Validation of algorithms to identify stroke risk factors in patients with acute ischemic stroke, transient ischemic attack, or intracerebral hemorrhage in an administrative claims database

Sheng Feng Sung, Cheng Yang Hsieh, Huey Juan Lin, Yu Wei Chen, Yea-Huei Kao, Chung-Yi Li

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

Background Stroke patients have a high risk for recurrence, which is positively correlated with the number of risk factors. The assessment of risk factors is essential in both stroke outcomes research and the surveillance of stroke burden. However, methods for assessment of risk factors using claims data are not well developed. Methods We enrolled 6469 patients with acute ischemic stroke, transient ischemic attack, or intracerebral hemorrhage from hospital-based stroke registries, which were linked with Taiwan's National Health Insurance (NHI) claims database. We developed algorithms using diagnosis codes and prescription data to identify stroke risk factors including hypertension, diabetes, hyperlipidemia, atrial fibrillation (AF), and coronary artery disease (CAD) in the claims database using registry data as reference standard. We estimated the kappa statistics to quantify the agreement of information on the risk factors between claims and registry data. Results The prevalence of risk factors in the registries was hypertension 77.0%, diabetes 39.1%, hyperlipidemia 55.6%, AF 10.1%, and CAD 10.9%. The highest kappa statistics were 0.552 (95% confidence interval 0.528-0.577) for hypertension, 0.861 (0.836-0.885) for diabetes, 0.572 (0.549-0.596) for hyperlipidemia, 0.687 (0.663-0.712) for AF, and 0.480 (0.455-0.504) for CAD. Algorithms based on diagnosis codes alone could achieve moderate to high agreement in identifying the selected risk factors, whereas prescription data helped improve identification of hyperlipidemia. Conclusions We tested various claims-based algorithms to ascertain important risk factors in stroke patients. These validated algorithms are useful for assessing stroke risk factors in future studies using Taiwan's NHI claims data.

Original languageEnglish
Pages (from-to)277-282
Number of pages6
JournalInternational Journal of Cardiology
Volume215
DOIs
Publication statusPublished - 2016 Jul 15

Fingerprint

Transient Ischemic Attack
Cerebral Hemorrhage
Stroke
Databases
Hyperlipidemias
Registries
Atrial Fibrillation
Coronary Artery Disease
National Health Programs
Hypertension
Taiwan
Prescriptions
Outcome Assessment (Health Care)
Confidence Intervals
Recurrence

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine

Cite this

@article{eb256fd3f931462b89be4a0b7b481033,
title = "Validation of algorithms to identify stroke risk factors in patients with acute ischemic stroke, transient ischemic attack, or intracerebral hemorrhage in an administrative claims database",
abstract = "Background Stroke patients have a high risk for recurrence, which is positively correlated with the number of risk factors. The assessment of risk factors is essential in both stroke outcomes research and the surveillance of stroke burden. However, methods for assessment of risk factors using claims data are not well developed. Methods We enrolled 6469 patients with acute ischemic stroke, transient ischemic attack, or intracerebral hemorrhage from hospital-based stroke registries, which were linked with Taiwan's National Health Insurance (NHI) claims database. We developed algorithms using diagnosis codes and prescription data to identify stroke risk factors including hypertension, diabetes, hyperlipidemia, atrial fibrillation (AF), and coronary artery disease (CAD) in the claims database using registry data as reference standard. We estimated the kappa statistics to quantify the agreement of information on the risk factors between claims and registry data. Results The prevalence of risk factors in the registries was hypertension 77.0{\%}, diabetes 39.1{\%}, hyperlipidemia 55.6{\%}, AF 10.1{\%}, and CAD 10.9{\%}. The highest kappa statistics were 0.552 (95{\%} confidence interval 0.528-0.577) for hypertension, 0.861 (0.836-0.885) for diabetes, 0.572 (0.549-0.596) for hyperlipidemia, 0.687 (0.663-0.712) for AF, and 0.480 (0.455-0.504) for CAD. Algorithms based on diagnosis codes alone could achieve moderate to high agreement in identifying the selected risk factors, whereas prescription data helped improve identification of hyperlipidemia. Conclusions We tested various claims-based algorithms to ascertain important risk factors in stroke patients. These validated algorithms are useful for assessing stroke risk factors in future studies using Taiwan's NHI claims data.",
author = "Sung, {Sheng Feng} and Hsieh, {Cheng Yang} and Lin, {Huey Juan} and Chen, {Yu Wei} and Yea-Huei Kao and Chung-Yi Li",
year = "2016",
month = "7",
day = "15",
doi = "10.1016/j.ijcard.2016.04.069",
language = "English",
volume = "215",
pages = "277--282",
journal = "International Journal of Cardiology",
issn = "0167-5273",
publisher = "Elsevier Ireland Ltd",

