New Methodology for an Expert-Designed Map From International Classification of Diseases (ICD) to Abbreviated Injury Scale (AIS) 3+ Severity Injury

Alcohol intake before injury and functional and survival outcomes after traumatic brain injury: Pan-Asian trauma outcomes study (PATOS)

There are controversies about the effects of alcohol intake shortly before injury on prognosis of traumatic brain injury (TBI) patients. We investigated the association between alcohol intake and functional/survival outcomes in TBI patients, and whether this effect varied according to age and sex. This was a prospective international multicenter cohort study using the Pan-Asian trauma outcomes study registry in Asian-Pacific countries, conducted on adult patients with TBI who visited participating hospitals. The main exposure variable was alcohol intake before injury, and the main outcomes were poor functional recovery (modified Rankin Scale score, 4-6) and in-hospital mortality. Multivariable logistic regression analyses were conducted to estimate the effects of alcohol intake on study outcomes. Interaction analysis between alcohol intake and age/sex were also performed. Among the study population of 12,451, 3263 (26.2%) patients consumed alcohol before injury. In multivariable logistic regression analysis, alcohol intake was associated with lower odds for poor functional recovery [4.4% vs 6.6%, a odds ratio (95% confidence interval): 0.68 (0.56-0.83)] and in-hospital mortality (1.9% vs 3.1%, 0.64 [0.48-0.86]). The alcohol intake had interaction effects with sex for poor functional recovery: 0.59 (0.45-0.75) for male and 0.94 (0.60-1.49) for female (P for-interaction < .01), whereas there were no interaction between alcohol intake and age. In TBI patients, alcohol intake before injury was associated with lower odds of poor functional recovery and in-hospital mortality, and these effects were maintained in the male group in the interaction analyses.

Improving identification of crash injuries: Statewide integration of hospital discharge and crash report data

OBJECTIVE

The availability of complete and accurate crash injury data is critical to prevention and intervention efforts. Relying solely on hospital discharge data or police crash reports may result in a biased undercount of injuries. Linking hospital data with crash reports may allow for a more robust identification of injuries and an understanding of which populations may be missed in an analysis of one source. We used the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse to examine the share of the entire crash-injured population identified in each of the two data sources, overall and by age, race/ethnicity, sex, injury severity, and road user type.

METHODS

We utilized 2016-2017 data from the NJ-SHO warehouse. We identified crash-involved individuals in hospital discharge data by applying the ICD-10-CM external cause of injury matrix. Among crash-involved individuals, we identified those with injury- or pain-related diagnosis codes as being injured. We also identified crash-involved individuals via crash report data and identified injuries using the KABCO scale. We jointly examined the two sources; injuries in the hospital discharge data were documented as being related to the same crash as injuries found in the crash report data if the date of the crash report preceded the date of hospital admission by no more than two days.

RESULTS

In total, there were 262,338 crash-involved individuals with a documented injury in the hospital discharge data or on the crash report during the study period; 168,874 had an injury according to hospital discharge data, and 164,158 had an injury in crash report data. Only 70,694 (26.9%) had an injury in both sources. We observed differences by age, race/ethnicity, injury severity, and road user type: hospital discharge data captured a larger share of those ages 65+, those who were Black or Hispanic, those with higher severity injuries, and those who were bicyclists or motorcyclists.

CONCLUSIONS

Each data source in isolation captures approximately two-thirds of the entire crash-injured population; one source alone misses approximately one-third of injured individuals. Each source undercounts people in certain groups, so relying on one source alone may not allow for tailored prevention and intervention efforts.

Agreement Between Standard and ICD-10-Based Injury Severity Scores

Introduction

Methods

Results

Conclusion

External validation of International Classification of Injury Severity Score to predict mortality in a Greek adult trauma population

INTRODUCTION

The International Classification of diseases- based Injury Severity Score (ICISS) obtained by empirically derived diagnosis-specific survival probabilities (DSPs) is the best-known risk-adjustment measure to predict mortality. Recently, a new set of pooled DSPs has been proposed by the International Collaborative Effort on Injury Statistics but it remains to be externally validated in other cohorts. The aim of this study was to externally validate the ICISS using international DSPs and compare its prognostic performance with local DSPs derived from Greek adult trauma population.

