Preventable readmissions within 30 days of ischemic stroke among Medicare beneficiaries

Racial/Ethnic Differences in Association Between Medicaid Expansion and Causes and Costs of Readmission After Acute Ischemic Stroke

OBJECTIVE

The purpose of this study was to examine whether the relative frequency of leading causes and total associated costs of readmission after acute ischemic stroke changed with Medicaid expansion, and how these changes differed by racial/ethnic group.

METHODS

We used a difference-in-differences approach to compare changes in the relative frequency of leading causes of unplanned 30-day readmission and to examine changes in the costs associated with unplanned readmission between expansion states (AR, MD, NM, and WA) and non-expansion states (FL and GA). To estimate the differential effect of Medicaid expansion by race/ethnicity on the causes and cost of readmission, we added a time*treatment*race interaction. Multinomial logistic regression was performed to analyze the changes in readmission cause. Gamma log-link modeling was used to study changes in readmission costs for expansion compared to non-expansion states.

RESULTS

The final multinomial model showed an association between expanded Medicaid and the relative frequency of sepsis readmission for White patients. According to predictive margins, White patients in expansion states had an estimated increase of 3.3 percentage points in the share of readmissions for sepsis but not for White patients in non-expansion states. In contrast, non-White patients in expansion states had a decrease of 1.8 percentage points in the share of readmissions for sepsis. Overall, Medicaid expansion was associated with a net increase of 6.7 percentage points in the share of readmissions for sepsis among non-Hispanic Whites relative to all other groups. In the final gamma model, Medicaid expansion was associated with a decrease in readmission costs overall. According to predictive margins, the net cost reduction in expansion versus non-expansion states was an average of $2509.

CONCLUSIONS

Medicaid expansion is associated with an overall decrease in unplanned readmission costs and an increase among readmitted White patients in the likelihood of readmission for sepsis.

Causes and Predictors of 30-Day Readmission in Patients With Stroke Undergoing Mechanical Thrombectomy: A Large Single-Center Experience

BACKGROUND AND OBJECTIVES

The 30-day readmission rate has emerged as a metric of quality care and is associated with increased health care expenditure. We aim to identify the rate and causes of 30-day readmission after mechanical thrombectomy and provide the risk factors of readmission to highlight high-risk patients who may require closer care.

METHODS

This is a retrospective study from a prospectively maintained database of 703 patients presenting for mechanical thrombectomy between 2017 and 2023. All patients who presented with a stroke and underwent a mechanical thrombectomy were included in this study. Patients who were deceased on discharge were excluded from this study.

RESULTS

Our study comprised 703 patients, mostly female (n = 402, 57.2%) with a mean age of 70.2 years ±15.4. The most common causes of readmission were cerebrovascular events (stroke [n = 21, 36.2%], intracranial hemorrhage [n = 9, 15.5%], and transient ischemic attack [n = 1, 1.7%]).Other causes of readmission included cardiovascular events (cardiac arrest [n = 4, 6.9%] and bradycardia [n = 1, 1.7%]), infection (wound infection postcraniectomy [n = 3, 5.2%], and pneumonia [n = 1, 1.7%]). On multivariate analysis, independent predictors of 30-day readmission were history of smoking (odds ratio [OR]: 2.2, 95% CI: 1.1-4.2) P = .01), distal embolization (OR: 3.2, 95% CI: 1.1-8.7, P = .03), decompressive hemicraniectomy (OR: 9.3, 95% CI: 3.2-27.6, P < .01), and intracranial stent placement (OR: 4.6, 95% CI: 2.4-8.7) P < .01).

CONCLUSION

In our study, the rate of 30-day readmission was 8.3%, and the most common cause of readmission was recurrent strokes. We identified a history of smoking, distal embolization, decompressive hemicraniectomy, and intracranial stenting as independent predictors of 30-day readmission in patients with stroke undergoing mechanical thrombectomy.

Clinical outcome prediction using observational supervision with electronic health records and audit logs

OBJECTIVE

Audit logs in electronic health record (EHR) systems capture interactions of providers with clinical data. We determine if machine learning (ML) models trained using audit logs in conjunction with clinical data ("observational supervision") outperform ML models trained using clinical data alone in clinical outcome prediction tasks, and whether they are more robust to temporal distribution shifts in the data.

MATERIALS AND METHODS

Using clinical and audit log data from Stanford Healthcare, we trained and evaluated various ML models including logistic regression, support vector machine (SVM) classifiers, neural networks, random forests, and gradient boosted machines (GBMs) on clinical EHR data, with and without audit logs for two clinical outcome prediction tasks: major adverse kidney events within 120 days of ICU admission (MAKE-120) in acute kidney injury (AKI) patients and 30-day readmission in acute stroke patients. We further tested the best performing models using patient data acquired during different time-intervals to evaluate the impact of temporal distribution shifts on model performance.

RESULTS

Performance generally improved for all models when trained with clinical EHR data and audit log data compared with those trained with only clinical EHR data, with GBMs tending to have the overall best performance. GBMs trained with clinical EHR data and audit logs outperformed GBMs trained without audit logs in both clinical outcome prediction tasks: AUROC 0.88 (95% CI: 0.85-0.91) vs. 0.79 (95% CI: 0.77-0.81), respectively, for MAKE-120 prediction in AKI patients, and AUROC 0.74 (95% CI: 0.71-0.77) vs. 0.63 (95% CI: 0.62-0.64), respectively, for 30-day readmission prediction in acute stroke patients. The performance of GBM models trained using audit log and clinical data degraded less in later time-intervals than models trained using only clinical data.

CONCLUSION

Observational supervision with audit logs improved the performance of ML models trained to predict important clinical outcomes in patients with AKI and acute stroke, and improved robustness to temporal distribution shifts.

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.

Associations Between Long-Term Air Pollutant Exposure and 30-Day All-Cause Hospital Readmissions in US Patients With Stroke

BACKGROUND

Long-term exposure to air pollutants is associated with increased stroke incidence, morbidity, and mortality; however, research on the association of pollutant exposure with poststroke hospital readmissions is lacking.

METHODS

We assessed associations between average annual carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter 2.5, and sulfur dioxide (SO2) exposure and 30-day all-cause hospital readmission in US fee-for-service Medicare beneficiaries age ≥65 years hospitalized for ischemic stroke in 2014 to 2015. We fit Cox models to assess 30-day readmissions as a function of these pollutants, adjusted for patient and hospital characteristics and ambient temperature. Analyses were then stratified by treating hospital performance on the Centers for Medicare and Medicaid Services risk-standardized 30-day poststroke all-cause readmission measure to determine if the results were independent of performance: low (Centers for Medicare and Medicaid Services rate for hospital <25th percentile of national rate), high (>75th percentile), and intermediate (all others).

