Creation and Validation of a Stroke Scale to Increase Utility of National Inpatient Sample Administrative Data for Clinical Stroke Research

Patel, Pious D.; Salwi, Sanjana; Liles, Campbell; Mistry, Akshitkumar M.; Mistry, Eva; Fusco, Matthew R.; Chitale, Rohan; Shannon, Chevis N.

doi:10.1016/j.jstrokecerebrovasdis.2021.105658

Cited by 14 publications

(16 citation statements)

References 26 publications

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“…Utilizing Medicare data also provided ample detail to enable a comprehensive analysis of imaging trends in terms of care disparities, impact on treatment utilization, and mortality. Using the Medicare claims data, however, has limitations, including potential misclassification of patients with ischemic stroke when using ICD ninth rev and ICD 10th rev diagnostic codes and the inability to determine whether neuroimaging occurred before treatment if they were on the same calendar day [17]. Furthermore, our study sample was limited to patients aged 65 years and older and was almost 83% White; therefore, our results may not be applicable to younger and more racially and sociodemographically diverse populations.…”

Section: Strengths and Limitationsmentioning

confidence: 98%

“…(2) clinical: Administrative Data Stroke Scale (ADSS [0, 1-2, >2]) was used as a surrogate for stroke severity [17], Charlson Comorbidity Index (0-1, !2) [18], arrival by transfer from another facility (transfer, nontransfer); (3) resource utilization: advanced neuroimaging (CTA, CTP, MRI, MRA) and acute treatment (IVT only, EVT with or without IVT, none); and (4) mortality: in the hospital, 30 days postdischarge, 1 year postdischarge. Race and ethnicity are combined in a single category in Medicare claims data, and the individual groups are mutually exclusive.…”

Section: Study Population and Data Collectionmentioning

confidence: 99%

See 1 more Smart Citation

Updated Trends, Disparities, and Clinical Impact of Neuroimaging Utilization in Ischemic Stroke in the Medicare Population: 2012 to 2019

Wang

Pelzl

Boltyenkov

et al. 2022

Journal of the American College of Radiology

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Section: Strengths and Limitationsmentioning

confidence: 98%

Section: Study Population and Data Collectionmentioning

confidence: 99%

Updated Trends, Disparities, and Clinical Impact of Neuroimaging Utilization in Ischemic Stroke in the Medicare Population: 2012 to 2019

Wang

Pelzl

Boltyenkov

et al. 2022

Journal of the American College of Radiology

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“…Notably, therefore, our model obtains a similar discriminant capacity to that offered by the one proposed by Smith et al, in both cases excluding the NIHSS variable. This is a useful achievement because our model is based on a clinical–administrative information source, which is inevitably less exhaustive than the database used in the earlier study [ 37 ]. The visual representation of the risk deciles (observed vs. expected) in the Hosmer–Lemeshow (HL) test shows our calibration data to be acceptable.…”

Section: Discussionmentioning

confidence: 99%

Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment

Lea-Pereira

Amaya-Pascasio

Martínez-Sánchez

et al. 2022

IJERPH

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Background: Stroke is the second cause of mortality worldwide and the first in women. The aim of this study is to develop a predictive model to estimate the risk of mortality in the admission of patients who have not received reperfusion treatment. Methods: A retrospective cohort study was conducted of a clinical–administrative database, reflecting all cases of non-reperfused ischaemic stroke admitted to Spanish hospitals during the period 2008–2012. A predictive model based on logistic regression was developed on a training cohort and later validated by the “hold-out” method. Complementary machine learning techniques were also explored. Results: The resulting model had the following nine variables, all readily obtainable during initial care. Age (OR 1.069), female sex (OR 1.202), readmission (OR 2.008), hypertension (OR 0.726), diabetes (OR 1.105), atrial fibrillation (OR 1.537), dyslipidaemia (0.638), heart failure (OR 1.518) and neurological symptoms suggestive of posterior fossa involvement (OR 2.639). The predictability was moderate (AUC 0.742, 95% CI: 0.737–0.747), with good visual calibration; Pearson’s chi-square test revealed non-significant calibration. An easily consulted risk score was prepared. Conclusions: It is possible to create a predictive model of mortality for patients with ischaemic stroke from which important advances can be made towards optimising the quality and efficiency of care. The model results are available within a few minutes of admission and would provide a valuable complementary resource for the neurologist.

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“…Big data studies often lack information on important clinical or biological measures, such as patient frailty [35••] or stroke severity [19,70,71], that are pertinent to the research of stroke outcomes. To overcome this limitation, authors have developed surrogate indicators for these measures using other routinely available prognostic variables.…”

Section: Validation Of Surrogate Measures Using Big Datamentioning

confidence: 99%

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

Olaiya

Sodhi-Berry

Dalli

et al. 2022

Curr Neurol Neurosci Rep

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Purpose of Review To critically appraise literature on recent advances and methods using “big data” to evaluate stroke outcomes and associated factors. Recent Findings Recent big data studies provided new evidence on the incidence of stroke outcomes, and important emerging predictors of these outcomes. Main highlights included the identification of COVID-19 infection and exposure to a low-dose particulate matter as emerging predictors of mortality post-stroke. Demographic (age, sex) and geographical (rural vs. urban) disparities in outcomes were also identified. There was a surge in methodological (e.g., machine learning and validation) studies aimed at maximizing the efficiency of big data for improving the prediction of stroke outcomes. However, considerable delays remain between data generation and publication. Summary Big data are driving rapid innovations in research of stroke outcomes, generating novel evidence for bridging practice gaps. Opportunity exists to harness big data to drive real-time improvements in stroke outcomes.

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Creation and Validation of a Stroke Scale to Increase Utility of National Inpatient Sample Administrative Data for Clinical Stroke Research

Cited by 14 publications

References 26 publications

Updated Trends, Disparities, and Clinical Impact of Neuroimaging Utilization in Ischemic Stroke in the Medicare Population: 2012 to 2019

Updated Trends, Disparities, and Clinical Impact of Neuroimaging Utilization in Ischemic Stroke in the Medicare Population: 2012 to 2019

Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

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