Development of a robust mapping between AIS 2+ and ICD-9 injury codes

Area of Science:

• Traumatology
• Injury Biomechanics
• Public Health

Background:

• Motor vehicle crashes cause significant injuries and fatalities annually in the U.S.
• Discrepancies exist between Abbreviated Injury Scale (AIS) codes in crash data and International Classification of Diseases, version 9 (ICD-9) codes in hospital data.
• This disparity hinders comprehensive analysis of crash injury data.

Purpose of the Study:

• To establish a reliable one-to-one mapping between AIS and ICD-9 codes.
• To facilitate integrated analysis of motor vehicle crash and hospital datasets.
• To improve understanding and prevention of crash-related injuries.

Main Methods:

• Investigated multiple mapping approaches using AIS 2+ injuries from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS).
• Generated and compared mappings from the National Trauma Data Bank (NTDB), ICDMAP, and the Crash Injury Research and Engineering Network (CIREN).
• Evaluated mapping accuracy using quantitative metrics and expert physician review.

Main Results:

• The CIREN dataset yielded the most accurate AIS-to-ICD-9 code mapping.
• CIREN's manual coding of both AIS and ICD-9 codes contributed to higher mapping precision.
• Other datasets like NTDB and ICDMAP produced numerous, sometimes unrelated, code pairings.

Conclusions:

• The CIREN-derived mapping offers a robust solution for bridging crash and hospital injury data.
• This integrated data approach can enhance future motor vehicle injury research and prevention strategies.
• Accurate code mapping is crucial for leveraging diverse injury datasets effectively.