RESULTS

Of 448 148 patients with stroke, 12.5% were readmitted within 30 days. Except for tropospheric NO2 (no national standard), average 2-year CO, O3, particulate matter 2.5, and SO2 values were below national limits. Each one SD increase in average annual CO, NO2, particulate matter 2.5, and SO2 exposure was associated with an adjusted 1.1% (95% CI, 0.4-1.9%), 3.6% (95% CI, 2.9%-4.4%), 1.2% (95% CI, 0.2%-2.3%), and 2.0% (95% CI, 1.1%-3.0%) increased risk of 30-day readmission, respectively, and O3 with a 0.7% (95% CI, 0.0%-1.5%) decrease. Associations between long-term air pollutant exposure and increased readmissions persisted across hospital performance categories.

CONCLUSIONS

Long-term air pollutant exposure below national limits was associated with increased 30-day readmissions after stroke, regardless of hospital performance category. Whether air quality improvements lead to reductions in poststroke readmissions requires further research.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Updates in mechanical thrombectomy

Stroke is a leading cause of morbidity and mortality. The advent of mechanical thrombectomy has largely improved patient outcomes. This article reviews the features and outcomes associated with aspiration, stent retrievers, and combination catheters used in current practice. There is also a discussion on clinical considerations based on anatomical features and clot composition. The reperfusion grading scale and outcome metrics commonly used following thrombectomy when a patient is still in the hospital are reviewed. Lastly, there are proposed discharge and outpatient follow-up goals in caring for patients hospitalized for a stroke.

Bounce-Back: Predicting Acute Readmission From Inpatient Rehabilitation for Patients With Stroke

OBJECTIVE

The aim of the study was to identify demographic, medical, and functional risk factors for discharge to an acute hospital before completion of an inpatient rehabilitation program and 7- and 30-day readmissions after completion of an inpatient rehabilitation program.

DESIGN

This cohort study included 138,063 fee-for-service Medicare beneficiaries with a primary diagnosis of new onset stroke discharged from an inpatient rehabilitation facility from June 2009 to December 2011. Multivariate models examined readmission outcomes and included data from 6 mos before onset of the stroke to 30 days after discharge from the inpatient rehabilitation facility.

RESULTS

In the acute discharge model (n = 9870), comorbidities and complications added risk, and the longer the stroke onset to admission to inpatient rehabilitation facility, the more likely discharge to the acute hospital. In the 7-day (n = 4755) and 30-day (n = 9861) readmission models, patients who were more complex with comorbidities, were black, or had managed care Medicare were more likely to have a readmission. Functional status played a role in all three models.

CONCLUSIONS

Results suggest that certain demographic, medical, and functional characteristics are associated differentially with rehospitalization after completion inpatient rehabilitation. The strongest model was the discharge to the acute hospital model with concordance statistic (c-statistic) of 0.87.

A Multi-Component Transition of Care Improvement Project to Reduce Hospital Readmissions Following Ischemic Stroke

Background:

Ischemic stroke (IS) is a common cause of hospitalization which carries a significant economic burden and leads to high rates of death and disability. Readmission in the first 30 days after hospitalization is associated with increased healthcare costs and higher risk of death and disability. Efforts to decrease the number of patients returning to the hospital after IS may improve quality and cost of care.

Methods:

Improving care transitions to reduce readmissions is amenable to quality improvement (QI) initiatives. A multi-component QI intervention directed at IS patients being discharged to home from a stroke unit at an academic comprehensive stroke center using IS diagnosis-driven home care referrals, improved post-discharge telephone calls, and timely completion of discharge summaries was developed. The improvement project was implemented on July 1, 2019, and evaluated for the 6 months following initiation in comparison to the same 6-month period pre-intervention in 2018.

Results:

Following implementation, a statistically significant decrease in 30-day all-cause same-hospital readmission rates from 7.4% to 2.8% ( p = .031, d = 1.61) in the project population and from 6.6% to 3% ( p = .010, d = 1.43) in the overall IS population was observed. Improvement was seen in all process measures as well as in patient satisfaction scores.

Conclusions:

An evidence-based bundled process improvement intervention for IS patients discharged to home was associated with decreased hospital readmission rates following IS.

Potentially preventable hospital readmissions after patients' first stroke in Taiwan

Readmission is an important indicator of the quality of care. The purpose of this study was to explore the probabilities and predictors of 30-day and 1-year potentially preventable hospital readmission (PPR) after a patient's first stroke. We used claims data from the National Health Insurance (NHI) from 2010 to 2018. Multinomial logistic regression was used to assess the predictors of 30-day and 1-year PPR. A total of 41,921 discharged stroke patients was identified. We found that hospital readmission rates were 15.48% within 30-days and 47.25% within 1-year. The PPR and non-PPR were 9.84% (4123) and 5.65% (2367) within 30-days, and 30.65% (12,849) and 16.60% (6959) within 1-year, respectively. The factors of older patients, type of stroke, shorter length of stay, higher Charlson Comorbidity Index (CCI), higher stroke severity index (SSI), regional hospital, public and private hospital, and hospital in the lower urbanized area were associated significantly with the 30-day PPR. In addition, the factors of male, hospitalization year, and monthly income were associated significantly with 1-year PPR. The ORs of long-term PPR showed a decreasing trend since implementing the national health insurance post-acute care (PAC) program in 2014 and a dramatic drop in 2018 after the government expanded the long-term care plan-LTC 2.0 in 2017. The results showed that better discharge planning, implementing post-acute care programs and long-term care plan-LTC 2.0 may benefit the care of stroke patients and help reduce long-term readmission in Taiwan.

Implementing a Neurohospitalist Program Improves Stroke Care Metrics and Patient Satisfaction Scores

Objective

Compare the differences in health outcomes and patient satisfaction between a neurohospitalist model of care and a community-based neurologists model at a single community-based teaching hospital among in-patients diagnosed with a cerebrovascular accident (CVA).

Methods

Data was collected from the Stamford Hospital’s electronic medical records system. An assessment of patient health outcomes and satisfaction scores was conducted, comparing both discrete and continuous variables between the two time periods. An omnibus P-value of 0.05 ( P < 0.05) was considered statistically significant.