MATERIALS AND METHODS

This retrospective single-center cohort study enrolled adult trauma patients (≥ 16 years) hospitalized between January 2015 and December 2019 and temporally divided into derivation (n = 21,614) and validation cohorts (n = 14,889). Two different ICISS values were calculated for each patient using two different sets of DSPs: international (ICISSint) and local (ICISSgr). The primary outcome was in-hospital mortality. Models' prediction was performed using discrimination and calibration statistics.

RESULTS

ICISSint displayed good discrimination in derivation (AUC = 0.836 CI 95% 0.819-0.852) and validation cohort (AUC = 0.817 CI 95% 0.797-0.836). Calibration using visual analysis showed accurate prediction at patients with low mortality risk, especially below 30%. ICISSgr yielded better discrimination (AUC = 0.834 CI 95% 0.814-0.854 vs 0.817 CI 95% 0.797-0.836, p ˂ .05) and marginally improved overall accuracy (Brier score = 0.0216 vs 0.0223) compared with the ICISSint in the validation cohort. Incorporation of age and sex in both models enhanced further their performance as reflected by superior discrimination (p ˂ .05) and closer calibration curve to the identity line in the validation cohort.

CONCLUSION

This study supports the use of international DSPs for the ICISS to predict mortality in contemporary trauma patients and provides evidence regarding the potential benefit of applying local DSPs. Further research is warranted to confirm our findings and recommend the widespread use of ICISS as a valid measure that is easily obtained from administrative data based on ICD-10 codes.

Early Trauma Predictors of Mobility in People with Spinal Cord Injury

STUDY DESIGN

A retrospective cohort study.

OBJECTIVE

This study aims to assess the potential value of very early trauma variables such as Abbreviated Injury Scale (AIS) and the Injury Severity Score for predicting independent ambulation following a traumatic spinal cord injury (TSCI).

SUMMARY OF BACKGROUND DATA

Several models for prediction of ambulation early after TSCI have been published and validated. The vast majority rely on the initial examination of American Spinal Injury Association (ASIA) impairment scale and level of injury; however, in many locations and clinical situations this examination is not feasible early after the injury.

METHODS

Patient characteristics, trauma data, and ASIA scores on admission to rehabilitation were collected for each of the 144 individuals in the study. Outcome measure was the indoor mobility item of the Spinal Cord Independence Measure taken upon discharge from rehabilitation. Univariate and multivariable models were created for each predictor, Odds ratios (ORs) were obtained by a multivariable logistic regression analysis, and area under the receiver operator curve was calculated for each model.

RESULTS

We observed a significant correlation between the trauma variables and independent ambulation upon discharge from rehabilitation. Of the early variables, the AIS for the spine region showed the strongest correlation.

CONCLUSION

These findings support using preliminary trauma variables for early prognostication of ambulation following a TSCI, allowing for tailored individual interventions.Level of Evidence: 3.

Transforming the German ICD-10 (ICD-10-GM) into Injury Severity Score (ISS)-Introducing a new method for automated re-coding

Background

While potentially timesaving, there is no program to automatically transform diagnosis codes of the ICD-10 German modification (ICD-10-GM) into the injury severity score (ISS).

Objective

To develop a mapping method from ICD-10-GM into ICD-10 clinical modification (ICD-10-CM) to calculate the abbreviated injury scale (AIS) and ISS of each patient using the ICDPIC-R and to compare the manually and automatically calculated scores.

Methods

Between January 2019 and June 2021, the most severe AIS of each body region and the ISS were manually calculated using medical documentation and radiology reports of all major trauma patients of a German level I trauma centre. The ICD-10-GM codes of these patients were exported from the electronic medical data system SAP, and a Java program was written to transform these into ICD-10-CM codes. Afterwards, the ICDPIC-R was used to automatically generate the most severe AIS of each body region and the ISS. The automatically and manually determined ISS and AIS scores were then tested for equivalence.

Results

Statistical analysis revealed that the manually and automatically calculated ISS were significantly equivalent over the entire patient cohort. Further sub-group analysis, however, showed that equivalence could only be demonstrated for patients with an ISS between 16 and 24. Likewise, the highest AIS scores of each body region were not equal in the manually and automatically calculated group.

Conclusion

Though achieving mapping results highly comparable to previous mapping methods of ICD-10-CM diagnosis codes, it is not unrestrictedly possible to automatically calculate the AIS and ISS using ICD-10-GM codes.

