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

Females with diabetes have a higher risk of ischemic stroke readmission: a retrospective cohort study

BACKGROUND

There are significant sex differences in the incidence of stroke or diabetes mellitus. However, little is known about sex differences in stroke rehospitalization among diabetic patients.

OBJECT

To explore the sex differences in short-term and long-term rehospitalization of ischemic stroke patients with Type 2 diabetes mellitus.

METHODS

A retrospective cohort study was conducted from 2017 to 2021. The rehospitalization events of ischemic stroke patients with diabetes mellitus were identified by the national unified Electronic Health Record. Propensity score matching was applied to adjust for multiple covariates, and LASSO regression was used to screen for independent variables. Cox proportional hazards model was utilized to analyze the different sex in short-term (90 days, 1 year) and long-term (5 years) rehospitalization in ischemic stroke patients with type 2 diabetes mellitus.

RESULT

A total of 10,724 ischemic stroke patients were included in this study, of whom 5,952 (55.5%) were males. After a 1:1 propensity score matching, there were 3,460 males and 2,772 females. After adjusting for confounding factors, female patients with type 2 diabetes had an increased risk of ischemic stroke rehospitalization at 90 days (HR: 1.94, 95%CI: 1.13-3.33, P < 0.05), 1 year (HR: 1.65, 95%CI:1.22-2.23, P = 0.001), and 5 years (HR: 1.58, 95%CI: 1.26-1.97, P < 0.001). However, there was no significant relationship between male patients with type 2 diabetes and the risk of ischemic stroke rehospitalization, either in the short or long term.

CONCLUSION

Females with type 2 diabetes mellitus have a higher risk of ischemic stroke rehospitalization in both the short-term and long-term.

Community Participation Transition After Stroke (COMPASS) Randomized Controlled Trial: Effect on Adverse Health Events

OBJECTIVE

To compare adverse health events in intervention versus control group participants in the Community Participation Transition After Stroke trial to reduce barriers to independent living for community-dwelling stroke survivors.

DESIGN

Randomized controlled trial.

SETTING

Inpatient rehabilitation (IR) to home and community transition.

PARTICIPANTS

Stroke survivors aged ≥50 years being discharged from IR who had been independent in activities of daily living prestroke (N=183).

INTERVENTIONS

Participants randomized to intervention group (n=85) received home modifications and self-management training from an occupational therapist over 4 visits in the home. Participants randomized to control group (n=98) received the same number of visits consisting of stroke education.

MAIN OUTCOME MEASURES

Death, skilled nursing facility (SNF) admission, 30-day rehospitalization, and fall rates after discharge from IR.

RESULTS

Time-to-event analysis revealed that the intervention reduced SNF admission (cumulative survival, 87.8%; 95% confidence interval [CI], 78.6%-96.6%) and death (cumulative survival, 100%) compared with the control group (SNF cumulative survival, 78.9%; 95% CI, 70.4%-87.4%; P=.039; death cumulative survival, 87.3%; 95% CI, 79.9%-94.7%; P=.001). Thirty-day rehospitalization also appeared to be lower among intervention participants (cumulative survival, 95.1%; 95% CI, 90.5%-99.8%) than among control participants (cumulative survival, 86.3%; 95% CI, 79.4%-93.2%; P=.050) but was not statistically significant. Fall rates did not significantly differ between the intervention group (5.6 falls per 1000 participant-days; 95% CI, 4.7-6.5) and the control group (7.2 falls per 1000 participant-days; 95% CI, 6.2-8.3; incidence rate ratio, 0.78; 95% CI, 0.46-1.33; P=.361).

CONCLUSIONS

A home-based occupational therapist-led intervention that helps stroke survivors transition to home by reducing barriers in the home and improving self-management could decrease the risk of mortality and SNF admission after discharge from rehabilitation.

The effect of 30-day adequate transitions of acute stroke care on 90-day readmission or death

OBJECTIVES

We explore patient-reported behaviors and activities within 30-days post-stroke hospitalization and their role in reducing death or readmissions within 90-days post-stroke.