Results

The sample consisted of 341 patients between the two periods, pre-period n = 168 (49.3%) post-period n = 173 (50.7%). Door to lab and door to tPA times decreased significantly between pre- and post-periods ( P = 0.003 and P = 0.002, respectively) as did the number of MRIs ( P < 0.001). In addition, statistically significant increases were found between pre-period and post-period percentages, all increasing over time: stroke education ( P < 0.001), discharged on anticoagulant medication ( P < 0.001), and discharged on anti-thrombolytic medication ( P = 0.019). Patient satisfaction scores demonstrated mean gain across both periods for five of six items. Two items “Doctor’s Concern of my Questions/Worries” and “Skill of Doctors” demonstrated statistical significance ( P = 0.020 and P = 0.029, respectively).

Conclusions

The introduction of a neurohospitalist service at a community-based teaching hospital improved patient health outcomes on time to intervention, stroke education, discharge medications as well as patient satisfaction. Therefore, it may be beneficial for hospitals to implement a neurohospitalist model of care for their patients presenting with CVA.

Cardiovascular Risk Factor Profiles, Emergency Department Visits, and Hospitalizations for Women and Men with a History of Stroke or Transient Ischemic Attack: A Cross-Sectional Study

Background: The relationship between cardiovascular disease risk factors (CVD-RFs) and health care utilization may differ by sex. We determined whether having more CVD-RFs was associated with all-cause emergency department (ED) visits and all-cause hospitalizations for women and men with prior stroke/transient ischemic attack (TIA). Materials and Methods: In this cross-sectional study, we used nationally representative Medical Expenditure Panel Survey (2012-2015) data for persons aged ≥18 years with a prior stroke/TIA. CVD-RF summary scores include six self-reported factors (hypertension, diabetes, high cholesterol, physical inactivity, smoking, and obesity). Sex-specific covariate-adjusted logistic regression models assessed associations between CVD-RF scores and having one or more all-cause ED visits and one or more all-cause hospitalizations. Results: The weighted sample represents 9.1 million individuals (mean age 66.6 years; 54.3% women). Prevalence of low (0-1 risk factors), intermediate (2-3), and high (4-6) CVD-RF scores was 19.4%, 60.5%, and 20.1% for women and 14.6%, 60.2%, and 25.2% for men, respectively. Women having intermediate and high scores had a 1.58-fold (95% confidence interval [CI], 1.14-2.18) and 2.21-fold (95% CI, 1.50-3.25) increased odds of ED visits compared with women with low scores. Women with high CVD-RF scores had a 2.18-fold (95% CI, 1.42-3.34) increased odds of hospitalizations, but there was no association for women with intermediate CVD-RF profiles. There was no association between CVD-RF scores and either outcome for men. Conclusions: Women, but not men, with high and intermediate CVD-RF profiles had increased odds of all-cause ED visits; women with high CVD-RF profiles had increased odds of all-cause hospitalizations. The burden of CVD-RFs may be a sex-specific predictor of higher health care utilization in women with a history of stroke/TIA.

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.

Nonadherence in diabetes care among US adults with diabetes by stroke status

Objective

Effects of stroke (i.e., memory loss, paralysis) may make effective diabetes care difficult which can in turn contribute to additional diabetes related complications and hospitalization. However, little is known about US post-stroke diabetes care levels. This study sought to examine diabetes care levels among US adults with diabetes by stroke status.

Methods

Using 2015–2018 Behavioral Risk Factor Surveillance System surveys, the prevalence of nonadherence with the American Diabetes Association’s diabetes care measures (<1 eye exam annually, <1 foot exam annually, <1 blood glucose check daily, <2 A1C tests annually, no receipt of annual flu vaccination) was ascertained in people with diabetes by stroke status. A separate logistic regression model was run for each diabetes care measure to determine if nonadherence patterns differed by stroke status after adjustment for stroke and diabetes associated factors.

Results

Our study included 72,630 individuals, with 9.8% having had a stroke. Nonadherence levels varied for each diabetes care measure ranging from 20.4–42.2% for stroke survivors and 22.8–44.0% for those who had never had stroke. By stroke status, nonadherence with diabetes management measures was comparable except for stroke survivors having both a lower prevalence (30.2% versus 40.1%) and odds of nonadherence (OR: 0.73, 95% CI: 0.65, 0.82) with daily blood glucose check than those who had never had stroke.

Conclusion

While nonadherence with diabetes management does not vary by stroke status, considerable nonadherence still exists among stroke survivors with diabetes. Additional interventions to improve diabetes care may help to reduce risk of further diabetes complications in this population.

Hospital Readmissions and Mortality Among Fee-for-Service Medicare Patients With Minor Stroke or Transient Ischemic Attack: Findings From the COMPASS Cluster-Randomized Pragmatic Trial

Background Mortality and hospital readmission rates may reflect the quality of acute and postacute stroke care. Our aim was to investigate if, compared with usual care (UC), the COMPASS-TC (Comprehensive Post-Acute Stroke Services Transitional Care) intervention (INV) resulted in lower all-cause and stroke-specific readmissions and mortality among patients with minor stroke and transient ischemic attack discharged from 40 diverse North Carolina hospitals from 2016 to 2018. Methods and Results Using Medicare fee-for-service claims linked with COMPASS cluster-randomized trial data, we performed intention-to-treat analyses for 30-day, 90-day, and 1-year unplanned all-cause and stroke-specific readmissions and all-cause mortality between INV and UC groups, with 90-day unplanned all-cause readmissions as the primary outcome. Effect estimates were determined via mixed logistic or Cox proportional hazards regression models adjusted for age, sex, race, stroke severity, stroke diagnosis, and documented history of stroke. The final analysis cohort included 1069 INV and 1193 UC patients (median age 74 years, 80% White, 52% women, 40% with transient ischemic attack) with median length of hospital stay of 2 days. The risk of unplanned all-cause readmission was similar between INV versus UC at 30 (9.9% versus 8.7%) and 90 days (19.9% versus 18.9%), respectively. No significant differences between randomization groups were seen in 1-year all-cause readmissions, stroke-specific readmissions, or mortality. Conclusions In this pragmatic trial of patients with complex minor stroke/transient ischemic attack, there was no difference in the risk of readmission or mortality with COMPASS-TC relative to UC. Our study could not conclusively determine the reason for the lack of effectiveness of the INV. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02588664.