Development of an expert derived ICD-AIS map for serious AIS3+ injury identification

Objective: The objective of the mapping project was to develop an expert derived map between the International Statistical Classification of Diseases and Related Health Problems (ICD) clinical modifications (CM) and the Abbreviated Injury Scale (AIS) to be able to relate AIS severity to ICD coded data road traffic collision data in EU datasets. The maps were developed to enable the identification of serious AIS3+ injury and provide details of the mapping process for assumptions to be made about injury severity from mass datasets. This article describes in detail the mapping process of the International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) and the International Classification of Diseases Tenth Revision, Clinical Modification (ICD-10-CM) codes to the Abbreviated Injury Scale 2005, Update 2008 (AIS08) codes to identify injury with an AIS severity of 3 or more (AIS3+ severity) to determine 'serious' (MAIS3+) road traffic injuries.Methods: Over 19,000 ICD codes were mapped from the following injury categories; injury ICD-9-CM (Chapter 17) codes between '800 and 999.9' and injury ICD-10-CM (Chapter 19) 'S' and 'T' prefixed codes were reviewed and mapped to an AIS08 category and then relate the severity to three groups; AIS3+, AIS < =2 and AIS 9 (no-map). The mapping was undertaken by ICD coding experts and certified AIS specialists from Europe, North America, Australia and Canada in face-to-face working groups and subsequent webinars between May 2014 and October 2015. During the process, the business rules were documented to define guidelines for the mapping process and enable inter-rater discrepancies to be resolved.Results: In total 2,504 ICD-9-CM codes were mapped to the AIS, of which 780 (31%) were assigned an AIS3+ severity. For the16,508 ICD-10-CM mapped codes a total of 2,323 (14%) were assigned an AIS3+ severity. Some 17% (n = 426) and 27% (n = 4,485) of ICD-9-CM and ICD-10-CM codes respectively were assigned to AIS9 (no-map) following the mapping process. It was evident there were 'problem' codes that could not be easily mapped to an AIS code to reflect severity. Problem maps affect the specificity of the map and severity when used to translate historical data in large datasets.Conclusions: The Association for the Advancement in Automotive Medicine, AAAM-endorsed expert-derived map offers a unique tool to road safety researchers to establish the number of MAIS3+ serious injuries occurring on the roads. The detailed process offered in this paper will enable researchers to understand the decision making and identify limitations when using the AIS08/ICD map on country-specific data. The results could inform protocols for dealing with problem codes to enable country comparisons of MAIS3+ serious injury rates.

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

OBJECTIVE

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 κ. 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% (κ = 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.

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

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.

Characteristics of Single Vehicle Crashes with a Teen Driver in South Carolina, 2005-2008

OBJECTIVE

Teens' crash risk is highest in the first years of independent driving. Circumstances surrounding fatal crashes have been widely documented, but less is known about factors related to nonfatal teen driver crashes. This study describes single vehicle nonfatal crashes involving the youngest teen drivers (15-17 years), compares these crashes to single vehicle nonfatal crashes among adult drivers (35-44 years) and examines factors related to nonfatal injury producing crashes for teen drivers.

METHODS

Police crash data linked to hospital inpatient and emergency department data for 2005-2008 from the South Carolina Crash Outcomes Data Evaluation System (CODES) were analyzed. Nonfatal, single vehicle crashes involving passenger vehicles occurring on public roadways for teen (15-17 years) drivers were compared with those for adult (35-44 years) drivers on temporal patterns and crash risk factors per licensed driver and per vehicle miles traveled. Vehicle miles traveled by age group was estimated using data from the 2009 National Household Travel Survey. Multivariable log-linear regression analysis was conducted for teen driver crashes to determine which characteristics were related to crashes resulting in a minor/moderate injury or serious injury to at least one vehicle occupant.

RESULTS

Compared with adult drivers, teen drivers in South Carolina had 2.5 times the single vehicle nonfatal crash rate per licensed driver and 11 times the rate per vehicle mile traveled. Teen drivers were nearly twice as likely to be speeding at the time of the crash compared with adult drivers. Teen driver crashes per licensed driver were highest during the afternoon hours of 3:00-5:59 pm and crashes per mile driven were highest during the nighttime hours of 9:00-11:59 pm. In 66% of the teen driver crashes, the driver was the only occupant. Crashes were twice as likely to result in serious injury when teen passengers were present than when the teen driver was alone. When teen drivers crashed while transporting teen passengers, the passengers were >5 times more likely to all be restrained if the teen driver was restrained. Crashes in which the teen driver was unrestrained were 80% more likely to result in minor/moderate injury and 6 times more likely to result in serious injury compared with crashes in which the teen driver was restrained.

