Trauma icd codes

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ICD Injury Matrices

What are the matrices?

The ICD Injury matrices are frameworks designed to organize ICD coded injury data into meaningful groupings. The matrices were developed specifically to facilitate national and international comparability in the presentation of injury statistics.

Why are there so many matrices?

Injuries can be described in the ICD in two ways; either (1) as “external cause” which describes the cause and intent in a single code or (2) as the “nature of injury” which describes the body and nature of injury in a single code. There are multiple revisions of the ICD. There are also clinical modifications which are more detailed for use with morbidity data. There is also a mapping between the revisions of the ICD.

Which ICD revisions, clinical modifications, and injury codes have matrices available?

Revisions/modificationsExternal causeNature of injury
ICD–9ICD–9 External causeNot yet developed
ICD–9–CMICD–9–CM External causeBarell Matrix
ICD–10ICD–10 External causeInjury Mortality Diagnosis Matrix
Map ICD–9 To ICD–10ICD–9 modified to be consistent with ICD–10

What are External Cause of Injury (E–code) codes?

The external cause of injury describes the vector that transfers the energy to the body (e.g. fall, motor vehicle traffic accident, or poisoning) and the intent of the injury (e.g. whether the injury was inflicted purposefully).

External cause–of–injury codes (E–codes) are the ICD codes used to classify injury incidents by mechanism (e.g., motor vehicle, fall, struck by/against, firearm, or poisoning) and intent (e.g., unintentional, homicide/assault, suicide/self–harm, or undetermined) and. Sometimes the external cause is referred to as the “mechanism of injury” and the intent is referred to as the “manner of death”.

In ICD–9, the external cause of injury codes are included in a Supplemental Classification and are designated with as “E800–E999.9”. In ICD–10, external cause of injury codes are in Chapter 20 and begin with the letter V,X,W, and Y.

What are the injury diagnosis codes?

The injury diagnosis codes (or the nature of injury codes) are the ICD codes used to classify injury the body region (e.g. head, and the nature of injury (e.g. fracture, laceration). In ICD–9, the nature of injury codes are included in a Chapter XVII and are designated by codes 800–999. In ICD–10, nature of injury codes are in Chapter 19 and begin with the letter S or T.

The Barell matrix for ICD–9CM codes and the Injury Mortality Diagnosis Matrix for ICD–10 codes are two–dimensional arrays describing both the body region and nature of the injury.

What are External Cause of Injury (E–code) matrices?

The External Cause of Injury Matrix is a two–dimensional array designed to present both the mechanism and manner of the injury. External Cause of Injury matrices have been developed for ICD-9, ICD-9CM and ICD-10. The first E-code matrix was developed for the ICD–9 external cause codes. It was jointly developed by the Injury Control and Emergency Health Services section of the American Public Health Association and the International Collaborative Effort (ICE) on Injury Statistics.

Modifications to the ICD–9 external cause of injury matrix are available which make the matrix more compatible with the ICD–10 matrix. For more information see: How to map ICD–9 To ICD–10 for external causes .

The ICD–10 injury mortality framework for external cause–of–injury was developed to be as consistent as possible with the ICD–9 external cause–of–injury matrix. Representatives from NCHS, International Collaborative Effort on Injury Statistics, and Injury Control and Emergency Health Services section of the American Public Health Association participated in its development. In some cases, the ICD–10 external cause–of–injury mortality codes are different than the ICD–9 codes. When the codes vary, more often the codes allow more detail, but in some cases, less detail is provided. The ICD–10 matrix development was guided by logic and internal consistency rather than directly calculated ICD–9 to ICD–10 comparability ratios from dual coded data.