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

OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Emergency department visits and readmissions in patients with psychogenic nonepileptic seizures (PNES) at a safety net hospital

INTRODUCTION

Readmissions and emergency department (ED) visits after an index admission have been become a quality measure due to associations with poor outcomes and increased healthcare costs. Readmissions and ED encounters have been studied in a variety of conditions including epilepsy but have not been examined exclusively in psychogenic nonepileptic seizures (PNES). In this study we examined the rate of readmissions and ED visits after a discharge from an Epilepsy Monitoring Unit (EMU) in a safety net hospital. We also determined patient phenotypes that are associated with readmissions.

MATERIAL AND METHODS

This was a retrospective chart review study with index admission being a discharge from an EMU between January 1 and December 31 2016 with follow-up until August 31 2020. We obtained data regarding demographics, medical and psychiatric history, and social history and treatment interventions. Our outcome variables were both all-cause and seizure-related hospital readmissions and ED visits 30 days following the index discharge and readmissions and ED visits 30 days thereafter.

RESULTS

Eleven of 122 patients (9%) had a non-seizure-related ED visit and/or hospitalization within 30 days of index discharge while 45 (37%) had re-contact with the health system thereafter for non-seizure-related issues. Seven of 122 patients (6%) had a seizure-related ED visit or hospital readmission within 30 days of discharge. Twenty-eight (23%) had a seizure-related readmission or ED visit after 30 days. Of these 28, 4 patients had been to an ER within 7 days of EMU discharge. The majority of subsequent encounters with the healthcare system were through the ED (n = 38) as compared to hospital (n = 10) and EMU readmissions (n = 9). On bivariate statistical analysis, charity or self-pay insurance status (p < 0.01), homelessness (p < 0.01), emergent EMU admission on index admission (p < 0.01), history of a psychiatric diagnosis (p < 0.02), and ED encounters 12 months prior to admission (p < 0.01) were significantly associated with readmission; however, on multivariate analysis only charity insurance status was a significant predictor.

CONCLUSIONS

In this study of readmissions and ED visits after discharge with a diagnosis of PNES at a safety net hospital, we found a seizure-related readmission rate of approximately 6% in 30 days and 23% thereafter with the majority of re-contact with the hospital being in the ED. On multi-variate analysis insurance status was a significant factor associated with readmission and ED visits. Our future research directions include examining referrals and treatment completion at the hospital's PNES clinic as well as creating a risk score to better identify patients with PNES at risk of readmission.

Functional and Cognitive Impairments Increased Risks of Outcomes of Healthcare Utilization in Patients With Stroke Receiving Home and Community-Based Care in Taiwan

Aim: Stroke is a leading cause of disability; however, little is known about the outcomes of the utilization of long-term care (LTC) recipients in Taiwan. This study aimed to quantify the burdens of disease of stroke survivors receiving LTC by evaluating the outcomes of their utilization including mortality, readmissions, and re-emergency within 1 year after diagnoses of strokes. Methods: By interlinkages among the national mortality registry, LTC dataset (LTC-CM), and the National Health Insurance Research Dataset (NHIRD), the outcomes and the factors associated with receiving LTC up to 1 year were explored. Patients were aged 50 years and over with an inpatient claim of the first diagnosis of stroke of intracerebral hemorrhage (ICH) and ischemic stroke during 2011-2016. Outcomes of the healthcare utilization include rehospitalization and re-emergency. Results: There were 15,662 patients with stroke who utilized the LTC services in the dataset among the stroke population in NHIRD. Stroke survivors receiving LTC showed no difference in clinical characteristics and their expected years of life loss (EYLL = 7.4 years) among those encountered in NHIRD. The LTC recipients showed high possibilities to be rehospitalized and resent to emergency service within 1 year after diagnosis. Apart from the comorbidity and stroke severity, both the physical and mental functional disabilities and caregiving resources predicted the outcomes of the utilization. Conclusions: For stroke survivors, both severe functional impairments and cognitive impairments were found as important factors for healthcare utilizations. These results regarding reserving functional abilities deserve our consideration in making the decision on the ongoing LTC policy reform in the aged society of Taiwan.

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.

Thirty-Day Post-Discharge Outcomes Following COVID-19 Infection

BACKGROUND

The clinical course of COVID-19 includes multiple disease phases. Data describing post-hospital discharge outcomes may provide insight into disease course. Studies describing post-hospitalization outcomes of adults following COVID-19 infection are limited to electronic medical record review, which may underestimate the incidence of outcomes.

OBJECTIVE

To determine 30-day post-hospitalization outcomes following COVID-19 infection.

DESIGN

Retrospective cohort study SETTING: Quaternary referral hospital and community hospital in New York City.

PARTICIPANTS

COVID-19 infected patients discharged alive from the emergency department (ED) or hospital between March 3 and May 15, 2020.

MEASUREMENT

Outcomes included return to an ED, re-hospitalization, and mortality within 30 days of hospital discharge.

RESULTS

Thirty-day follow-up data were successfully collected on 94.6% of eligible patients. Among 1344 patients, 16.5% returned to an ED, 9.8% were re-hospitalized, and 2.4% died. Among patients who returned to the ED, 50.0% (108/216) went to a different hospital from the hospital of the index presentation, and 61.1% (132/216) of those who returned were re-hospitalized. In Cox models adjusted for variables selected using the lasso method, age (HR 1.01 per year [95% CI 1.00-1.02]), diabetes (1.54 [1.06-2.23]), and the need for inpatient dialysis (3.78 [2.23-6.43]) during the index presentation were independently associated with a higher re-hospitalization rate. Older age (HR 1.08 [1.05-1.11]) and Asian race (2.89 [1.27-6.61]) were significantly associated with mortality.

CONCLUSIONS

Among patients discharged alive following their index presentation for COVID-19, risk for returning to a hospital within 30 days of discharge was substantial. These patients merit close post-discharge follow-up to optimize outcomes.

Hospital readmissions after stroke in patients with and without dementia and undergone gastrostomy tube placement

INTRODUCTION

Percutaneous endoscopic gastrostomy (PEG) in stroke patients is associated with high hospital readmission rates. The impact of dementia on hospital readmission rates in stroke patients who underwent PEG is unknown. We aimed to assess if stroke patients with dementia who undergo PEG are at risk for readmission.

METHODS

We conducted a retrospective, observational study using the National Readmission Database from Healthcare Cost and Utilization Project (HCUP) from 2013 to 2014. Patients 65 years or older admitted with stroke and who had gastrostomy in the same hospital admission were included. We compared readmission rates at 30 and 60 days between patients with and without dementia and assessed the five most common readmission diagnosis. The association of dementia and hospital readmission was analyzed.