METHODS

We constructed the adequate transitions of care (ATOC) composite score, measuring patient-reported participation in eligible behaviors and activities (diet modification, weekly exercise, follow-up medical appointment attendance, medication adherence, therapy use, and toxic habit cessation) within 30 days post-stroke hospital discharge. We analyzed ATOC scores in ischemic and intracerebral hemorrhage stroke patients discharged from the hospital to home or rehabilitation facilities and enrolled in the NIH-funded Transitions of Care Stroke Disparities Study (TCSD-S). We utilized Cox regression analysis, with the progressive adjustment for sociodemographic variables, social determinants of health, and stroke risk factors, to determine the associations between ATOC score within 30-days and death or readmission within 90-days post-stroke.

RESULTS

In our sample of 1239 stroke patients (mean age 64 +/- 14, 58 % male, 22 % Hispanic, 22 % Black, 52 % White, 76 % discharged home), 13 % experienced a readmission or death within 90 days (3 deaths, 160 readmissions, 3 readmissions with subsequent death). Seventy percent of participants accomplished a ≥75 % ATOC score. A 25 % increase in ATOC was associated with a respective 20 % (95 % CI 3-33 %) reduced risk of death or readmission within 90-days.

CONCLUSION

ATOC represents modifiable behaviors and activities within 30-days post-stroke that are associated with reduced risk of death or readmission within 90-days post-stroke. The ATOC score should be validated in other populations, but it can serve as a tool for improving transitions of stroke care initiatives and interventions.

Characteristics associated with 30-day post-stroke readmission within an academic urban hospital network

OBJECTIVES

Hospital readmissions are associated with poor health outcomes including illness severity and medical complications. The objective of this study was to identify characteristics associated with 30-day post-stroke readmission in an academic urban hospital network.

MATERIALS AND METHODS

We collected data on patients admitted with stroke from 2017 through 2022 who were readmitted within 30 days of discharge and compared them to a subset of non-readmitted stroke patients. Chart review was used to collect demographics, characteristics of the stroke, co-morbid conditions, in-hospital complications, and post-discharge care. Univariate analyses followed by regression analysis were used to assess characteristics associated with post-stroke readmission.

RESULTS

We identified 4743 patients with stroke (18 % hemorrhagic, mean age 70.1 (standard deviation (SD) 17.2), 47.3 % female) discharged from the stroke services, of whom 282 (5.9 %) patients were readmitted within 30 days of index hospitalization. Univariate analyses identified 18 significantly different features between admitted and readmitted patients. Regression analysis revealed characteristics associated with readmission included private insurance (odds ratio (OR) 0.4, confidence interval (CI) 0.3-0.6, p < 0.001), comorbid peripheral vascular disease (PVD) (OR 2.7, CI 1.3-5.5, p = 0.009), malignancy (OR 1.6, CI 1.0-2.6, p = 0.04), seizure (OR 3.4, CI 1.4-8.2, p = 0.007), thrombolytic administration (OR 0.4, CI 0.2-0.7, p = 0.003), undergoing thrombectomy (OR 5.4, CI 2.9-10.1, p < 0.001), and higher discharge modified Rankin Scale score (OR 1.2, CI 1.0-1.3, p = 0.047).

CONCLUSIONS

Our data demonstrate that thrombectomy, high discharge Rankin score, comorbid malignancy, seizure or PVD, and lack of thrombolytic administration or private insurance predict readmission.

Timing of stroke survivors' hospital readmissions to guide APRNs in primary care

BACKGROUND

Caring for patients after a neurovascular incident is common for advanced practice registered nurses (APRNs). Most neurological readmission studies focus on a small subset of neurovascular incident groups, but advanced practice nurses in primary care attend to a diverse neurovascular population and lack time to adequately search hospital records.

PURPOSE

The aim of this study was to determine readmission risk factors after a neurovascular incident to guide APRNs in the primary care setting.