CONCLUSIONS

Despite the reductions in teen driver crashes associated with Graduated Driver Licensing (GDL), South Carolina's teen driver crash rates remain substantially higher than those for adult drivers. Established risk factors for fatal teen driver crashes, including restraint nonuse, transporting teen passengers, and speeding also increase the risk of nonfatal injury in single vehicle crashes. As South Carolina examines strategies to further reduce teen driver crashes and associated injuries, the state could consider updating its GDL passenger restriction to either none or one passenger <21years and dropping the passenger restriction exemption for trips to and from school. Surveillance systems such as CODES that link crash data with health outcome data provide needed information to more fully understand the circumstances and consequences of teen driver nonfatal crashes and evaluate the effectiveness of strategies to improve teen driver safety.

The ability of the ICD-AIS map to identify seriously injured patients in road traffic accidents-A study from Finland

OBJECTIVE

In Finland, the severity of road traffic injuries is determined using the International Classification of Diseases, 10th Revision, Finnish Modification (ICD-10-FM) injury codes from Finnish Hospital Discharge data 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 evaluate the ability of the ICD-AIS map to identify seriously injured patients due to traffic accidents in Finnish injury data by comparing the severity rating generated by an expert and by the ICD-AIS map.

METHODS

Our data came from the North Kymi Hospital (level 2 trauma center at the time of the study). The data included 574 patients who were injured in traffic accidents during 2 years. The severity rating (Maximum Abbreviated Injury Scale [MAIS] 3+) of each patient was recorded retrospectively by an expert based on information from patient records. In addition, the rating was generated from ICD-10 injury codes by the ICD-AIS map conversion tool. These 2 ratings were compared by road user categories and the strength of agreement was described using Cohen's kappa.

RESULTS

The proportion of seriously injured patients was 10.1% as defined by the expert and 6.6% as generated by the ICD-AIS map; exact agreement was 65.5%. The highest concordance was for pedestrians (exact agreement 100%) and the weakest for moped drivers and motorcyclists (46.7%). Furthermore, the overall strength of agreement of the severity ratings (slightly or seriously injured) between the expert and the ICD-AIS map was good (κ = 0.70). Most (65%) of the conversion problems were misclassifications caused by the simplicity of the Finnish ICD-10 injury codes compared to the injury codes used in the ICD-AIS map. In Finland, the injuries are recorded mainly with 4-digit codes and, infrequently, with 5-digit codes, whereas the ICD-AIS map defines up to 6-digit codes.

CONCLUSIONS

For this sample of simplified ICD-10-FM codes, the ICD-AIS map underestimated the number of seriously injured patients. The mapping result could be improved if at least open and closed fractures of extremities and visceral contusions and ruptures had separate codes. In addition, there were a few injury codes that should be considered for inclusion in the map.

Complications and Mortality Among Correctly Triaged and Undertriaged Severely Injured Older Adults With Traumatic Brain Injuries

Determining differences in clinical outcomes of older adults treated at trauma centers (TCs) and nontrauma centers (NTCs) is imperative considering their persistent undertriage and the projected costs of fixing the problem. This study compared the incidence and predictors of complications and mortality among brain-injured older adults treated at TCs and NTCs. This secondary analysis of New York inpatient data included patients aged 55+ years, primary brain injury diagnosis, and acute care hospital admission. Interfacility transfers and nontraumatic brain injuries were excluded. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes identified complications and mortality. Injury severity was determined by mapping ICD-9-CM diagnoses to Abbreviated Injury Scale 2005 Revision 2008 dictionary scores. A subgroup analysis of 1,594 patients with New Injury Severity Scores greater than 15 was performed to examine complications and mortality. This study included 7,138 patients who met inclusion criteria. Predictors of subgroup complications included chronic renal failure, odds ratio (OR) = 2.251 (confidence interval [CI] = 1.470-3.447), p < .001; major operating room procedure, OR = 2.349 (CI = 1.679-3.285), p < .001; number of diagnoses, OR = 1.201 (CI = 1.158-1.245), p < .001; and number of procedures, OR = 1.119 (CI = 1.077-1.162), p £ .001. Mortality predictors included age, OR = 1.031 (CI = 1.017-1.045), p < .001; preexisting coagulopathy, OR = 1.753 (C = 1.130-2.719), p = .012; number of procedures, OR = 1.122 (CI = 1.081-1.166), p < .001; acute renal failure, OR = 3.114 (CI = 1.672-5.797), p < .001; systemic inflammatory response syndrome, OR = 4.058 (CI = 1.463-11.258), p = .007; adult respiratory distress syndrome, OR = 3.179 (CI = 1.673-6.041), p < .001; and subarachnoid bleed, OR = 2.667 (CI = 1.415-5.029), p = .002. Nearly 23% of the severely/critically injured patients experienced 1 or more complications. Incidence of complications was low and comparable for TCs and NTCs. The proportion of deaths was slightly higher at TCs but not significant. The most prevalent complications carry a high mortality risk.