RESULTS

Out of 492,727 patients over 65 who had stroke/PEG, 45,477 (9 %) had dementia. Patients with dementia underwent PEG placement more frequently than those without dementia (4.3% vs. 3.3%, respectively). There was no significant difference in the 30 and 60 days readmission rates between those with dementia and those without. Septicemia, aspiration pneumonitis and complications from the procedure were among top five readmission diagnosis. Dementia was not significantly associated with 30-day (odds ratio (OR) 0.99, 95% CI 0.87-1.13) or 60-day (OR 1, 95% CI 0.89-1.12) readmissions.

CONCLUSIONS

Risks and benefits of gastrostomy in older adults with stroke and dementia should be honestly discussed with patients and their families since it exposes them to a higher risk of hospital readmission due to aspiration pneumonitis and complications from PEG.

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.

Effect of occupational therapy home visit discharge planning on participation after stroke: protocol for the HOME Rehab trial

INTRODUCTION

After first stroke, the transition from rehabilitation to home can be confronting and fraught with challenges. Although stroke clinical practice guidelines recommend predischarge occupational therapy home visits to ensure safe discharge and provision of appropriate equipment, there is currently limited evidence to support this recommendation.

METHODS AND ANALYSIS

The HOME Rehab trial is a national, multicentre, phase III randomised controlled trial with concealed allocation, blinded assessment and intention-to-treat analysis being conducted in Australia. The trial aim is to determine the effect and potential cost-effectiveness of an enhanced occupational therapy discharge planning intervention that involves pre and postdischarge home visits, goal setting and occupational therapy in the home (the HOME programme) in comparison to an in-hospital predischarge planning intervention. Stroke survivors aged ≥45 years, admitted to a rehabilitation ward, expected to return to a community (private) dwelling after discharge, with no significant prestroke disability will be randomly allocated 1:1 to receive a standardised discharge planning intervention and the HOME programme or the standardised discharge planning intervention alone. The primary outcome is participation measured using the Nottingham Extended Activities of Daily Living. Secondary outcome areas include hospital readmission, disability, performance of instrumental activities of daily living, health-related quality of life, quality of care transition and carer burden. Resources used/costs will be collected for the cost-effectiveness analysis and hospital readmission. Recruitment commenced in 2019. Allowing for potential attrition, 360 participants will be recruited to detect a clinically important treatment difference with 80% power at a two-tailed significance level of 0.05.

ETHICS AND DISSEMINATION

This study is approved by the Alfred Health Human Research Ethics Committee and site-specific ethics approval has been obtained at all participating sites. Results of the main trial and the secondary endpoint of cost-effectiveness will be submitted for publication in peer-reviewed journalsTrial registration numberACTRN12618001360202.

Correlations Between Physician and Hospital Stroke Thrombectomy Volumes and Outcomes: A Nationwide Analysis

[Figure: see text].

sTWEAK as Predictor of Stroke Recurrence in Ischemic Stroke Patients Treated With Reperfusion Therapies

Aim: The purpose of this study was to investigate clinical and neuroimaging factors associated with stroke recurrence in reperfused ischemic stroke patients, as well as the influence of specific biomarkers of inflammation and endothelial dysfunction.

Methods: We conducted a retrospective analysis on a prospectively registered database. Of the 875 patients eligible for this study (53.9% males; mean age 69.6 ± 11.8 years vs. 46.1% females; mean age 74.9 ± 12.6 years), 710 underwent systemic thrombolysis, 87 thrombectomy and in 78, systemic or intra-arterial thrombolysis together with thrombectomy was applied. Plasma levels of interleukin 6 (IL-6) and tumor necrosis factor alpha (TNFα) were analyzed as markers of inflammation, and soluble tumor necrosis factor-like inducer of apoptosis (sTWEAK) as an endothelial dysfunction marker. The main outcome variables of the study were the presence and severity of leukoaraiosis (LA) and stroke recurrence.

Results: The average follow-up time of the study was 25 ± 13 months, during which 127 patients (14.5%) showed stroke recurrence. The presence and severity of LA was more severe in the second stroke episode (Grade III of the Fazekas 28.3 vs. 52.8%; p < 0.0001). IL-6 levels at the first admission and before reperfusion treatment in patients with and without subsequent recurrence were similar (9.9 ± 10.4 vs. 9.1 ± 7.0 pg/mL, p = 0.439), but different for TNFα (14.7 ± 5.6 vs. 15.9 ± 5.7 pg/mL, p = 0.031) and sTWEAK (5,970.8 ± 4,330.4 vs. 8,660.7 ± 5,119.0 pg/mL, p < 0.0001). sTWEAK values ≥7,000 pg/mL determined in the first stroke were independently associated to recurrence (OR 2.79; CI 95%: 1.87–4.16, p < 0.0001).

Conclusions: The severity and the progression of LA are the main neuroimaging factors associated with stroke recurrence. Likewise, sTWEAK levels were independently associated to stroke recurrence, so further studies are necessary to investigate sTWEAK as a therapeutic target.

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients

Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting-XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64-0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.

Better nurse work environments associated with fewer readmissions and shorter length of stay among adults with ischemic stroke: A cross-sectional analysis of United States hospitals

Stroke is among the most common reasons for disability and death. Avoiding readmissions and long lengths of stay among ischemic stroke patients has benefits for patients and health care systems alike. Although reduced readmission rates among a variety of medical patients have been associated with better nurse work environments, it is unknown how the work environment might influence readmissions and length of stay for ischemic stroke patients. Using linked data sources, we conducted a cross-sectional analysis of 543 hospitals to evaluate the association between the nurse work environment and readmissions and length of stay for 175,467 hospitalized adult ischemic stroke patients. We utilized logistic regression models for readmission to estimate odds ratios (OR) and zero-truncated negative binomial models for length of stay to estimate the incident-rate ratio (IRR). Final models accounted for hospital and patient characteristics. Seven and 30-day readmission rates were 3.9% and 10.1% respectively and the average length of stay was 4.9 days. In hospitals with better nurse work environments ischemic stroke patients experienced lower odds of 7- and 30-day readmission (7-day OR, 0.96; 95% confidence interval [CI]: 0.93-0.99 and 30-day OR, 0.97; 95% CI: 0.94-0.99) and lower length of stay (IRR, 0.97; 95% CI: 0.95-0.99). The work environment is a modifiable feature of hospitals that should be considered when providing comprehensive stroke care and improving post-stroke outcomes.