}

TY - JOUR

T1 - Validation of algorithms to identify stroke risk factors in patients with acute ischemic stroke, transient ischemic attack, or intracerebral hemorrhage in an administrative claims database

AU - Sung, Sheng Feng

AU - Hsieh, Cheng Yang

AU - Lin, Huey Juan

AU - Chen, Yu Wei

AU - Kao, Yea-Huei

AU - Li, Chung-Yi

PY - 2016/7/15

Y1 - 2016/7/15

N2 - Background Stroke patients have a high risk for recurrence, which is positively correlated with the number of risk factors. The assessment of risk factors is essential in both stroke outcomes research and the surveillance of stroke burden. However, methods for assessment of risk factors using claims data are not well developed. Methods We enrolled 6469 patients with acute ischemic stroke, transient ischemic attack, or intracerebral hemorrhage from hospital-based stroke registries, which were linked with Taiwan's National Health Insurance (NHI) claims database. We developed algorithms using diagnosis codes and prescription data to identify stroke risk factors including hypertension, diabetes, hyperlipidemia, atrial fibrillation (AF), and coronary artery disease (CAD) in the claims database using registry data as reference standard. We estimated the kappa statistics to quantify the agreement of information on the risk factors between claims and registry data. Results The prevalence of risk factors in the registries was hypertension 77.0%, diabetes 39.1%, hyperlipidemia 55.6%, AF 10.1%, and CAD 10.9%. The highest kappa statistics were 0.552 (95% confidence interval 0.528-0.577) for hypertension, 0.861 (0.836-0.885) for diabetes, 0.572 (0.549-0.596) for hyperlipidemia, 0.687 (0.663-0.712) for AF, and 0.480 (0.455-0.504) for CAD. Algorithms based on diagnosis codes alone could achieve moderate to high agreement in identifying the selected risk factors, whereas prescription data helped improve identification of hyperlipidemia. Conclusions We tested various claims-based algorithms to ascertain important risk factors in stroke patients. These validated algorithms are useful for assessing stroke risk factors in future studies using Taiwan's NHI claims data.

AB - Background Stroke patients have a high risk for recurrence, which is positively correlated with the number of risk factors. The assessment of risk factors is essential in both stroke outcomes research and the surveillance of stroke burden. However, methods for assessment of risk factors using claims data are not well developed. Methods We enrolled 6469 patients with acute ischemic stroke, transient ischemic attack, or intracerebral hemorrhage from hospital-based stroke registries, which were linked with Taiwan's National Health Insurance (NHI) claims database. We developed algorithms using diagnosis codes and prescription data to identify stroke risk factors including hypertension, diabetes, hyperlipidemia, atrial fibrillation (AF), and coronary artery disease (CAD) in the claims database using registry data as reference standard. We estimated the kappa statistics to quantify the agreement of information on the risk factors between claims and registry data. Results The prevalence of risk factors in the registries was hypertension 77.0%, diabetes 39.1%, hyperlipidemia 55.6%, AF 10.1%, and CAD 10.9%. The highest kappa statistics were 0.552 (95% confidence interval 0.528-0.577) for hypertension, 0.861 (0.836-0.885) for diabetes, 0.572 (0.549-0.596) for hyperlipidemia, 0.687 (0.663-0.712) for AF, and 0.480 (0.455-0.504) for CAD. Algorithms based on diagnosis codes alone could achieve moderate to high agreement in identifying the selected risk factors, whereas prescription data helped improve identification of hyperlipidemia. Conclusions We tested various claims-based algorithms to ascertain important risk factors in stroke patients. These validated algorithms are useful for assessing stroke risk factors in future studies using Taiwan's NHI claims data.

UR - http://www.scopus.com/inward/record.url?scp=84964692054&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964692054&partnerID=8YFLogxK

U2 - 10.1016/j.ijcard.2016.04.069

DO - 10.1016/j.ijcard.2016.04.069

M3 - Article

C2 - 27128546

AN - SCOPUS:84964692054

VL - 215

SP - 277

EP - 282

JO - International Journal of Cardiology

JF - International Journal of Cardiology

SN - 0167-5273

ER -