Validation of an ICD-9-CM and ICD-10-CM map to AIS 2005 Update 2008

Glerum, Kimberly M.; Zonfrillo, Mark R.

doi:10.1136/injuryprev-2017-042519

Cited by 38 publications

(42 citation statements)

References 4 publications

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“…In addition to different tools, there is considerable variation in the used ICD classifications and AIS versions. Glerum and Zonfrillo [15] have comparable results closest to ours. They compared the agreement of AIS scores between ICD-AIS map by AAAM and direct AIS-coding by trained coders.…”

Section: Comparison With Previous Studiessupporting

confidence: 89%

“…Furthermore, the AIS rating results were compared according to ISS body regions. The highest agreements of AIS scores between the ICD-AIS map and direct AIS coding were among injuries of extremities (84%) and external injuries (82%) and lowest among head and neck injuries (44%) [15]. The results differed from ours especially in external injuries and head and neck injuries.…”

Section: Comparison With Previous Studiescontrasting

confidence: 74%

“…However, they used a German version of ICD-10 (ICD-10-GM) which is, to some extent, more specific than the ICD-10 used in Finland. In general, several studies indicate that different kinds of automatic ICD-AIS conversion tools generates rather lower than higher numbers of serious injuries than direct AIS coding [6,[14][15][16]. The reliability of input data, and more specifically, the quality and accuracy of ICD injury coding, is essential for the successful conversion and has a significant impact on the results.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

“…Presumably the main reason for the different results was the use of ICD-10-CM injury codes; we used the more simple ICD-10. Furthermore, Glerum and Zonfrillo [15] used all AIS classes [16], whereas we used only two (AIS 1-2 and AIS 3+). However, they highlighted some of the same challenges as we did regarding determination of the severity of specific head injuries.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

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The reliability of the ICD-AIS map in identifying serious road traffic injuries from the Helsinki Trauma Registry

Airaksinen

Heinänen

Handolin

2019

Injury

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A B S T R A C TObjective: The EU has recommended that its member countries compile statistics on the number of serious road traffic injuries. In Finland, the number of seriously injured road traffic patients is assessed using the International Classification of Diseases, 10 th Revision (ICD-10) and the automatic conversion tool (ICD-AIS map) developed by The Association for the Advancement of Automotive Medicine (AAAM). The aim of this study was to assess how reliably the ICD-AIS map identifies both serious injuries and seriously injured patients due to road traffic accidents. Methods: Data was derived from the Helsinki Trauma Registry (HTR) and included 215 severe (New Injury Severity Score >15) trauma patients injured in road traffic accidents from the years 2016 and 2017. The severity ratings of injuries (Abbreviated Injury Scale, AIS 3+) and patients (Maximum Abbreviated Injury Scale, MAIS 3+) were determined by direct AIS coding of the HTR and were also generated by the ICD-AIS map based on ICD-10 injury codes. These two ratings were compared by injury mechanism and Injury Severity Score (ISS) body regions. The strength of agreement was described using Cohen's k. The most common injury codes with errors in severity rating by the ICD-AIS map were presented. Results: The number of seriously injured patients by the ICD-AIS map was 21% lower, and the number of serious injuries was 36% lower than the corresponding numbers by direct coding. The exact agreement of the injury ratings was 72% (k = 0.44, 95% CI 0.42-0.46). Most of the conversion errors were due to the simplicity of the ICD-10 codes used in Finland compared to those used in the ICD-AIS map (ICD-10-CM) and the missing codes from the ICD-AIS map. The most frequent misclassifications were due to multiple rib fractures, visceral organ injuries, some open fractures of extremities, and specific head injuries. Missing codes were most common in face, chest, and limb injuries. Conclusions: The ICD-10 injury codes presently used in Finland should be more specific to permit reliable conversion results by the ICD-AIS map. The problem with missing codes should be considered more closely. When implementing the ICD-11, all detailed injury codes should be introduced.

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Section: Comparison With Previous Studiessupporting

confidence: 89%

Section: Comparison With Previous Studiescontrasting

confidence: 74%

Section: Comparison With Previous Studiesmentioning

confidence: 99%

Section: Comparison With Previous Studiesmentioning

confidence: 99%

See 2 more Smart Citations

The reliability of the ICD-AIS map in identifying serious road traffic injuries from the Helsinki Trauma Registry

Airaksinen

Heinänen

Handolin

2019

Injury

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“…In 2016, AAAM representatives created a program to derive AIS 2005 (update 2008) scores from ICD-9-CM and ICD-10-CM rubrics [16]. The attempt to validate the method [17] showed that the agreement between ISS calculated by expert coders and map-derived ISS values was moderate, indicating that the conversion tool was not perfect.…”

Section: Introductionmentioning

confidence: 99%

Comparison of injury severity scores (ISS) obtained by manual coding versus “Two-step conversion” from ICD-9-CM

et al. 2019

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Background The International Classification of Diseases (ICD) is the standard diagnostic tool for classifying and coding diseases and injuries. The Abbreviated Injury Scale (AIS) is the most widely used injury severity scoring system. Although manual coding is considered the gold standard, it is sometimes unavailable or impractical. There have been many prior attempts to develop programs for the automated conversion of ICD rubrics into AIS codes. Objective To convert ICD, Ninth Revision, Clinical Modification (ICD-9-CM) codes into AIS 2005 (update 2008) codes via a derived map using a two-step process and, subsequently, to compare Injury Severity Score (ISS) resulting from said conversion with manually coded ISS values. Methods A cross-sectional retrospective study was designed in which medical records at the Hospital Universitario Marqués de Valdecilla of Cantabria (HUMV) and the Complejo Hospitalario of Navarra (CHN), both in Spain, were reviewed. Coding of injuries using AIS 2005 (update 2008) version was done manually by a certified AIS specialist and ISS values were calculated. ICD-9-CM codes were automatically converted into ISS values by another certified AIS specialist in a two-step process. ISS scores obtained from manual coding were compared to those obtained through this conversion process. Results The comparison of obtained through conversion versus manual ISS resulted in 396 concordant pairs (70.2%); the analysis of values according to ISS categories (ISS<9, ISS 9–15, ISS 16–24, ISS>24) showed 493 concordant pairs (87.4%). Regarding the criterion of “major trauma” patient (i.e., ISS> 15), 538 matching pairs (95.2%) were obtained. The conversion process resulted in underestimation of ISS in 112 cases (19.9%) and conversion was not possible in 136 cases (19%) for different reasons. Conclusions The process used in this study has proven to be a useful tool for selecting patients who meet the ISS>15 criterion for “major trauma”. Further research is needed to improve the conversion process.

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Identifying individual-based injury patterns in multi-trauma road users by using an association rule mining method

Fagerlind

Harvey

Humburg

et al. 2022

Accident Analysis & Prevention

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Validation of an ICD-9-CM and ICD-10-CM map to AIS 2005 Update 2008

Cited by 38 publications

References 4 publications

The reliability of the ICD-AIS map in identifying serious road traffic injuries from the Helsinki Trauma Registry

The reliability of the ICD-AIS map in identifying serious road traffic injuries from the Helsinki Trauma Registry

Comparison of injury severity scores (ISS) obtained by manual coding versus “Two-step conversion” from ICD-9-CM

Identifying individual-based injury patterns in multi-trauma road users by using an association rule mining method

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