Risk of 28-day readmissions among stroke patients in Malaysia (2008-2015): Trends, causes and its associated factors

BACKGROUND AND OBJECTIVES

Risk of readmissions is an important quality indicator for stroke care. Such information is limited among low- and middle-income countries. We assessed the trends for 28-day readmissions after a stroke in Malaysia from 2008 to 2015 and evaluated the causes and factors associated with readmissions in 2015.

METHODS

Using the national hospital admission records database, we included all stroke patients who were discharged alive between 2008 and 2015 for this secondary data analysis. The risk of readmissions was described in proportion and trends. Reasons were coded according to the International Classification of Diseases, 10th Edition. Multivariable logistic regression was performed to identify factors associated with readmissions.

RESULTS

Among 151729 patients, 11 to 13% were readmitted within 28 days post-discharge from their stroke events each year. The trend was constant for ischemic stroke but decreasing for hemorrhagic stroke. The leading causes for readmissions were recurrent stroke (32.1%), pneumonia (13.0%) and sepsis (4.8%). The risk of 28-day readmission was higher among those with stroke of hemorrhagic (adjusted odds ratio (AOR): 1.52) and subarachnoid hemorrhage (AOR: 2.56) subtypes, and length of index admission >3 days (AOR: 1.48), but lower among younger age groups of 35-64 (AORs: 0.61-0.75), p values <0.001.

CONCLUSION

The risk of 28-day readmission remained constant from 2008 to 2015, where one in eight stroke patients required readmission, mainly attributable to preventable causes. Age, ethnicity, stroke subtypes and duration of the index admission influenced the risk of readmission. Efforts should focus on minimizing potentially preventable admissions, especially among those at higher risk.

A novel simple risk model to predict the prognosis of patients with paraquat poisoning

To identify risk factors and develop a simple model to predict early prognosis of acute paraquat (PQ) poisoning patients, we performed a retrospective cohort study of acute PQ poisoning patients (n = 1199). Patients (n = 913) with PQ poisoning from 2011 to 2018 were randomly divided into training (n = 609) and test (n = 304) samples. Another two independent cohorts were used as validation samples for a different time (n = 207) and site (n = 79). Risk factors were identified using a logistic model with Markov Chain Monte Carlo (MCMC) simulation and further evaluated using a latent class analysis. The prediction score was developed based on the training sample and was evaluated using the testing and validation samples. Eight factors, including age, ingestion volume, creatine kinase-MB [CK-MB], platelet [PLT], white blood cell [WBC], neutrophil counts [N], gamma-glutamyl transferase [GGT], and serum creatinine [Cr] were identified as independent risk indicators of in-hospital death events. The risk model had C statistics of 0.895 (95% CI 0.855-0.928), 0.891 (95% CI 0.848-0.932), and 0.829 (95% CI 0.455-1.000), and predictive ranges of 4.6-98.2%, 2.3-94.9%, and 0-12.5% for the test, validation_time, and validation_site samples, respectively. In the training sample, the risk model classified 18.4%, 59.9%, and 21.7% of patients into the high-, average-, and low-risk groups, with corresponding probabilities of 0.985, 0.365, and 0.03 for in-hospital death events. We developed and evaluated a simple risk model to predict the prognosis of patients with acute PQ poisoning. This risk scoring system could be helpful for identifying high-risk patients and reducing mortality due to PQ poisoning.

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.

Patient Characteristics Associated With Readmission to 3 Neurology Services at an Urban Academic Center

Background and Purpose

Hospital 30-day readmissions in patients with primary neurological problems are not well characterized. We sought to determine patient characteristics associated with readmission across 3 different inpatient neurology services at New York University Langone Hospital.

Methods

We retrospectively reviewed all 30-day readmissions from the General Neurology, Epilepsy, and Stroke services at NYULH Brooklyn and Manhattan campuses from 2016-2017 and compared them to a random sample of non-readmitted neurology patients. We used univariate analyses to compare demographics, clinical characteristics, disease specific metrics, and discharge factors of non-readmitted and readmitted groups and binomial logistic regression to examine specific variables with adjustment for confounders.

Results

We included 284 patients with 30-day readmissions and 306 control patients without readmissions matched by discharge location and service. After adjusting for confounders, we found that the following factors were associated with increased readmission risk: a recent hospital encounter increased risk for all services, increased number of medications at discharge, intensive care unit stay, higher length of stay, and prior history of seizure for the General Neurology Service, increased number of medications at discharge for the Epilepsy Service, and active malignancy and higher discharge modified Rankin Scale score for the Stroke Service.

Conclusion

This study identifies potential risk factors for readmission in patients across multiple neurology services. Further research is needed to establish whether these risk factors hold across multiple institutions.

Readmissions and Mortality During the First Year After Stroke-Data From a Population-Based Incidence Study

Background: After a first-ever-in-a-lifetime stroke (FELS), hospital readmissions are common and associated with increased mortality and morbidity of stroke survivors, thus, raising the overall health burden of stroke. Population-based stroke studies on hospital readmissions are scarce despite it being an important healthcare service quality indicator. We evaluated unplanned readmissions or death during the first year after a FELS and their potential factors, based on a Portuguese community register.

Methods: Data were retrieved from a population-based prospective register undertaken in Northern Portugal (ACIN2) in 2009–2011. Retrospective information about unplanned hospital readmissions and case fatality within 1 year after FELS index hospitalization (FELS-IH) was evaluated. Readmission/death-free survival 1 year after discharge was estimated using the Kaplan–Meyer method. Independent risk factors for readmission/death were identified using Cox proportional hazard models.

Results: Unplanned readmission/death within 1 year occurred in 120 (31.6%) of the 389 hospitalized FELS survivors. In 31.2% and 33.5% of the cases, it occurred after ischemic stroke or intracerebral hemorrhage, respectively. Infections and cerebrovascular and cardiovascular diseases were the main causes of readmission. Of the readmissions, 65.3% and 52.5% were potentially avoidable or stroke related, respectively. The main cause of potentially avoidable readmissions was the continuation/recurrence of the event responsible for the initial admission or a closely related condition (71.2%). Male sex, age, previous and post-stroke functional status, and FELS-IH length of stay were independent factors of readmission/death within 1 year.

Conclusions: Almost one-third of FELS survivors were readmitted/dead 1 year after their FELS-IH. This outcome persisted after the first months after stroke hospitalization in all stroke subtypes. More than half of readmissions were considered potentially avoidable or stroke related.

Inpatient rehabilitation facilities' hospital readmission rates for medicare beneficiaries treated following a stroke

BACKGROUND

Stroke is the leading cause for admission to the nearly 1,200 Inpatient Rehabilitation Facilities (IRFs) nationally in the US. For many patients, post-acute care is an important component of their rehabilitation. Several quality measures have been publicly reported for post-acute care providers, including hospital readmissions. However, to date none have focused on specific medical conditions, limiting the usability for patients and quality improvement.

