Readmissions After Mechanical Thrombectomy for Acute Ischemic Stroke in the United States: A Nationwide Analysis

Ramchand, Preethi; Thibault, Dylan; Crispo, James A.G.; Levine, Joshua M.; Hurst, Robert W.; Mullen, Michael T.; Kasner, Scott E.; Willis, Allison W.

doi:10.1016/j.jstrokecerebrovasdis.2018.05.035

Cited by 15 publications

(14 citation statements)

References 38 publications

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“…The ICD-based data collection used in their study runs the risk of missing relevant clinical information. 15 Our study demonstrated that hemorrhage was strongly associated with 30-day readmission. Postprocedure hemorrhage significantly affects patient management, as it influences decisions regarding antiplatelet medication, hypertonic saline administration, ICU LOS, and possible surgical intervention (i.e., craniectomy).…”

Section: Reason For Readmissionmentioning

confidence: 52%

“…Using the NRD, they studied 4850 cases and showed the 30-and 90-day readmission rates to be 12.5% and 20.7%, respectively. 15 Additionally, Ramchand et al identified certain factors, such as longer initial LOS, discharge to inpatient postacute care facilities, and female sex to be associated with a higher likelihood of readmission. Similar to our study results, sepsis and recurrent cerebrovascular events were found to be the most commonly cited reasons for readmission.…”

Section: Reason For Readmissionmentioning

confidence: 99%

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

Mouchtouris

Saiegh

Valcarcel

et al. 2021

Journal of Neurosurgery

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OBJECTIVEThe 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.METHODSThe 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.RESULTSOf 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 mean time to readmission was 10.9 ± 7.9 days postdischarge. 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.CONCLUSIONSThe 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.

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Section: Reason For Readmissionmentioning

confidence: 52%

Section: Reason For Readmissionmentioning

confidence: 99%

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

Mouchtouris

Saiegh

Valcarcel

et al. 2021

Journal of Neurosurgery

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“…Recent studies using the NRD have focused on incidence, predictors, and causes of readmission following mechanical thrombectomy. These studies suggest a roughly 12% 30-day readmission rate20 21 and a 20.7% 90-day readmission rate, respectively 22. Our study demonstrates similar rates of readmission (24.8% 90-day readmission rate) and focuses on factors that influence readmission to index and non-index hospitals and compares outcomes between these hospital types.…”

Section: Discussionmentioning

confidence: 53%

Frequency, predictors, and outcomes of readmission to index versus non-index hospitals after mechanical thrombectomy in patients with ischemic stroke

Shkirkova

Connor

Lamorie‐Foote

et al. 2019

J NeuroIntervent Surg

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BackgroundStroke systems of care employ a hub-and-spoke model, with fewer centers performing mechanical thrombectomy (MT) compared with stroke-receiving centers, where a higher number offer high-level, centralized treatment to a large number of patients.ObjectiveTo characterize rates and outcomes of readmission to index and non-index hospitals for patients with ischemic stroke who underwent MT.MethodsThis study leveraged a population-based, nationally representative sample of patients with stroke undergoing MT from the Nationwide Readmissions Database between 2010 and 2014. Descriptive, logistic regression analyses, and univariate and multivariate logistic regression models were carried out to determine patient- and hospital-level factors, mortality, complications, and subsequent readmissions associated with index and non-index hospitals' 90-day readmissions.ResultsIn the study, 2111 patients with a stroke were treated with MT, of whom 534 were readmitted within 90 days. The most common reasons for readmission were: septicemia (5.9%), atrial fibrillation (4.8%), and cerebral artery occlusion with infarct (4.8%). Among readmitted patients, 387 (74%) were readmitted to index and 136 (26%) to non-index hospitals. On multivariable logistic regression analysis, non-index hospital readmission was not independently associated with major complications (p=0.09), mortality (p=0.34), neurological complications (p=0.47), or second readmission (p=0.92).ConclusionOne-quarter of patients with a stroke treated with MT were readmitted within 90 days, and one quarter of these patients were readmitted to non-index hospitals. Readmission to a non-index hospital was not associated with mortality or increased complication rates. In a hub-and-spoke model it is important that follow-up care for a specialized procedure can be performed effectively at a vast number of non-index hospitals covering a large geographic area.

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“…For further validation of the significant association observed between PAC and 30-day readmission for stroke, Table 7 compares six relevant studies performed in the United States or Taiwan (39)(40)(41)(42)(43)(44). The six studies shared the following features:…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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BackgroundMachine 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.MethodsThe 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.ResultsFor predicting 30-day readmission after stroke, the ANN model had significantly (P < 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 < 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.ConclusionUsing 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.

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Readmissions After Mechanical Thrombectomy for Acute Ischemic Stroke in the United States: A Nationwide Analysis

Cited by 15 publications

References 38 publications

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

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

Frequency, predictors, and outcomes of readmission to index versus non-index hospitals after mechanical thrombectomy in patients with ischemic stroke

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

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