METHODOLOGY

The study is a retrospective observational study that used a crude single predictor model to determine potential risks for readmission.

RESULTS

A total of 876 neurovascular participants were studied. Of these, only 317 experienced at least one hospital readmission, with 703 readmissions within 1 year, indicating some were readmitted more than once. Risks for readmission varied across neurovascular events. The main reasons for readmission were because of neurological, cardiovascular, and musculoskeletal complications.

CONCLUSIONS

Stroke readmission rates are high and require intervention by APRNs. To prevent readmission includes timely follow-up within 30 days and should also include longitudinal follow-up beyond 90 days to prevent hospital readmission.

IMPLICATIONS

Future studies are needed to create guidelines for APRNs that implement rehabilitation strategies to decrease hospital readmission for the neurovascular population that focus on interdisciplinary communication.

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.

Home-based care of stroke patients in rural Zimbabwe: Knowledge of caregivers

OBJECTIVES

To describe and explore the knowledge and practices of primary caregivers and information they get from village health workers on infection prevention and control among home-based stroke patients.

MATERIALS AND METHODS

A descriptive and exploratory study was conducted on 200 primary caregivers and 200 village health workers selected using multistage random sampling method. Data were collected using questionnaires. Visual Basic for Applications package analysed the data and analysis of variance examined differences between demographic characteristics of participants. Chi-square test was used to determine whether the socio-demographic information and adequacy of information given were associated. Statistical significance was set at p < 0.05.

RESULTS

Primary caregivers were not well informed of the measures to prevent chest infections and urinary tract infections as they rated themselves poor or very poor in practising these measures. There was a correlation between knowledge of prevention and control of infection to primary caregivers' level of education (chi-square=7.49; p=0.024), and residence (chi-square=72.33; p=0.001). There was an association between information given by village health workers on rated adequacy of information and information given on: chest infections (chi-square=20.65; p < 0.0005), skin infections (chi-square=13.42; p=0.009) and urinary tract infections (chi-square=19.20; p=0.001). The information given by village health workers to primary caregivers was also associated with residence (chi-square=107.15; p < 0.0005).

CONCLUSION

Overall, primary caregivers had limited knowledge of infections in home-based stroke patients while the village health caregivers seldom gave them the necessary information. With the necessary training home-based care of stroke patient in Zimbabwe may improve.

Cumulative rehospitalizations and implications for subsequent mortality after first-ever ischemic stroke

INTRODUCTION

Clinical implications of readmission following initial hospitalization for acute ischemic stroke (AIS) are not known. We examined predictors of readmissions and impact of readmissions on subsequent mortality after first-ever AIS.

MATERIALS AND METHODS

Adults aged ≥18 years who survived to discharge after hospitalization for first-ever AIS from 2003 to 2019 were included in the study. For each patient, the overall burden of hospitalizations was measured as total number of hospitalizations and aggregate days spent hospitalized during follow-up. We used Poisson regression to estimate incident rate ratios (IRR) for predictors of re-hospitalization and time-dependent Cox regression to estimate hazard ratios (HR) for mortality.

RESULTS

Of 908 AIS survivors, 537 died, 669 had 2,645 readmissions over 4,535 person-years follow-up. Adjusted independent predictors of cumulative readmission inlcuded being white (IRR 1.21, 95% CI 1.03-1.42), dependency on discharge (IRR 1.27, 95% CI 1.17-1.38), cardio-embolism (IRR 1.35, 95% CI 1.18-1.45), smoking (IRR 1.21, 95% CI 1.08-1.35), anemia (IRR 1.40, 95% CI 1.24-1.57), arthritis (IRR 1.20, 95% CI 1.10-1.31), coronary artery disease (IRR 1.34, 95% CI 1.23-1.47), cancer (IRR 1.96, 95% CI 1.64-2.30), chronic kidney disease (IRR 1.36, 95% CI 1.21-1.57), COPD (IRR 1.18, 95% CI 1.04-1.34), depression (IRR 1.50, 95% CI 1.37-1.66), diabetes mellitus (IRR 1.48, 95% CI 1.36-1.48), and heart failure (IRR 1.17, 95% CI 1.03-1.34). Conversely, hyperlipidemia was associated with a lower risk of readmission (IRR 0.79, 95% CI 0.71-0.88). Mortality was significantly increased with each hospitalization and cumulative days spent in hospital.