OBJECTIVE

To assess hospital readmission rates for Medicare patients receiving inpatient rehabilitation following stroke and to identify risk factors in order to evaluate the feasibility of a stroke-specific hospital readmission measure.

METHODS

Observational study analyzing national Medicare inpatient claims and administrative data to assess hospital readmissions. Using logistic regression, we calculated unadjusted and risk-standardized readmission rates, which adjusted for patient characteristics, including type of stroke and admission function, to capture stroke severity.

RESULTS

Our national study included 116,073 fee-for-service Medicare beneficiary discharged from IRFs in 2013-2014 following stroke from 1,162 IRFs nationally. The observed hospital readmission rate among IRF patients following stroke was 11.6% and varied by patients' admission motor function. Patients with greater functional dependence had higher readmission rates on average. Lower admission function, hemorrhagic and other stroke types (relative to ischemic) were significantly associated with higher odds of hospital readmission.

CONCLUSION

Results suggest it is feasible to assess hospital readmission rates among a stroke-cohort treated in IRFs. Stroke-focused quality measures would be useful to patients in selecting a provider and for providers in evaluating their stroke rehabilitation program outcomes. Secondary results suggest that admission function (FIM) capture stroke severity, a limitation with other claims-based stroke measures.

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.

Predictors of 30-day hospital readmission after mechanical thrombectomy for acute ischemic stroke

OBJECTIVE

The 30-day readmission rate is of increasing interest to hospital administrators and physicians, as it is used to evaluate hospital performance and is associated with increased healthcare expenditures. The estimated yearly cost to Medicare of readmissions is $17.4 billion. The Centers for Medicare and Medicaid Services therefore track unplanned 30-day readmissions and institute penalties against hospitals whose readmission rates exceed disease-specific national standards. One of the most important conditions with potential for improvement in cost-effective care is ischemic stroke, which affects 795,000 people in the United States and is a leading cause of death and disability. Recent widespread adoption of mechanical thrombectomy has revolutionized stroke care, requiring reassessment of readmission causes and costs in this population.

METHODS

The authors retrospectively analyzed a prospectively maintained database of stroke patients and identified 561 patients who underwent mechanical thrombectomy between 2010 and 2019 at the authors' institution. Univariate and multivariate analyses were conducted to identify clinical variables and comorbidities related to 30-day readmissions in this patient population.

RESULTS

Of the 561 patients, 85.6% (n = 480) survived their admission and were discharged from the hospital to home or rehabilitation, and 8.8% (n = 42/480) were readmitted within 30 days. The median time to readmission was 10.5 days (IQR 6.0-14.3). The most common reasons for readmission were infection (33.3%) and acute cardiac or cerebrovascular events (19% and 20%, respectively). Multivariate analysis showed that hypertension (p = 0.030; OR 2.72) and length of initial hospital stay (p = 0.040; OR 1.032) were significantly correlated with readmission within 30 days, while hemorrhagic conversion (grades 3 and 4) approached significance (p = 0.053; OR 2.23). Other factors, such as unfavorable outcome at discharge, history of coronary artery disease, and discharge destination, did not predict readmission.

CONCLUSIONS

The study data demonstrate that hypertension, length of hospital stay, and hemorrhagic conversion were predictors of 30-day hospital readmission in stroke patients after mechanical thrombectomy. Infection was the most common cause of 30-day readmission, followed by cardiac and cerebrovascular diagnoses. These results therefore may serve to identify patients within the stroke population who require increased surveillance following discharge to reduce complications and unplanned readmissions.

Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals.

RESULTS

Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Financial Burden of Stroke Reflected in a Pilot Center for the Implementation of Thrombolysis

Stroke represents a serious illness and is extremely relevant from the public health point of view, implying important social and economic burdens. Introducing new procedures or therapies that reduce the costs both in the acute phase of the disease and in the long term becomes a priority for health systems worldwide. The present study quantifies and compares the direct costs for ischemic stroke in patients with thrombolysis treatment versus conservative treatment over a 24-month period from the initial diagnosis, in one of the 7 national pilot centres for the implementation of thrombolytic treatment. The significant reduction (p < 0.001) of the hospitalization period, especially of the days in the intensive care unit (ICU) for stroke, resulted in a significant reduction (p < 0.001) of the total average costs in the patients with thrombolysis, both at the first hospitalization and for the subsequent hospitalizations, during the period followed in the study. It was also found that the percentage of patients who were re-hospitalized within the first 24-months after stroke was significantly lower (p < 0.001) among thrombolyzed patients. The present study demonstrates that the quick intervention in cases of stroke is an efficient policy regarding costs, of Romanian Public Health System, Romania being the country with the highest rates of new strokes and deaths due to stroke in Europe.

Development and validation of a simple risk model to predict major cancers for patients with nonalcoholic fatty liver disease

Objective

To recognize risk factors and build up and validate a simple risk model predicting 8‐year cancer events after nonalcoholic fatty liver disease (NAFLD).

Methods

This was a retrospective cohort study. Patients with NAFLD (n = 5561) were randomly divided into groups: training (n = 1254), test (n = 627), evaluation (n = 627), and validation (n = 3053). Risk factors were recognized by statistical method named as a Cox model with Markov chain Monte Carlo (MCMC) simulation. This prediction score was established based on the training group and was further validated based on the testing and evaluation group from January 1, 2007 to December 31, 2009 and another 3053 independent cases from January 1, 2010 to February 13, 2014.

Results

The main outcomes were NAFLD‐related cancer events, including those of the liver, breast, esophagus, stomach, pancreas, prostate and colon, within 8 years after hospitalization for NAFLD diagnosis. Seven risk factors (age (every 5 years),LDL, smoking, BMI, diabetes, OSAS, and aspartate aminotransferase (every 5 units)) were identified as independent indicators of cancer events. This risk model contained a predictive range of 0.4%‐37.7%, 0.3%‐39.6%, and 0.4%‐39.3% in the training, test, evaluation group, respectively, with a range 0.4%‐30.4% for validation groups. In the training group, 12.6%, 76.9%, and 10.5% of patients, which corresponded to the low ‐, moderate ‐, and high‐risk groups, had probabilities of, <0.01, <0.1, and 0.23 for 8‐year events.

Conclusions

Seven risk factors were recognized and a simple risk model were developed and validated to predict the risk of cancer events after NAFLD based on 8 years. This simple risk score system may recognize high‐risk patients and reduce cancer incidence.

