Prediction of Hospital Disposition After Thrombolysis for Acute Ischemic Stroke Using the National Institutes of Health Stroke Scale

Schlegel, Daniel J.; Tanné, David; Demchuk, Andrew M.; Levine, Steven R.; Kasner, Scott E.

doi:10.1001/archneur.61.7.1061

Cited by 70 publications

(44 citation statements)

References 12 publications

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“…19 -30 It has been used in all of these trials and others for patient selection. It has been shown to be a strong predictor of outcome 18,31 and therefore is a fundamental factor in risk adjustment.…”

Section: Justificationmentioning

confidence: 99%

Metrics for Measuring Quality of Care in Comprehensive Stroke Centers: Detailed Follow-Up to Brain Attack Coalition Comprehensive Stroke Center Recommendations

Leifer¹,

Bravata²,

Connors³

et al. 2011

Stroke

164

133

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Background-Stroke is a major cause of disability and death. The Brain Attack Coalition has proposed establishment of primary and comprehensive stroke centers to provide appropriate care to stroke patients who require basic and more advanced interventions, respectively. Primary stroke centers have been designated by The Joint Commission since 2003, as well as by various states. The designation of comprehensive stroke centers (CSCs) is now being considered. To assist in this process, we propose a set of metrics and related data that CSCs should track to monitor the quality of care that they provide and to facilitate quality improvement. Methods and Results-We analyzed available guideline statements, reviews, and other literature to identify the major features that distinguish CSCs from primary stroke centers, drafted a set of metrics and related data elements to measure the key components of these aspects of stroke care, and then revised these through an iterative process to reach a consensus. We propose a set of metrics and related data elements that cover the major aspects of specialized care for patients with ischemic cerebrovascular disease and nontraumatic subarachnoid and intracerebral hemorrhages at CSCs. Conclusions-The metrics that we propose are intended to provide a framework for standardized data collection at CSCs to facilitate local quality improvement efforts and to allow for analysis of pooled data from different CSCs that may lead to development of national performance standards for CSCs in the future. (Stroke. 2011;42:849-877.)

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Section: Justificationmentioning

confidence: 99%

Metrics for Measuring Quality of Care in Comprehensive Stroke Centers: Detailed Follow-Up to Brain Attack Coalition Comprehensive Stroke Center Recommendations

Leifer¹,

Bravata²,

Connors³

et al. 2011

Stroke

164

133

View full text Add to dashboard Cite

show abstract

“…Increasing age, living alone, cognitive impairment particularly dementia, psychiatric diagnoses, and limited functional performance (activities of daily living and instrumental activities of daily living) have all been shown to strongly predict the need for skilled care in the United States and Europe. [11][12][13][14][15]36,37 These findings uniquely inform clinical decision making as they are derived from the largest non-proprietary longitudinal data set concerning individuals with TBI that is representative of patients receiving inpatient rehabilitation for TBI in the United States. 22,23 While age and pre-injury living status are important to consider when establishing a plan of care on admission to IR, skills for independence improve with recovery, making prediction of outcome for any individual challenging and the discharge planning process dynamic.…”

Section: Discussionmentioning

confidence: 99%

Predicting Institutionalization after Traumatic Brain Injury Inpatient Rehabilitation

Eum

Seel

Goldstein

et al. 2015

Journal of Neurotrauma

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Risk factors contributing to institutionalization after inpatient rehabilitation for people with traumatic brain injury (TBI) have not been well studied and need to be better understood to guide clinicians during rehabilitation. We aimed to develop a prognostic model that could be used at admission to inpatient rehabilitation facilities to predict discharge disposition. The model could be used to provide the interdisciplinary team with information regarding aspects of patients' functioning and/or their living situation that need particular attention during inpatient rehabilitation if institutionalization is to be avoided. The study population included 7219 patients with moderate-severe TBI in the Traumatic Brain Injury Model Systems (TBIMS) National Database enrolled from 2002-2012 who had not been institutionalized prior to injury. Based on institutionalization predictors in other populations, we hypothesized that among people who had lived at a private residence prior to injury, greater dependence in locomotion, bed-chair-wheelchair transfers, bladder and bowel continence, feeding, and comprehension at admission to inpatient rehabilitation programs would predict institutionalization at discharge. Logistic regression was used, with adjustment for demographic factors, proxy measures for TBI severity, and acute-care length-of-stay. Cstatistic and predictiveness curves validated a five-variable model. Higher levels of independence in bladder management (adjusted odds ratio [OR], 0.88; 95% CI 0.83, 0.93), bed-chair-wheelchair transfers (OR, 0.81 [95% CI,, and comprehension (OR, 0.78 [95% CI, 0.68, 0.89]) at admission were associated with lower risks of institutionalization on discharge. For every 10-year increment in age was associated with a 1.38 times higher risk for institutionalization (95% CI, 1.29, 1.48) and living alone was associated with a 2.34 times higher risk (95% CI, 1.86, 2.94). The c-statistic was 0.780. We conclude that this simple model can predict risk of institutionalization after inpatient rehabilitation for patients with TBI.

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“…20 Predicting outcome makes the NIHSS useful for neuroscience nurses working with families on discharge planning needs. 4,21,22 Factor analysis is a statistical process that is used to establish how individual items cluster around a dimension. Two factors were found in factor analysis of the NIHSS corresponding to left and right brain function in a sample of mild to moderate stroke 23 and another of patients with large strokes.…”

Section: Validitymentioning

confidence: 99%

Reliability and Validity of the National Institutes of Health Stroke Scale for Neuroscience Nurses

Hinkle

2014

Stroke

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Prediction of Hospital Disposition After Thrombolysis for Acute Ischemic Stroke Using the National Institutes of Health Stroke Scale

Cited by 70 publications

References 12 publications

Metrics for Measuring Quality of Care in Comprehensive Stroke Centers: Detailed Follow-Up to Brain Attack Coalition Comprehensive Stroke Center Recommendations

Metrics for Measuring Quality of Care in Comprehensive Stroke Centers: Detailed Follow-Up to Brain Attack Coalition Comprehensive Stroke Center Recommendations

Predicting Institutionalization after Traumatic Brain Injury Inpatient Rehabilitation

Reliability and Validity of the National Institutes of Health Stroke Scale for Neuroscience Nurses

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