CONCLUSIONS

Among survivors of AIS hospitalization, certain sociodemographic indicators, stroke-specific features, and several key comorbid conditions were associated with increased risk of readmissions, which in turn correlated with increased mortality. Therefore, lifestyle modification and optimal treatment of comorbidities are likely to improve the outcome after AIS.

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.

Spatial distribution of stroke readmission within 30 days and the influencing factors in Hunan Province

OBJECTIVES

Stroke readmission increases financial burden on the family and the consumption of medical resources, and 30-day readmission rate is an important indicator for quality evaluation on health services. The influential factors for readmission mainly include patient-related factors, hospital factors, and society-related factors, with regional differences. This study aims to explore the spatial distribution and its main relevant factors for 30-day readmission of stroke patients in Hunan Province, and to provide the useful information for the improvement of regional prevention and control of stroke readmission.

METHODS

Stroke patients in Hunan Province who were hospitalized in 2018 and readmitted within 30 days were included in the study. The vector map of the county boundary in Hunan Province was used as the basic map since county was the spatial analysis unit. SPSS 26.0 and ArcGIS 10.8 were used for statistical analysis that contains descriptive analysis of the general situation and the distribution map of readmission rate within 30 days of stroke patients. Spatial autocorrelation analysis and spatial regression analysis were further used to find the spatial clusters of the 30-day readmission rate of stroke and the local relationship between the readmission rate and main influential factors.

RESULTS

In 2018, a total of 172 800 stroke patients were hospitalized in Hunan Province, of which 6 953 patients were re-hospitalized within 30 days after discharging due to stroke. The 30-day readmission rate was 4.09% in Hunan Province. The clusters of stroke readmission rates were mainly concentrated in the northeast and western regions in Hunan Province. The geographically weighted regression revealed that proportion of patients with complications, number of hospitals per 10 000 population and number of primary medical and health care institution per 10 000 population were the main relevant factors for stroke readmission, and there were differences both in the direction and degree of the effect on readmission in different regions.

CONCLUSIONS

The 30-day readmission rate for stroke patients in Hunan province and its main influential factors had spatial heterogeneity. The key prevention and control areas were mainly concentrated in the northeast and western regions. It is recommended that the prevention and treatment of stroke complications and the construction of medical institutions need to be strengthened to improve the quality of medical services, particularly in the western region. The importance to the treatment of stroke complications should be attached in the northern region, and the primary health care should be reinforced in the northeast region. All counties should take prevention and control measures according to local conditions, so as to effectively control the readmission rate of stroke within 30 days.

Impact of NP follow-up calls on reducing 30-day readmissions in patients with stroke

ABSTRACT

Hospital readmissions are major contributors to increased healthcare costs and lower quality of life. Despite advanced stroke care, patients who have experienced a stroke require ongoing follow-up care to prevent complications and hospital readmissions. We evaluated the impact of NP follow-up calls, providing another level of expertise to promptly identify new symptoms and complications, on readmission rates in patients who have experienced stroke.

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.

Real-world outcomes associated with the use of the EmboTrap revascularization device for ischemic stroke in the United States

BACKGROUND

Mechanical thrombectomy (MT) has become the standard of care for the treatment of acute ischemic stroke (AIS). The EmboTrap revascularization device (CERENOVUS, Johnson & Johnson Medical Devices, Irvine, California, USA) has an innovative, dual layer feature designed to facilitate thrombus retrieval.

OBJECTIVE

To investigate the real-world clinical and economic outcomes among patients with AIS undergoing MT using the EmboTrap device in the United States (US).