The Medicare Shared Savings Program and Outcomes for Ischemic Stroke Patients: a Retrospective Cohort Study

BACKGROUND

Post-stroke care delivery may be affected by provider participation in Medicare Shared Savings Program (MSSP) Accountable Care Organizations (ACOs) through systematic changes to discharge planning, care coordination, and transitional care.

OBJECTIVE

To evaluate the association of MSSP with patient outcomes in the year following hospitalization for ischemic stroke.

DESIGN

Retrospective cohort SETTING: Get With The Guidelines (GWTG)-Stroke (2010-2014) PARTICIPANTS: Hospitalizations for mild to moderate incident ischemic stroke were linked with Medicare claims for fee-for-service beneficiaries ≥ 65 years (N = 251,605).

MAIN MEASURES

Outcomes included discharge to home, 30-day all-cause readmission, length of index hospital stay, days in the community (home-time) at 1 year, and 1-year recurrent stroke and mortality. A difference-in-differences design was used to compare outcomes before and after hospital MSSP implementation for patients (1) discharged from hospitals that chose to participate versus not participate in MSSP or (2) assigned to an MSSP ACO versus not or both. Unique estimates for 2013 and 2014 ACOs were generated.

KEY RESULTS

For hospitals joining MSSP in 2013 or 2014, the probability of discharge to home decreased by 2.57 (95% confidence intervals (CI) = - 4.43, - 0.71) percentage points (pp) and 1.84 pp (CI = - 3.31, - 0.37), respectively, among beneficiaries not assigned to an MSSP ACO. Among discharges from hospitals joining MSSP in 2013, beneficiary ACO alignment versus not was associated with increased home discharge, reduced length of stay, and increased home-time. For patients discharged from hospitals joining MSSP in 2014, ACO alignment was not associated with changes in utilization. No association between MSSP and recurrent stroke or mortality was observed.

CONCLUSIONS

Among patients with mild to moderate ischemic stroke, meaningful reductions in acute care utilization were observed only for ACO-aligned beneficiaries who were also discharged from a hospital initiating MSSP in 2013. Only 1 year of data was available for the 2014 MSSP cohort, and these early results suggest further study is warranted.

REGISTRATION

None.

30-Day Readmissions After Endovascular Thrombectomy for Acute Ischemic Stroke

OBJECTIVES

The authors sought to investigate the incidence, predictors, and causes of 30-day nonelective readmissions after endovascular thrombectomy (EVT).

BACKGROUND

Randomized trials have demonstrated that EVT improves outcomes in patients with acute ischemic stroke.

METHODS

The Nationwide Readmissions Database, years 2013 and 2014, was used to identify hospitalizations for a primary diagnosis of acute ischemic stroke during which patients underwent EVT, with or without intravenous thrombolysis. The incidence and reasons of 30-day readmissions were investigated. A hierarchical Cox regression model was used to identify independent predictors of 30-day nonelective readmissions. A propensity score-matched analysis was performed to compare the risk of 30-day nonelective readmissions in those who underwent EVT versus thrombolysis alone.

RESULTS

Among 2,055,365 weighted hospitalizations with acute ischemic stroke and survival to discharge, 10,795 (0.5%) underwent EVT. The 30-day readmission rate was 12.4% within a median of 9 days (interquartile range: 4 to 18 days). Diabetes mellitus, coagulopathy, Medicare or Medicaid insurance, and gastrostomy during the index hospitalization were independent predictors of 30-day readmission, but coadministration of thrombolytics with EVT was not an independent predictor. The most common reasons for readmission were infections (17.2%), cardiac causes (17.0%), and recurrent stroke or transient ischemic attack (14.8%). Compared with thrombolysis alone, the hazard of 30-day readmissions was similar (hazard ratio: 0.98; 95% confidence interval: 0.91 to 1.05; p = 0.55).

CONCLUSIONS

In patients hospitalized with acute ischemic stroke who underwent EVT, 30-day nonelective readmissions were common, occurring in approximately 1 in 8 patients, but were similar to those of patients treated with thrombolysis alone. Risk of readmission was associated with certain patient demographics, comorbidities, and complications, but not thrombolysis coadministration. Infections, cardiac causes, and recurrent stroke or transient ischemic attack are the most common reasons for readmission after EVT, emphasizing the need for comprehensive multidisciplinary treatment in the transition to outpatient care.

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.

Association of Kidney Function With 30-Day and 1-Year Poststroke Mortality and Hospital Readmission

Background and Purpose- Kidney dysfunction is common among patients hospitalized for ischemic stroke. Understanding the association of kidney disease with poststroke outcomes is important to properly adjust for case mix in outcome studies, payment models and risk-standardized hospital readmission rates. Methods- In this cohort study of fee-for-service Medicare patients admitted with ischemic stroke to 1579 Get With The Guidelines-Stroke participating hospitals between 2009 and 2014, adjusted multivariable Cox proportional hazards models were used to determine the independent associations of estimated glomerular filtration rate (eGFR) and dialysis status with 30-day and 1-year postdischarge mortality and rehospitalizations. Results- Of 204 652 patients discharged alive (median age [25th-75th percentile] 80 years [73.0-86.0], 57.6% women, 79.8% white), 48.8% had an eGFR ≥60, 26.5% an eGFR 45 to 59, 16.3% an eGFR 30 to 44, 5.1% an eGFR 15 to 29, 0.6% an eGFR <15 without dialysis, and 2.8% were receiving dialysis. Compared with eGFR ≥60, and after adjusting for relevant variables, eGFR <45 was associated with increased 30-day mortality with the risk highest among those with eGFR <15 without dialysis (hazard ratio [HR], 2.09; 95% CI, 1.66-2.63). An eGFR <60 was associated with increased 1-year poststroke mortality that was highest among patients on dialysis (HR, 2.65; 95% CI, 2.49-2.81). Dialysis was also associated with the highest 30-day and 1-year rehospitalization rates (HR, 2.10; 95% CI, 1.95-2.26 and HR, 2.55; 95% CI, 2.44-2.66, respectively) and 30-day and 1-year composite of mortality and rehospitalization (HR, 2.04; 95% CI, 1.90-2.18 and HR, 2.46; 95% CI, 2.36-2.56, respectively). Conclusions- Within the first year after index hospitalization for ischemic stroke, eGFR and dialysis status on admission are associated with poststroke mortality and hospital readmissions. Kidney function should be included in risk-stratification models for poststroke outcomes.