001), atrial fibrillation (p = 0.03), index admission to non-neurology service (p < 0.01), and discharge to other than home (p < 0.01). On multivariate analysis, index admission to a non-neurology service was an independent predictor of 30-day readmission (p ≤ 0.01). The mortality after a within 30-day readmission after stroke was higher than index admission (36.6 vs. 13.8% p ≤ 0.001) (OR 3.6 95% CI 2.5-5.3). Among those readmitted, mortality was significantly higher for those admitted for a recurrent stroke (p = 0.006).

CONCLUSION

Approximately one-third of 30-day readmissions were infection related and one-fifth returned with recurrent stroke or TIA. Index admission to non-neurology service was an independent risk factor of 30-day readmissions. The mortality rate for 30-day readmission after stroke is more than 2.5 times greater than index admissions and highest among those readmitted for recurrent stroke. Identifying high-risk patients for readmission, ensuring appropriate level of service, and early outpatient follow-up may help reduce 30-day readmission and the high associated risk of mortality.

Advanced Age and Post-Acute Care Outcomes After Subarachnoid Hemorrhage

Background

Older patients with aneurysmal subarachnoid hemorrhage (aSAH) are unique, and determinants of post–acute care outcomes are not well elucidated. The primary objective was to identify hospital characteristics associated with 30‐day readmission and mortality rates after hospital discharge among older patients with aSAH.

Methods and Results

This cohort study used Medicare patients ≥65 years discharged from US hospitals from January 1, 2008, to November 30, 2010, after aSAH. Medicare data were linked to American Hospital Association data to describe characteristics of hospitals treating these patients. Using multivariable logistic regression to adjust for patient characteristics, hospital factors associated with (1) hospital readmission and (2) mortality within 30 days after discharge were identified. A total of 5515 patients ≥65 years underwent surgical repair for aSAH in 431 hospitals. Readmission rate was 17%, and 8.5% of patients died within 30 days of discharge. In multivariable analyses, patients treated in hospitals with lower annualized aSAH volumes were more likely to be readmitted 30 days after discharge (lowest versus highest quintile, 1–2 versus 16–30 cases; adjusted odds ratio, 2.10; 95% confidence interval, 1.56–2.84). Patients treated in hospitals with lower annualized aSAH volumes (lowest versus highest quintile: adjusted odds ratio, 1.52; 95% confidence interval, 1.05–2.19) had a greater likelihood of dying 30 days after discharge.

Conclusions

Older patients with aSAH discharged from hospitals treating lower volumes of such cases are at greater risk of readmission and dying within 30 days. These findings may guide clinician referrals, practice guidelines, and regulatory policies influencing which hospitals should care for older patients with aSAH.

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.

Systematic Review of Hospital Readmissions in Stroke Patients

Background. Previous evidence on factors and causes of readmissions associated with high-impact users of stroke is scanty. The aim of the study was to investigate common causes and pattern of short- and long-term readmissions stroke patients by conducting a systematic review of studies using hospital administrative data. Common risk factors associated with the change of readmission rate were also examined. Methods. The literature search was conducted from 15 February to 15 March 2016 using various databases, such as Medline, Embase, and Web of Science. Results. There were a total of 24 studies (n = 2,126,617) included in the review. Only 4 studies assessed causes of readmissions in stroke patients with the follow-up duration from 30 days to 5 years. Common causes of readmissions in majority of the studies were recurrent stroke, infections, and cardiac conditions. Common patient-related risk factors associated with increased readmission rate were age and history of coronary heart disease, heart failure, renal disease, respiratory disease, peripheral arterial disease, and diabetes. Among stroke-related factors, length of stay of index stroke admission was associated with increased readmission rate, followed by bowel incontinence, feeding tube, and urinary catheter. Conclusion. Although risk factors and common causes of readmission were identified, none of the previous studies investigated causes and their sequence of readmissions among high-impact stroke users.

Short- and longer-term health-care resource utilization and costs associated with acute ischemic stroke

Objectives

The mean lifetime cost of ischemic stroke is approximately $140,048 in the United States, placing stroke among the top 10 most costly conditions among Medicare beneficiaries. The objective of this study was to describe the health-care resource utilization and costs in the year following hospitalization for acute ischemic stroke (AIS).

Methods

This retrospective claims analysis quantifies utilization and costs following inpatient admission for AIS among the commercially insured and Medicare beneficiaries in the Truven Health databases. Patients who were 18 years or older and continuously enrolled for 12 months before and after an AIS event occurring (index) between January 2009 and December 2012 were identified. Patients with AIS in the year preindex were excluded. Demographic and clinical characteristics were evaluated at admission and in the preindex, respectively. Direct costs, readmissions, and inpatient length of stay (LOS) were described in the year postindex.

Results

The eligible populations comprised 20,314 commercially insured patients and 31,037 Medicare beneficiaries. Average all-cause costs were $61,354 and $44,929 (commercial and Medicare, respectively) in the first year after the AIS. Approximately 50%–55% of total 12-month costs were incurred between day 31 and day 365 following the incident AIS. One quarter (24.6%) of commercially insured patients and 38.8% of Medicare beneficiaries were readmitted within 30 days with 16.6% and 71.7% (commercial and Medicare, respectively) of those having a principal diagnosis of AIS. The average AIS-related readmission length of stay was nearly three times that of the initial hospitalization for both commercially insured patients (3.8 vs 10.8 days) and Medicare beneficiaries (4.0 vs 10.8 days).

Conclusion

In addition to the substantial costs of the initial hospitalization of an AIS, these costs double within the year following this event. Given the high cost associated with AIS, new interventions reducing either the acute or longer-term burden of AIS are needed.