METHODS

Adult patients (≥18 years) who underwent MT for AIS using the EmboTrap device between July 2018 and December 2020 were identified from the Premier Healthcare Database. Patient outcomes included discharge status (including in-hospital mortality), mean length of stay (LOS), intracranial hemorrhage (ICH), mean hospital costs, and 30-day readmissions (all-cause, cardiovascular (CV)-related, and AIS-related).

RESULTS

A total of 318 patients (mean age 68.5±14.6 years) with AIS treated with the EmboTrap device as the only stent retriever used were identified. Approximately 25% of patients were discharged to home/home health organization, and the in-hospital mortality rate was 10.7%. The rate of ICH was 16.7%. Mean hospital LOS was 9.9±11.3 days, and the mean hospital costs were US$47 367±30 297. The 30-day readmission rate was 9.6% for all-causes, 5.9% for CV-related causes, and 2.6% for AIS-related causes.

CONCLUSIONS

This is the first study in the US to report real-world outcomes sourced by retrospective database analysis among patients with AIS undergoing MT using the EmboTrap device. Further research is needed to better understand performance of the EmboTrap device in real-world settings.

Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation

After short-term, acute-care hospitalization for stroke, patients may be discharged home or other facilities for continued medical or rehabilitative management. The site of postacute care affects overall mortality and functional outcomes. Determining discharge disposition is a complex decision by the healthcare team. Early prediction of discharge destination can optimize poststroke care and improve outcomes. Previous attempts to predict discharge disposition outcome after stroke have limited clinical validations. In this study, readmission status was used as a measure of the clinical significance and effectiveness of a discharge disposition prediction. Low readmission rates indicate proper and thorough care with appropriate discharge disposition. We used Medicare beneficiary data taken from a subset of base claims in the years of 2014 and 2015 in our analyses. A predictive tool was created to determine discharge disposition based on risk scores derived from the coefficients of multivariable logistic regression related to an adjusted odds ratio. The top five risk scores were admission from a skilled nursing facility, acute heart attack, intracerebral hemorrhage, admission from "other" source, and an age of 75 or older. Validation of the predictive tool was accomplished using the readmission rates. A 75% probability for facility discharge corresponded with a risk score of greater than 9. The prediction was then compared to actual discharge disposition. Each cohort was further analyzed to determine how many readmissions occurred in each group. Of the actual home discharges, 95.7% were predicted to be there. However, only 47.8% of predictions for home discharge were actually discharged home. Predicted discharge to facility had 15.9% match to the actual facility discharge. The scenario of actual discharge home and predicted discharge to facility showed that 186 patients were readmitted. Following the algorithm in this scenario would have recommended continued medical management of these patients, potentially preventing these readmissions.

Healthcare Utilization for Stroke Patients at the End of Life: Nationally Representative Data

Objectives Stroke and post-stroke complications are associated with high morbidity, mortality, and cost. Our objective was to examine healthcare utilization and hospice enrollment for stroke patients at the end of life. Materials and methods The 2014 Nationwide Readmissions Database is a national database of > 14 million admissions. We used validated ICD-9 codes to identify fatal ischemic stroke, summarized demographics and hospitalization characteristics, and examined healthcare use within 30 days before fatal stroke admission. We used de-identified 2014 Medicare hospice data to identify stroke and non-stroke patients admitted to hospice. Results Among IS admissions in 2014 (n = 472,969), 22652 (4.8%) had in-hospital death. 28.2% with fatal IS had two or more hospitalizations in 2014. Among those with fatal IS admission, 13.0% were admitted with cerebrovascular disease within 30 days of fatal IS admission. Half of stroke patients discharged to hospice from the Medicare dataset were hospitalized with cerebrovascular disease within the thirty days prior to hospice enrollment. Within the study year, 6.9% of hospice enrollees had one or more emergency room visits, 31.7% had one or more inpatient encounters, and 5.2% had one or more nursing facility encounters (compared to 21.4%, 70.6%, and 27.2% respectively in the 30-day period prior to enrollment). Conclusions High rates of readmission prior to fatal stroke may indicate opportunity for improvement in acute stroke management, secondary prevention, and palliative care involvement as encouraged by AHA/ASA guidelines. For patients who are expected to survive 6 months or less, hospice may offer goal-concordant services for patients and caregivers who desire comfort-focused care.

