Validating the Use of ICD-9 Code Mapping to Generate Injury Severity Scores

Fleischman, Ross J.; Mann, N. Clay; Dai, Mengtao; Holmes, James F.; Wang, N. Ewen; Haukoos, Jason S.; Hsia, Renee Y.; Rea, Thomas D.; Newgard, Craig D.

doi:10.1097/jtn.0000000000000255

Cited by 35 publications

(34 citation statements)

References 21 publications

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“…After initial testing, we reclassified code 850.11 to AIS = 2 as recommended by Fleischman et al (Fleischman et al, 2017), and codes 806.1 and 862.8 to AIS = 5 as recommended by DiBartolomeo et al (Di Bartolomeo et al, 2010)…”

Section: Methodsmentioning

confidence: 99%

“…The initial methodology for ICDPIC to perform this function was developed using ICD-9-CM codes and ISS data from the 2008 American College of Surgeons (ACS) National Trauma Data Bank (NTDB), and it has been validated by several independent researchers (Sears et al, 2014; Greene et al, 2015; Fleischman et al, 2017). Mapping from ICD codes will obviously not be as accurate as AIS or ISS scoring based on direct access to the actual person or primary medical record (Meredith et al, 2002; Di Bartolomeo et al, 2010), and might be more accurate if more recent versions of the AIS were applied, but it appears to be a reasonable substitute when other methods are impractical or unavailable.…”

Section: Introductionmentioning

confidence: 99%

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Open-access programs for injury categorization using ICD-9 or ICD-10

et al. 2018

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BackgroundThe article introduces Programs for Injury Categorization, using the International Classification of Diseases (ICD) and R statistical software (ICDPIC-R). Starting with ICD-8, methods have been described to map injury diagnosis codes to severity scores, especially the Abbreviated Injury Scale (AIS) and Injury Severity Score (ISS). ICDPIC was originally developed for this purpose using Stata, and ICDPIC-R is an open-access update that accepts both ICD-9 and ICD-10 codes.MethodsData were obtained from the National Trauma Data Bank (NTDB), Admission Year 2015. ICDPIC-R derives CDC injury mechanism categories and an approximate ISS (“RISS”) from either ICD-9 or ICD-10 codes. For ICD-9-coded cases, RISS is derived similar to the Stata package (with some improvements reflecting user feedback). For ICD-10-coded cases, RISS may be calculated in several ways: The “GEM” methods convert ICD-10 to ICD-9 (using General Equivalence Mapping tables from CMS) and then calculate ISS with options similar to the Stata package; a “ROCmax” method calculates RISS directly from ICD-10 codes, based on diagnosis-specific mortality in the NTDB, maximizing the C-statistic for predicting NTDB mortality while attempting to minimize the difference between RISS and ISS submitted by NTDB registrars (ISSAIS). Findings were validated using data from the National Inpatient Survey (NIS, 2015).ResultsNTDB contained 917,865 cases, of which 86,878 had valid ICD-10 injury codes. For a random 100,000 ICD-9-coded cases in NTDB, RISS using the GEM methods was nearly identical to ISS calculated by the Stata version, which has been previously validated. For ICD-10-coded cases in NTDB, categorized ISS using any version of RISS was similar to ISSAIS; for both NTDB and NIS cases, increasing ISS was associated with increasing mortality. Prediction of NTDB mortality was associated with C-statistics of 0.81 for ISSAIS, 0.75 for RISS using the GEM methods, and 0.85 for RISS using the ROCmax method; prediction of NIS mortality was associated with C-statistics of 0.75–0.76 for RISS using the GEM methods, and 0.78 for RISS using the ROCmax method. Instructions are provided for accessing ICDPIC-R at no cost.ConclusionsThe ideal methods of injury categorization and injury severity scoring involve trained personnel with access to injured persons or their medical records. ICDPIC-R may be a useful substitute when this ideal cannot be obtained.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Open-access programs for injury categorization using ICD-9 or ICD-10

et al. 2018

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“…(15) We used the ICD Programs for Injury Categorization,(16) a validated method to translate ICD9-CM diagnosis codes into ISS scores. (15,17) Some ICD9-CM codes (N=5961) were translated into ISS scores of 0 (905-908, 909.2, 909.4, 909.9, 920-924, 930-939, 958-959, 960-979, 980-994, 995.5, 995.80-995.85) and were included in the “minor” ISS category. We collected the following ED-level covariates: trauma center designation, academic status, and annual pediatric visit volume.…”

Section: Methodsmentioning

confidence: 99%

Variation in Computed Tomography Imaging for Pediatric Injury-Related Emergency Visits

Marin

Wang

Winger

et al. 2015

The Journal of Pediatrics

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Objectives To assess variation in the use of computed tomography (CT) for pediatric injury-related emergency department (ED) visits. Study design Retrospective cohort study of visits to 14 network-affiliated EDs from November 2010 through February 2013. Visits were identified by International Classification of Diseases, 9th revision, Clinical Modification diagnosis codes. Primary outcome was CT use. We used descriptive statistics and performed multivariable logistic regression to evaluate the association of patient and ED covariates on any and body region-specific CT use. Results Of the 80,868 injury-related visits, 11.4% included CT, and 28.4% of those had more than one study. Across EDs, CT use ranged from 7.6%- 25.5% of visits and did not correlate with institutional injury severity score (P=0.33) or admission/transfer rates (P=0.07). In multivariable analysis of non-pediatric EDs, trauma centers and non-academic EDs were associated with CT use. Higher pediatric volume was associated with any CT use, however, there was an inverse relationship between volume and non-head CT use. When the pediatric ED was included in multivariable modeling, the effect of level 1-3 trauma center designation remained, and the pediatric level 1 trauma center was less likely to use most body region-specific CTs. Conclusions There is wide variation in CT imaging for pediatric injury-related visits not solely attributable to case mix. Future work to optimize CT utilization should focus on additional factors contributing to imaging practices and interventions.

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“…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%

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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Validating the Use of ICD-9 Code Mapping to Generate Injury Severity Scores

Cited by 35 publications

References 21 publications

Open-access programs for injury categorization using ICD-9 or ICD-10

Open-access programs for injury categorization using ICD-9 or ICD-10

Variation in Computed Tomography Imaging for Pediatric Injury-Related Emergency Visits

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

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