Boback Ziaeian1,2, Haolin Xu3, Roland A Matsouaka3,4, Ying Xian3,5, Yosef Khan6, Lee S Schwamm7, Eric E Smith8, Gregg C Fonarow9,10. 1. Division of Cardiology, David Geffen School of Medicine at University of California, 10833 LeConte Avenue, Room A2-237 CHS, Los Angeles, CA, 90095-1679, USA. bziaeian@mednet.ucla.edu. 2. Division of Cardiology, Veteran Affairs Greater Los Angeles Healthcare System, Los Angeles, California, USA. bziaeian@mednet.ucla.edu. 3. Duke Clinical Research Institute, Durham, North Carolina, UK. 4. Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, UK. 5. Department of Neurology, Duke University Medical Center, Durham, North Carolina, UK. 6. Healthcare Quality Research and Bioinformatics, American Heart Association, Dallas, TX, USA. 7. Department of Neurology, Comprehensive Stroke Center Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 8. Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada. 9. Division of Cardiology, David Geffen School of Medicine at University of California, 10833 LeConte Avenue, Room A2-237 CHS, Los Angeles, CA, 90095-1679, USA. 10. Ahmanson-UCLA Cardiomyopathy Center, University of California, Los Angeles Medical Center, Los Angeles, California, USA.
Abstract
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.
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.
Entities:
Keywords:
Bayesian analysis; Epidemiology; Health services; Ischemic stroke; Population surveillance; Quality and outcomes
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