[Comparison of ICD 10 and AIS with the Development of a Method for Automated Conversion]

[A retrospective identification of severely injured patients using ICD 10 diagnoses codes : Part of the project "Quality of life and ability to work after severe trauma" (LeAf Trauma)]

BACKGROUND

Due to continuous improvements in treatment, more and more severely and seriously injured patients are surviving. The complexity of the injury patterns of these patients means that they are difficult to map in routine data.

AIM OF THE WORK

The aim of the data exploration was to identify ICD 10 diagnoses that show an association with an injury severity score (ISS) ≥ 16 and could therefore be used to operationalize severely injured patients in routine data.

MATERIAL AND METHODS

The coded four-digit ICD 10 S diagnoses and the calculated ISS of trauma patients from the Armed Forces Central Hospital Koblenz (BwZKrhs) and the University Hospital Düsseldorf (UKD) were analyzed using statistical association measures (phi and Cramer's V), linear regressions and machine learning methods (e.g., random forest).

RESULTS

The S diagnoses of facial, head, thoracic and pelvic injuries, associated with an ISS ≥ 16 were identified. Some S diagnoses showed an association with an ISS ≥ 16 in only 1 of the 2 datasets. Likewise, facial, head, thoracic and pelvic injuries were found in the subgroup of 18-55-year-old patients.

DISCUSSION

The current evaluations show that it is possible to identify ICD 10 S diagnoses that have a significant association with an ISS ≥ 16. According to the annual report of the trauma register of the German Society for Trauma Surgery (TR-DGU®), injuries with an abbreviated injury scale (AIS) ≥ 3 are particularly common in the head and thoracic regions.

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

Introduction

Methods

Results

Conclusion

Administrative data in pediatric critical care research-Potential, challenges, and future directions

Heterogenous patient populations with small case numbers constitute a relevant barrier to research in pediatric critical care. Prospective studies bring along logistic barriers and-if interventional-ethical concerns. Therefore, retrospective observational investigations, mainly multicenter studies or analyses of registry data, prevail in the field of pediatric critical care research. Administrative health care data represent a possible alternative to overcome small case numbers and logistic barriers. However, their current use is limited by a lack of knowledge among clinicians about the availability and characteristics of these data sets, along with required expertise in the handling of large data sets. Specifically in the field of critical care research, difficulties to assess the severity of the acute disease and estimate organ dysfunction and outcomes pose additional challenges. In contrast, trauma research has shown that classification of injury severity from administrative data can be achieved and chronic disease scores have been developed for pediatric patients, nurturing confidence that the remaining obstacles can be overcome. Despite the undoubted challenges, interdisciplinary collaboration between clinicians and methodologic experts have resulted in impactful publications from across the world. Efforts to enable the estimation of organ dysfunction and measure outcomes after critical illness are the most urgent tasks to promote the use of administrative data in critical care. Clever analysis and linking of different administrative health care data sets carry the potential to advance observational research in pediatric critical care and ultimately improve clinical care for critically ill children.

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.

[Challenges of digitalization in trauma care]

The increasing digitalization of social life opens up new possibilities for modern health care. This article describes innovative application possibilities that could help to sustainably improve the treatment of severe injuries in the future with the help of methods such as big data, artificial intelligence, intelligence augmentation, and machine learning. For the successful application of these methods, suitable data sources must be available. The TraumaRegister DGU® (TR-DGU) currently represents the largest database in Germany in the field of care for severely injured patients that could potentially be used for digital innovations. In this context, it is a good example of the problem areas such as data transfer, interoperability, standardization of data sets, parameter definitions, and ensuring data protection, which still represent major challenges for the digitization of trauma care. In addition to the further development of new analysis methods, solutions must also continue to be sought to the question of how best to intelligently link the relevant data from the various data sources.

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.

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.

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

Background

The 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.

Methods

Data 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).

Results

NTDB 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.

Conclusions

The 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.