Practice Patterns for Acute Ischemic Stroke Workup: A Longitudinal Population‐Based Study

Loftspring, Matthew C.; Kissela, Brett M.; Flaherty, Matthew L.; Alwell, Kathleen; Moomaw, Charles J.; Kleindorfer, Dawn; Woo, Daniel; Adeoye, Opeolu; Ferioli, Simona; Broderick, Joseph P.; Khatri, Pooja

doi:10.1161/jaha.116.005097

Cited by 6 publications

(3 citation statements)

References 14 publications

(16 reference statements)

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“…The Greater Cincinnati/Northern Kentucky Stroke Study makes epidemiologic inferences using case ascertainment for an urban population to report stroke incidence rates. The population described is slightly younger, more female, has a higher representation of African-Americans, and higher rates of coronary artery disease and heart failure than is estimated from the NIS or weighted GWTG-Stroke presented (Table 2) [25][26][27].…”

Section: Discussionmentioning

confidence: 95%

National surveillance of stroke quality of care and outcomes by applying post-stratification survey weights on the Get With The Guidelines-Stroke patient registry

Ziaeian

Matsouaka

et al. 2021

BMC Med Res Methodol

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Background The U.S. lacks a stroke surveillance system. This study develops a method to transform an existing registry into a nationally representative database to evaluate acute ischemic stroke care quality. Methods Two statistical approaches are used to develop post-stratification weights for the Get With The Guidelines-Stroke registry by anchoring population estimates to the National Inpatient Sample. Post-stratification survey weights are estimated using a raking procedure and Bayesian interpolation methods. Weighting methods are adjusted to limit the dispersion of weights and make reasonable epidemiologic estimates of patient characteristics, quality of hospital care, and clinical outcomes. Standardized differences in national estimates are reported between the two post-stratification methods for anchored and non-anchored patient characteristics to evaluate estimation quality. Primary measures evaluated are patient and hospital characteristics, stroke severity, vital and laboratory measures, disposition, and clinical outcomes at discharge. Results A total of 1,388,296 acute ischemic strokes occurred between 2012 and 2014. Raking and Bayesian estimates of clinical data not available in administrative data are estimated within 5 to 10% of margin for expected values. Median weight for the raking method is 1.386 and the weights at the 99th percentile is 6.881 with a maximum weight of 30.775. Median Bayesian weight is 1.329 and the 99th percentile weights is 11.201 with a maximum weight of 515.689. Conclusions Leveraging existing databases with patient registries to develop post-stratification weights is a reliable approach to estimate acute ischemic stroke epidemiology and monitoring for stroke quality of care nationally. These methods may be applied to other diseases or settings to better monitor population health.

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Section: Discussionmentioning

confidence: 95%

National surveillance of stroke quality of care and outcomes by applying post-stratification survey weights on the Get With The Guidelines-Stroke patient registry

Ziaeian

Matsouaka

et al. 2021

BMC Med Res Methodol

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“…To determine the causative mechanism of an ischemic stroke, clinicians synthesize a vast array of data including clinical history and physical examination, laboratory data, cardiac 230918 rhythm interrogation, cardiac imaging, and neuroradiologic studies. Utilization of diagnostic tools has increased with time, nevertheless, a significant proportion of patients remain cryptogenic 22 . Diagnostic uncertainty arises due to 1) an inadequate or incomplete workup with further results pending after discharge, 2) a complete workup yielding no known stroke etiology, or 3) multiple, competing possible etiologies, resulting in a diagnosis of stroke of undetermined etiology 3 .…”

Section: Introductionmentioning

confidence: 99%

StrokeClassifier: Ischemic Stroke Etiology Classification by Ensemble Consensus Modeling Using Electronic Health Records

Lee,

Schwamm,

Sansing

et al. 2023

Preprint

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Determining the etiology of an acute ischemic stroke (AIS) is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification machine intelligence tool, StrokeClassifier, using electronic health record (EHR) text data from 2,039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology determined by agreement of at least 2 board-certified vascular neurologists’ review of the stroke hospitalization EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with stroke etiologies adjudicated by vascular neurologists, nine base classifiers performed well with a mean cross-validated area under the receiver operating curve (AUCROC) of 0.90. Their ensemble meta-model, StrokeClassifier, achieved a mean cross-validated accuracy of 0.74 and weighted F1 of 0.74. In the MIMIC-III cohort, the accuracy and weighted F1 of StrokeClassifier were 0.70, and 0.71, respectively. SHapley Additive exPlanation analysis revealed that the top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We then designed a certainty heuristic to deem a StrokeClassifier diagnosis as confidently non-cryptogenic by the degree of consensus among the 9 classifiers, and applied it to 788 cryptogenic patients. This reduced the percentage of the cryptogenic strokes from 25.2–7.2% of all ischemic strokes. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology for individual patients. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

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“…(24)(25)(26).The approach described in the present paper is a far more robust estimation of the characteristics of stroke presentation and the quality of hospital care nationally. The GWTG-Stroke patient registry captures 58% of all strokes nationally.…”

mentioning

confidence: 98%

National Surveillance of Stroke Quality of Care and Outcomes by Applying Post-Stratification Survey Weights on the Get With The Guidelines-Stroke Patient Registry

Ziaeian¹,

Matsouaka

et al. 2020

Preprint

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Background: The U.S. lacks a stroke surveillance system. This study develops a method to transform an existing registry into a nationally representative database to evaluate acute ischemic stroke care quality.Methods: Two statistical approaches were used to develop post-stratification weights for the Get With The Guidelines-Stroke registry by anchoring population estimates to the National Inpatient Sample. Post-stratification survey weights were estimated using a raking procedure and Bayesian interpolation methods. Weighting methods were adjusted to limit the dispersion of weights and make reasonable epidemiologic estimates of patient characteristics, quality of hospital care, and clinical outcomes. Standardized differences in national estimates were reported between the two post-stratification methods for anchored and non-anchored patient characteristics to evaluate estimation quality. Primary measures evaluated were patient and hospital characteristics, stroke severity, vital and laboratory measures, disposition, and clinical outcomes at discharge. Results: A total of 1,388,296 acute ischemic strokes occurred between 2012 and 2014. Raking and Bayesian estimates of clinical data not recorded in administrative databases were estimated within 5% to 10% of the margins of expected values. Median weights for the raking method were 1.386 and the weights at the 99th percentile were 6.881 with a maximum weight of 30.775. Median Bayesian weights were 1.329 and the 99th percentile weights were 11.201 with a maximum weight of 515.689. Conclusions: Leveraging existing databases with patient registries to develop post-stratification weights is a reliable approach to estimate acute ischemic stroke epidemiology and monitoring for stroke quality of care nationally. These methods may be applied to other diseases or settings to better monitor population health.

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Practice Patterns for Acute Ischemic Stroke Workup: A Longitudinal Population‐Based Study

Cited by 6 publications

References 14 publications

National surveillance of stroke quality of care and outcomes by applying post-stratification survey weights on the Get With The Guidelines-Stroke patient registry

National surveillance of stroke quality of care and outcomes by applying post-stratification survey weights on the Get With The Guidelines-Stroke patient registry

StrokeClassifier: Ischemic Stroke Etiology Classification by Ensemble Consensus Modeling Using Electronic Health Records

National Surveillance of Stroke Quality of Care and Outcomes by Applying Post-Stratification Survey Weights on the Get With The Guidelines-Stroke Patient Registry

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