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Development of administrative data algorithms to identify patients with critical limb ischemia.
Journal article

Development of administrative data algorithms to identify patients with critical limb ischemia.

  • Bekwelem W Lillehei Heart Institute and Cardiovascular Division, University of Minnesota Medical School, Minneapolis, MN, USA.
  • Bengtson LG Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, MN, USA.
  • Oldenburg NC Lillehei Heart Institute and Cardiovascular Division, University of Minnesota Medical School, Minneapolis, MN, USA.
  • Winden TJ Center for Healthcare Research and Innovation, Allina Health, Minneapolis, MN, USA.
  • Keo HH Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, MN, USA Division of Angiology, Kantonsspital Aarau AG, Aarau, Switzerland.
  • Hirsch AT Lillehei Heart Institute and Cardiovascular Division, University of Minnesota Medical School, Minneapolis, MN, USA.
  • Duval S Lillehei Heart Institute and Cardiovascular Division, University of Minnesota Medical School, Minneapolis, MN, USA sueduval@umn.edu.
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  • 2014-12-03
Published in:
  • Vascular medicine (London, England). - 2014
administrative data
critical limb ischemia
peripheral artery disease
registries
Adult
Aged
Aged, 80 and over
Algorithms
Extremities
Female
Humans
International Classification of Diseases
Ischemia
Male
Middle Aged
Peripheral Arterial Disease
Registries
Risk Factors
English Administrative data have been used to identify patients with various diseases, yet no prior study has determined the utility of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-based codes to identify CLI patients. CLI cases (n=126), adjudicated by a vascular specialist, were carefully defined and enrolled in a hospital registry. Controls were frequency matched to cases on age, sex and admission date in a 2:1 ratio. ICD-9-CM codes for all patients were extracted. Algorithms were developed using frequency distributions of these codes, risk factors and procedures prevalent in CLI. The sensitivity for each algorithm was calculated and applied within the hospital system to identify CLI patients not included in the registry. Sensitivity ranged from 0.29 to 0.92. An algorithm based on diagnosis and procedure codes exhibited the best overall performance (sensitivity of 0.92). Each algorithm had differing CLI identification characteristics based on patient location. Administrative data can be used to identify CLI patients within a health system. The algorithms, developed from these data, can serve as a tool to facilitate clinical care, research, quality improvement, and population surveillance.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://sonar.ch/global/documents/512
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