Sex Differences in Stroke Hospitalization Incidence, 30-Day Mortality, and Readmission in a Regional Medical Center in the Southwestern United States

OBJECTIVES

This study explores sex differences in ischemic stroke hospitalization incidence, 30-day mortality, and 30-day readmission in a southwestern US medical center.

METHODS

Ischemic stroke admissions in a regional medical center in the southwestern United States were obtained for a 6.5-year time frame (N = 1968). Logistic regression models examine the adjusted effects of sex on 30-day mortality and 30-day readmission outcomes among individuals hospitalized for ischemic stroke.

RESULTS

Findings confirm that although women experience higher mortality than men (9.1% vs 6.7%), the sex disparity in mortality is explained by the age distribution of strokes. Women experience far more strokes and deaths because of stroke at older ages. No differences in principal procedure or 30-day readmission emerged.

CONCLUSIONS

Men experienced higher stroke hospitalization incidence, although women exhibited higher 30-day mortality. Age composition explained sex differences in mortality, but higher male stroke hospitalization incidence represents a larger public health issue that suggests the need for behavioral change at the population level. No meaningful sex differences emerged in treatment, mortality, or readmission.

The outcome in patients with brain stroke: A deep learning neural network modeling

Background:

The artificial intelligence field is obtaining ever-increasing interests for enhancing the accuracy of diagnosis and the quality of patient care. Deep learning neural network (DLNN) approach was considered in patients with brain stroke (BS) to predict and classify the outcome by the risk factors.

Materials and Methods:

A total of 332 patients with BS (mean age: 77.4 [standard deviation: 10.4] years, 50.6% – male) from Imam Khomeini Hospital, Ardabil, Iran, during 2008–2018 participated in this prospective study. Data were gathered from the available documents of the BS registry. Furthermore, the diagnosis of BS was considered based on computerized tomography scans and magnetic resonance imaging. The DLNN strategy was applied to predict the effects of the main risk factors on mortality. The quality of the model was measured by diagnostic indices.

Results:

The finding of this study for 81 selected models demonstrated that ranges of accuracy, sensitivity, and specificity are 90.5%–99.7%, 83.8%–100%, and 89.8%–99.5%, respectively. Based on the optimal model (tangent hyperbolic activation function with the minimum–maximum hidden units of 10–20, max epochs of 400, momentum of 0.5, and learning rate of 0.1), the most important predictors for BS mortality were time interval after 10 years (accuracy = 92.2%), age category (75.6%), the history of hyperlipoproteinemia (66.9%), and education level (66.9%). The other independent variables are at moderate importance (66.6%) which include sex, employment status, residential place, smoking habits, history of heart disease, cerebrovascular accident type, blood pressure, diabetes, oral contraceptive pill use, and physical activity.

Conclusion:

The best means for dropping the BS load is effective BS prevention. DLNN strategy showed a surprising presentation in the prediction of BS mortality based on the main risk factors with an excellent diagnostic accuracy. Moreover, the time interval after 10 years, age, the history of hyperlipoproteinemia, and education level are the most important predictors for BS.

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.

Thirty-Day Hospital Readmissions: A Predictor of Higher All-cause Mortality for Up to Two Years

Introduction

Readmission within 30 days is used as a standard quality metric for hospitalized patients. We hypothesized that patients who get readmitted within 30 days may have higher short- and long-term mortality.

Material and Methods

Using administrative data, we retrospectively analyzed 2,353 patients admitted to inpatient medicine service over a period of one year. The patients were matched for diagnostic group (DRG) and severity index (SI) using nearest propensity scores in a 2:1 ratio between non-readmissions (NRA) to readmissions (RA) patients.

