Comparing and Predicting the Costs and Outcomes of Patients with Major and Minor Stroke Using the Boston Acute Stroke Imaging Scale Neuroimaging Classification System

Cipriano, Lauren E.; Steinberg, Michael L.; Gazelle, G. Scott; González, R. Gilberto

doi:10.3174/ajnr.a1441

Cited by 36 publications

(25 citation statements)

References 28 publications

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“…The correlations were not very strong, a point largely reported in the literature [22][23][24] and easy to understand, considering the numerous additional factors that can influence clinical outcomes at the disability/handicap level (On-line Fig 1). [25][26][27] Here, other clinical (initial stroke severity, age) or neuroimaging (recanalization, reperfusion) variables were significantly associated with the outcome, in line with previous studies. 25,28 The relatively weak association between infarct volumes and clinical outcomes does not necessarily discredit volume as a surrogate marker.…”

Section: Discussionsupporting

confidence: 76%

Final Cerebral Infarct Volume Is Predictable by MR Imaging at 1 Week

Tourdias¹,

Renou²,

Sibon³

et al. 2010

AJNR Am J Neuroradiol

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Section: Discussionsupporting

confidence: 76%

Final Cerebral Infarct Volume Is Predictable by MR Imaging at 1 Week

Tourdias¹,

Renou²,

Sibon³

et al. 2010

AJNR Am J Neuroradiol

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“…Second, numerous studies have identified stroke severity to be the most important determinant of resource use in the treatment of stroke patients [32,33,34,35,36,37]. However, stroke severity is not included as a classification variable in any of the DRG systems analysed.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis-Related Groups for Stroke in Europe: Patient Classification and Hospital Reimbursement in 11 Countries

Peltola

Quentin²

2013

Cerebrovasc Dis

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Background: Diagnosis-related groups (DRGs) are increasingly being used for various purposes in many countries. However, there are no studies comparing different DRG systems in the care of stroke. As part of the EuroDRG project, researchers from 11 countries (i.e. Austria, England, Estonia, Finland, France, Germany, Ireland, the Netherlands, Poland, Sweden and Spain) compared how their DRG systems deal with stroke patients. The study aims to assist clinicians and national authorities to optimize their DRG systems. Methods: National or regional databases were used to identify hospital cases with a diagnosis of stroke. DRG classification algorithms and indicators of resource consumption were compared for those DRGs that individually represent at least 1% of stroke cases. In addition, standardized case vignettes were defined, and quasi prices according to national DRG-based hospital payment systems were ascertained. Results: European DRG systems vary widely: they classify stroke patients according to different sets of variables (between 1 and 7 classification variables) into diverging numbers of DRGs (between 1 and 10 DRGs). In 6 of the countries more than half of the patients are concentrated within a single DRG. The countries’ systems also vary with respect to the evaluation of different kinds of stroke patients. The most complex DRG is considered 3.8 times more resource intensive than an index case in Finland. By contrast, in England, the DRG system does not account for complex cases. Comparisons of quasi prices for the case vignettes show that hypothetical payments for the index case amount to only EUR 907 in Poland but to EUR 7,881 in Ireland. Conclusions: Large variations in the classification of stroke patients raise concerns whether all systems rely on the most appropriate classification variables and whether the DRGs adequately reflect differences in the complexity of treating different groups of patients. Learning from other DRG systems may help in improving the national systems. Clinicians and national DRG authorities should consider how other countries’ DRG systems classify stroke patients in order to optimize their DRG system and to ensure fair and appropriate reimbursement. In future, quantitative research is needed to verify whether the most important determinants of cost are considered in different patient classification systems, and whether differences between systems reflect country-specific differences in treatment patterns and, most importantly, what influence they have on patient outcomes.

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“…If there is much variation in LoS between units, or if the LoS averages differ substantially, it raises the question whether the treatment is optimally managed in all units. For cerebral infarction, LoS is a strong indicator for the costs of treatment (Cipriano et al, 2009;Diringer et al, 1999;Saxena et al, 2006;Seitz and Edwardson, 1987). Therefore, any unwarranted upward variation in LoS also implies undesirable variation in costs and inefficient use of resources.…”

Section: Introductionmentioning

confidence: 99%

Individual and Regional‐level Factors Contributing to Variation in Length of Stay After Cerebral Infarction in Six European Countries

et al. 2015

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Using patient-level data for cerebral infarction cases in 2007, gathered from Finland, Hungary, Italy, the Netherlands, Scotland and Sweden, we studied the variation in risk-adjusted length of stay (LoS) of acute hospital care and 1-year mortality, both within and between countries. In addition, we analysed the variance of LoS and associations of selected regional-level factors with LoS and 1-year mortality after cerebral infarction. The data show that LoS distributions are surprisingly different across countries and that there is significant deviation in the risk-adjusted regional-level LoS in all of the countries studied. We used negative binomial regression to model the individual-level LoS, and random intercept models and ordinary least squares regression for the regional-level analysis of risk-adjusted LoS, variance of LoS, 1-year risk-adjusted mortality and crude mortality for a period of 31-365 days. The observed variations between regions and countries in both LoS and mortality were not fully explained by either patient-level or regional-level factors. The results indicate that there may exist potential for efficiency gains in acute hospital care of cerebral infarction and that healthcare managers could learn from best practices.

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Comparing and Predicting the Costs and Outcomes of Patients with Major and Minor Stroke Using the Boston Acute Stroke Imaging Scale Neuroimaging Classification System

Cited by 36 publications

References 28 publications

Final Cerebral Infarct Volume Is Predictable by MR Imaging at 1 Week

Final Cerebral Infarct Volume Is Predictable by MR Imaging at 1 Week

Diagnosis-Related Groups for Stroke in Europe: Patient Classification and Hospital Reimbursement in 11 Countries

Individual and Regional‐level Factors Contributing to Variation in Length of Stay After Cerebral Infarction in Six European Countries

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