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

Although the Abbreviated Injury Scale (AIS) is the most widely used severity scoring system for traumatic injuries, hospitals are required to document and bill based on the International Classification of Diseases (ICD). An expert panel recently developed a map between ICD-9-CM and ICD-10-CM to AIS 2005 Update 2008. This study aimed to validate the recently developed map using a large trauma registry. The map demonstrated moderate to substantial agreement for maximum AIS (MAIS) scores per body region based on expert chart review versus map-derived values (range: 44%–86%). Injury Severity Scores (ISSs) calculated from expert coders versus map-derived values were also compared and demonstrated moderate agreement (ICD-9-CM: 48%, ICD-10-CM: 54%). Although not a perfect conversion tool, the new ICD-AIS map provides a systematic method to assign injury severity for datasets with only ICD-9-CM and ICD-10-CM codes available and can be used for future injury-related research and data analysis.

Development of an expert based ICD-9-CM and ICD-10-CM map to AIS 2005 update 2008

OBJECTIVE

This article describes how maps were developed from the clinical modifications of the 9th and 10th revisions of the International Classification of Diseases (ICD) to the Abbreviated Injury Scale 2005 Update 2008 (AIS08). The development of the mapping methodology is described, with discussion of the major assumptions used in the process to map ICD codes to AIS severities. There were many intricacies to developing the maps, because the 2 coding systems, ICD and AIS, were developed for different purposes and contain unique classification structures to meet these purposes.

METHODS

Experts in ICD and AIS analyzed the rules and coding guidelines of both injury coding schemes to develop rules for mapping ICD injury codes to the AIS08. This involved subject-matter expertise, detailed knowledge of anatomy, and an in-depth understanding of injury terms and definitions as applied in both taxonomies. The official ICD-9-CM and ICD-10-CM versions (injury sections) were mapped to the AIS08 codes and severities, following the rules outlined in each coding manual. The panel of experts was composed of coders certified in ICD and/or AIS from around the world. In the process of developing the map from ICD to AIS, the experts created rules to address issues with the differences in coding guidelines between the 2 schemas and assure a consistent approach to all codes.

RESULTS

Over 19,000 ICD codes were analyzed and maps were generated for each code to AIS08 chapters, AIS08 severities, and Injury Severity Score (ISS) body regions. After completion of the maps, 14,101 (74%) of the eligible 19,012 injury-related ICD-9-CM and ICD-10-CM codes were assigned valid AIS08 severity scores between 1 and 6. The remaining 4,911 codes were assigned an AIS08 of 9 (unknown) or were determined to be nonmappable because the ICD description lacked sufficient qualifying information for determining severity according to AIS rules. There were also 15,214 (80%) ICD codes mapped to AIS08 chapter and ISS body region, which allow for ISS calculations for patient data sets.

CONCLUSION

This mapping between ICD and AIS provides a comprehensive, expert-designed solution for analysts to bridge the data gap between the injury descriptions provided in hospital codes (ICD-9-CM, ICD-10-CM) and injury severity codes (AIS08). By applying consistent rules from both the ICD and AIS taxonomies, the expert panel created these definitive maps, which are the only ones endorsed by the Association for the Advancement of Automotive Medicine (AAAM). Initial validation upheld the quality of these maps for the estimation of AIS severity, but future work should include verification of these maps for MAIS and ISS estimations with large data sets. These ICD-AIS maps will support data analysis from databases with injury information classified in these 2 different systems and open new doors for the investigation of injury from traumatic events using large injury data sets.