Results

There was no statistically significant difference in the groups between age, sex, length of stay (LOS), race, and ethnicity. The hazard model yielded a hazard ratio (HR) of 2.06 for 30-day readmissions (95% CI of 1.55, 2.74; p=<0.001). The survival probability at 6, 12, 18, and 24 months was consistently greater for NRA patients. 

Conclusions

Thirty-day readmissions are an independent risk factor for all-cause mortality which persists for at least two years independent of DRG and SI.

A quantitative analysis of the effect of continuity of care on 30-day readmission and in-hospital mortality among patients with acute ischemic stroke

BACKGROUND

Continuity of care is a core element of high-quality patient care in a primary care setting and one of a national priority.

OBJECTIVE

To assess and quantify the impact of continuity of care on 30-day readmissions, 30-day inpatient mortality, and hospital length of stay (LOS), among hospitalized patients with acute ischemic stroke disease.

DESIGN AND SUBJECTS

Observational retrospective cohort (n = 356,134) using a 2.75% random sample (n=1,036,753) from the State of Florida Agency for Health Care Administration (AHCA) database from 2006 to 2016.

MEASURES

We assessed continuity of care using an integrated continuity of care CoC score, calculated by merging three standard indices of continuity of care - Bice-Boxerman Continuity of Care Index (COCI), Herfindahl Index (HI), and Usual Provider of Care (UPC) Index via a Principal Component Analysis (PCA). We measured 30-day hospital readmissions, 30-day inpatient mortality, and LOS.

RESULTS

Our analysis revealed that hospital LOS was significantly affected by CoC. The statistically significant average treatment effect (ATEs), expressed in risk difference (RD), ranged between 0.27 [95%CI: (0.07, 0.48)] and 1.0 day [95%CI: (0.57, 1.43)]. A similar trend was observed for 30-day readmission (ATEs ranging from 0.0067 [95%CI: (0.0002, 0.0132) to 0.0071 [95%CI: (0.0005, 0.0136)]), and inpatient mortality (ATEs ranging from 0.0006 [95% confidence interval (CI): (0.0001, 0.0012)] to 0.0007 [95%CI: (0.0001, 0.0012)]).

CONCLUSIONS

Our findings suggest a strong association between continuity of care and clinical outcomes. Continuity of care leads to a reduction in mortality, rehospitalization, and hospital length of stay.

Annual incidences and predictors of 30-day readmissions following spontaneous intracerebral hemorrhage from 2010 to 2014 in the United States: A retrospective Nationwide analysis

Objective

30-day readmission rate is a quality metric often employed to represent hospital and provider performance. Currently, little is known regarding 30-day readmissions (30dRA) following spontaneous intracerebral hemorrhage (sICH). The purpose of this study was to use a national database to identify risk factors and trends in 30dRAs following sICH.

Patients and methods

64,909 cases with a primary diagnosis of sICH were identified within the Nationwide Readmission Database (NRD) from 2010 through 2014. Charlson Comorbidity Index (CCI) was used to adjust for the severity of each patient's comorbidities. A binary logistic regression model was constructed to identify predictors of 30-day readmission. Cochran-Mantel-Haenszel test was used to generate a pooled odd ratio (OR) describing the likelihood of experiencing a 30dRA according to year.

Results

The 30dRA rate following sICH decreased from 13.9% in 2010 to 12.5% in 2014 (pooled OR = 0.90, 95% CI 0.87–0.94). Cerebrovascular and cardiovascular etiologies accounted for the greatest number of admissions (36.1%). Sodium abnormality, healthcare-associated infection, gastrostomy, venous thromboembolism, and ischemic stroke during the index admission were associated with 30-day readmission. Furthermore, patients who underwent ventriculostomy (OR = 1.20, 95% CI 1.03–1.38) and craniotomy (OR = 1.20, 95% CI 1.09–1.31) were more likely to be readmitted within 30 days. Hospital volume, hospital teaching status, mechanical ventilation, and tracheostomy did not affect 30dRAs. Median readmission costs increased from $9,875 in 2012 to $11,028 in 2014 (p = 0.040).