Several changes were made to the ICD–10 matrix that warrant attention:

  • Two rows have been added. The first is labeled “All transport” and it includes all transport related deaths that were classified as unintentional: suicide, homicide, intent undetermined, and operations of war. In ICD–9, the codes for suicide and intent undetermined by crashing of a motor vehicle were included with motor vehicle traffic injuries. There is no indication in the actual codes that these are traffic deaths. The second row, “Other land transport” was added to accommodate new codes in ICD–10.
  • A change was made to the transportation and drowning categories. The ICD–10 codes for water transportation–related drowning, V90 and V92, are included with the “other transport” codes rather than with the drowning codes. In the ICD–9 version of the matrix, the comparable codes, E830 and E832, were included with drowning. This change was made to be consistent with the categorization of other mechanisms of injury (i.e., falls, fires, and machinery) involved with water transport–related injuries.
  • In the ICD–9 matrix, E846–E848 “Vehicle accidents not elsewhere classifiable” were categorized with other codes into the category “Other specified and classifiable”. However, with the additional transportation categories in ICD–10, and to be consistent with the NCHS 113 Cause–of–Death list, ICD–10 codes V98–V99, “Other and unspecified transport accident”, are included with other transportation codes (including water transport and air and space transport related accidents) in a group V90–V99, making them part of all transportation related accidents.

NOTE: The November 2002 version also includes the newly developed US ICD–10 codes for terrorism. The November 2002 version of the matrix shows a correction in the motor vehicle traffic category. Codes V81.1 and V82.1 were moved from the occupant codes to the other codes. In addition, codes X82 for suicide, Y03 homicide and Y32 intent undetermined for the crashing of a motor vehicle are included in the group, “Other land transport” and code Y36.1 is included with “Other transport.” In the preliminary matrix, they were only included in the row for “All transportation.”

How to map ICD–9 To ICD–10 for external causes

About every 10 to 20 years, the ICD is revised to stay abreast of advances in medical science and changes in medical terminology. ICD–10 was implemented in the US in 1999. The ICD–9 External Cause of Injury matrix was modified to be consistent with the ICD–10 matrix.

Classification and rule changes affect cause‑of‑death trend data by shifting deaths away from some cause–of–death categories and into others. ICD–10 and ICD–9 Comparability ratios are based on a comparability study in which the same deaths were coded by both the Ninth and Tenth Revisions. The comparability ratio was calculated by dividing the number of deaths classified by ICD–10 by the number of deaths classified by ICD–9. Comparability ratios measure the effect of changes in classification and coding rules and were calculated for all external causes of injury based on the external cause of injury mortality matrix. For information on all causes of death comparability ratios. See Comparability of Cause–of–death Between ICD Revisions

What is the Barell Matrix (ICD–9–CM)?

The Barell Injury Diagnosis matrix is a two–dimensional array of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD–9–CM) diagnosis codes for injury (updated as of 2002) grouped by body region of the injury and the nature of the injury. The ICD–10 matrix is referred to as the Injury Mortality Matrix.

The Barell matrix provides a standard format for reporting injury data. This injury diagnosis matrix is a product of the International Collaborative Effort (ICE) on Injury Statistics. Guiding its development was the work of the late Vita Barell, of the Health Services Research Department in the Gertner Institute, Tel Hashomer, Israel and Ellen MacKenzie of the Johns Hopkins Center for Injury Research and Policy. The matrix is based on ICD–9–CM coded data and not on data directly obtained from patients. Future plans include a version of the matrix based on 3–digit ICD–9–CM codes that can be used for multiple cause of death analyses (when detailed 5–digit codes are often not available). In addition, once ICD–10–CM is adopted for use, the matrix will be ‘translated’ into those appropriate codes. A complete discussion of the matrix including guidelines for use and data analysis was published in the journal Injury Prevention (June 2002). The matrix was adopted for use by the State and Territorial Injury Prevention Directors Association and recommended as the basis for defining injury hospitalizations.

Excluded from the matrix are ICD–9–CM codes for adverse effects and complications of care. There is disagreement within the “Injury Community” as to whether these should be included. For the time being, they are excluded. This can certainly be reconsidered in future versions of the matrix.

What is the Injury Mortality Diagnosis (IMD) Matrix (ICD–10)?

