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Development of stroke identification algorithm for claims data using the multicenter stroke registry database.

Jun Yup Kim1, Keon-Joo Lee1, Jihoon Kang1

  • 1Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.

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Summary
This summary is machine-generated.

A new algorithm accurately identifies acute ischemic stroke (AIS) cases from health insurance claims data. This tool aids in understanding stroke burden, though further refinement is needed.

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Area of Science:

  • Health Informatics
  • Neurology
  • Public Health

Background:

  • Accurate identification of acute ischemic stroke (AIS) is vital for stroke research using claims data.
  • Existing diagnostic codes (International Classification of Diseases 10th revision) show limitations in precision for AIS identification.

Purpose of the Study:

  • To develop and validate a novel algorithm for identifying AIS cases from National Health Insurance Service (NHIS) claims data.
  • To improve the accuracy of stroke case ascertainment in large-scale health databases.

Main Methods:

  • Extracted potential stroke cases (ICD-10 codes I60-I64) from NHIS claims data.
  • Linked claims data with a multicenter stroke registry (CRCS-K) for validation.
  • Developed an algorithm using 17 key identifiers (e.g., brain CT, MRI, treatments) and 37 conditions.

Main Results:

  • Identified 40,443 potential stroke cases; 31.7% were confirmed AIS.
  • The algorithm achieved a diagnostic accuracy of 82.4% in the development set and 81.4% in the validation set.
  • Key identifiers included neuroimaging, specific treatments, and medications.

Conclusions:

  • The developed algorithm shows promise for estimating stroke burden in Korea using claims data.
  • Further refinement of the algorithm is recommended for enhanced accuracy and broader application.