Conclusion

The overall U.S. 30dRA rate after sICH from 2010 to 2014 was 12.9% and decreased slightly during this time period, but associated costs increased. Prospective studies are required to confirm the risk factors described in this study and to identify methods for preventing readmissions.

Readmission to a different hospital following acute stroke is associated with worse outcomes

OBJECTIVE

There is a high risk of readmission within 30 days of index acute ischemic stroke (AIS), but effect of readmission to a different hospital is not known. We performed a retrospective cohort study to assess our hypothesis that 30-day readmission outcomes after AIS are worse for those readmitted to another hospital vs the discharging hospital.

METHODS

We utilized the 2013 Nationwide Readmissions Database to identify patients with index stroke admissions with ICD-9-CM codes. We identified all-cause readmissions with Clinical Classification Software. Outcomes included length of stay (LOS), total charges of hospitalization, and in-hospital mortality during 30-day readmission. Using linear and logistic regression, outcomes were compared in those readmitted to another hospital vs the discharging hospital.

RESULTS

There were 194,549 patients included, with an average age of 80.0 ± 14.0 years; 51.2% were female; 24,545 were readmitted within 30 days, and 7,274 (29.6%) to a different hospital. Readmission to a different hospital was associated with an increased LOS of 1.0 days (95% confidence interval [CI] 0.7-1.2, p < 0.0001) and $7,677.28 (95% CI $5,496-$9,858, p < 0.0001) greater total charges. The odds ratio for in-hospital mortality during readmission was 1.2 for readmission to another hospital (95% CI 1.0-1.3, p = 0.0079).

CONCLUSIONS

Readmission to another hospital within 30 days of AIS index admission was independently associated with longer LOS, increased total charges, and greater in-hospital mortality compared to readmission to the same hospital.

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.

Structural Equation Model (SEM) of Stroke Mortality in Spanish Inpatient Hospital Settings: The Role of Individual and Contextual Factors

Introduction: Traditionally, predictive models of in-hospital mortality in ischemic stroke have focused on individual patient variables, to the neglect of in-hospital contextual variables. In addition, frequently used scores are betters predictors of risk of sequelae than mortality, and, to date, the use of structural equations in elaborating such measures has only been anecdotal. Aims: The aim of this paper was to analyze the joint predictive weight of the following: (1) individual factors (age, gender, obesity, and epilepsy) on the mediating factors (arrhythmias, dyslipidemia, hypertension), and ultimately death (exitus); (2) contextual in-hospital factors (year and existence of a stroke unit) on the mediating factors (number of diagnoses, procedures and length of stay, and re-admission), as determinants of death; and (3) certain factors in predicting others. Material and Methods: Retrospective cohort study through observational analysis of all hospital stays of Diagnosis Related Group (DRG) 14, non-lysed ischemic stroke, during the time period 2008-2012. The sample consisted of a total of 186,245 hospital stays, taken from the Minimum Basic Data Set (MBDS) upon discharge from Spanish hospitals. MANOVAs were carried out to establish the linear effect of certain variables on others. These formed the basis for building the Structural Equation Model (SEM), with the corresponding parameters and restrictive indicators. Results: A consistent model of causal predictive relationships between the postulated variables was obtained. One of the most interesting effects was the predictive value of contextual variables on individual variables, especially the indirect effect of the existence of stroke units on reducing number of procedures, readmission and in-hospital mortality. Conclusion: Contextual variables, and specifically the availability of stroke units, made a positive impact on individual variables that affect prognosis and mortality in ischemic stroke. Moreover, it is feasible to determine this impact through the use of structural equation methodology. We analyze the methodological and clinical implications of this type of study for hospital policies.

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.