The ICD–10 Injury Mortality Diagnosis (IMD) matrix is a framework designed to organize injury diagnosis mortality data into meaningful groupings by body region and nature of injury. The ICD–9–CM matrix is referred to as the Barell matrix. Injury diagnoses describe the body region and nature of the injury mentioned on the death certificate that are the injuries sustained as a result of the underlying external cause of injury death. The IMD matrix categorizes the nearly 1,200 injury diagnosis codes from ICD–10’s Chapter XIX (S and T codes, excluding adverse effects and complications of medical and surgical care [T79, T80–T88, T98.3]) by body region and nature of the injury. At its most detailed level, the ICD–10 matrix has 19 nature–of–injury categories and 43 body–region categories. For most analyses of mortality data, similar categories can be aggregated to reduce the categories to those most meaningful for mortality. The detailed structure can be readily collapsed into a more meaningful matrix for mortality using 16 nature–of–injury diagnosis categories and 17 body region of injury diagnosis categories. Categories for both axes were combined based on characteristics of the body region (e.g., foot and ankle injuries are part of “Other lower extremities”) as well as the number of injury diagnoses mentioned in a category (e.g. if there were too few). The latter was generally a reflection of the low lethality of the diagnosis (sprains and strains, for example). The body regions can be further combined into five groups; this is often useful for analyses using additional dimensions, such as external cause or age. The ICD–10 IMD Matrix is similar in structure to the Barell Injury Diagnosis Matrix that categorizes ICD–9–CM injury morbidity codes by body region and nature of injury. However, the ICD–10 matrix is adapted for use with mortality data, which tend to be less detailed than morbidity data, and also takes into account important changes related to the revision of the ICD classification scheme.

Suggested citations for the matrices

External Cause–of–Injury (E–code) Matrices

ICD–10 suggested citation: NCHS. ICD–10: External cause of injury mortality matrix [online]. Available from: /nchs/injury/injury_matrices.htm

ICD–9 & ICD–9–CM suggested citation: CDC. Recommended framework for presenting injury mortality data. MMWR 46 (RR-14) Centers for Disease Control and Prevention. Available from: 1997.
ICD–9 modified to be consistent with ICD–10 External Cause of Injury

Injury Diagnosis Matrices

ICD–10 (IMD Matrix) suggested citation: L.A. Fingerhut and M. Warner, The ICD–10 Injury Mortality Diagnosis Matrix, Injury Prevention, 2006;12;24-29

ICD–9–CM (Barell Matrix) suggested citation: Barell V, Aharonson-Daniel L, Fingerhut LA, MacKenzie EJ, et al. An introduction to the Barell body region by nature of injury diagnosis matrix. Inj Prev 8:91–6. 2002.

Other references for the matrices

Bergen G , Chen LH, Warner M, Fingerhut LA. Injury in the United States: 2007 Chartbook. Hyattsville, MD: National Center for Health Statistics. 2008. Cdc-pdf[PDF – 6.7 MB]

Miniño AM, Anderson RN, Fingerhut LA, Boudreault MA, Warner M. Deaths: Injuries, 2002. National vital statistics reports; vol 54 no 10. Hyattsville, Maryland: National Center for Health Statistics. 2006. Cdc-pdf[PDF – 4.8 MB]

Anderson RN, Miniño AM, Fingerhut LA, Warner M, Heinen MA. Deaths: Injuries, 2001. National vital statistics reports; vol 52 no 21. Hyattsville, Maryland: National Center for Health Statistics.2004. Cdc-pdf[PDF – 4.5 MB]

ICD codes for matrices and SAS statements

For ICD codes for each of the matrices and the SAS statements, see Tools for Classifying ICD Codes.


IMP-ICDX: an injury mortality prediction based on ICD-10-CM codes

  • Research article
  • Open Access
  • Published:

World Journal of Emergency Surgeryvolume 14, Article number: 46 (2019) Cite this article

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The International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) Injury Severity Score (ICISS) is a risk adjustment model when injuries are recorded using ICD-9-CM coding. The trauma mortality prediction model (TMPM-ICD9) provides better calibration and discrimination compared with ICISS and injury severity score (ISS). Though TMPM-ICD9 is statistically rigorous, it is not precise enough mathematically and has the tendency to overestimate injury severity. The purpose of this study is to develop a new ICD-10-CM injury model which estimates injury severities for every injury in the ICD-10-CM lexicon by a combination of rigorous statistical probit models and mathematical properties and improves the prediction accuracy.


