4.5 Article

Validation of Operational Definition to Identify Patients with Osteoporotic Hip Fractures in Administrative Claims Data

Journal

HEALTHCARE
Volume 10, Issue 9, Pages -

Publisher

MDPI
DOI: 10.3390/healthcare10091724

Keywords

hip fractures; osteoporosis; validation; claims data; diagnosis codes

Funding

  1. Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) - Ministry of Health & Welfare, Republic of Korea [HI18C0284]

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This study developed an operational definition to identify patients with osteoporotic hip fractures (OHFs) using diagnosis and procedure codes from health insurance claims data. The validity of the operational definition was assessed through a chart review. The results showed that the operational definition had high accuracy and discriminative power, making it a valid tool for detecting OHFs.
As incidences of osteoporotic hip fractures (OHFs) have increased, identifying OHFs has become important to establishing the medical guidelines for their management. This study was conducted to develop an operational definition to identify patients with OHFs using two diagnosis codes and eight procedure codes from health insurance claims data and to assess the operational definition's validity through a chart review. The study extracted data on OHFs from 522 patients who underwent hip surgeries based on diagnosis codes. Orthopedic surgeons then reviewed these patients' medical records and radiographs to identify those with true OHFs. The validities of nine different algorithms of operational definitions, developed using a combination of three levels of diagnosis codes and eight procedure codes, were assessed using various statistics. The developed operational definition showed an accuracy above 0.97 and an area under the receiver operating characteristic curve above 0.97, indicating excellent discriminative power. This study demonstrated that the operational definition that combines diagnosis and procedure codes shows a high validity in detecting OHFs and can be used as a valid tool to detect OHFs from big health claims data.

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