We developed an injury mortality prediction (IMP-ICDX) using data of 794,098 patients admitted to 738 hospitals in the National Trauma Data Bank from 2015 to 2016. Empiric measures of severity for each of the trauma ICD-10-CM codes were estimated using a weighted median death probability (WMDP) measurement and then used as the basis for IMP-ICDX. ISS (version 2005) and the single worst injury (SWI) model were re-estimated. The performance of each of these models was compared by using the area under the receiver operating characteristic (AUC), the Hosmer-Lemeshow (HL) statistic, and the Akaike information criterion statistic.


IMP-ICDX exhibits significantly better discrimination (AUCIMP-ICDX, 0.893, and 95% confidence interval (CI), 0.887 to 0.898; AUCISS, 0.853, and 95% CI, 0.846 to 0.860; and AUCSWI, 0.886, and 95% CI, 0.881 to 0.892) and calibration (HLIMP-ICDX, 68, and 95% CI, 36 to 98; HLISS, 252, and 95% CI, 191 to 310; and HLSWI, 92, and 95% CI, 53 to 128) compared with ISS and SWI. All models were improved after the extension of age, gender, and injury mechanism, but the augmented IMP-ICDX still dominated ISS and SWI by every performance.


The IMP-ICDX has a better discrimination and calibration compared to ISS. Therefore, we believe that IMP-ICDX could be a new viable trauma research assessment method.


Trauma score methods can be divided into two categories of systems. First, the injury severity score (ISS), the new injury severity score (NISS), the tangent injury severity score (TISS), the trauma mortality prediction model (TMPM), and injury mortality prediction (IMP) [1,2,3,4,5] score methods based on the Abbreviated Injury Scale (AIS) [6] lexicon. Their ability of predicting trauma death is also improved [2,3,4,5, 7]. However, the AIS codes must be evaluated by trauma surgeon experts. In these circumstances, a great deal of manpower and material resources is consumed. It is difficult for developed countries, let alone developing ones. These situations hinder the trauma score in-depth research and popularization. Second, the International Classification of Diseases Ninth Edition (ICD-9-CM) Injury Severity Score (ICISS) and the trauma mortality prediction model (TMPM)-ICD9 score methods based on ICD-9-CM lexicon [8, 9]. ICD-9-CM codes are the common disease diagnosis codes around the world. Currently, most countries and regions apply the updated ICD-10-CM. The number of diagnostic categories available is approximately over 9000, which is more than the number of AIS code categories. Although ICD-10-CM codes are not similar to AIS which implies injury severities, each diagnosis has implied the information of anatomy trauma, a variety of disease severity, and the possibility of mortality. ICD-10-CM codes also include the possibility of death, such as traumatic hemorrhage of right cerebrum with loss of consciousness of 30 min or less, initial encounter; displaced fracture of base of neck of right femur, initial encounter for closed fracture; and major laceration of liver, initial encounter.

The ICISS is the product of empirically derived survival risk ratios (SRRs) for trauma ICD-9-CM codes [8]. SRR is a survival rate of all trauma patients in a specific trauma ICD-9-CM code. It contains survival rates of patients who sustained both a single injury and multiple injuries. Although ICISS is better than the ISS and NISS in the prediction ability of death [8, 10, 11], the SRR underestimates the survival rate of patients with a single injury and overestimates survival rate of patients with multiple injuries. Therefore, ICISS is inaccurate for the prediction of mortality (survival).

TMPM-ICD9 [9] derived an empirical severity value for each ICD-9-CM code that is called the model-averaged regression coefficient (MARC) which is similar to TMPM [4]. Then, calculating the TMPM-ICD9 value according to MARC values by using a special formula. The TMPM-ICD9 is better than the ICISS as a predictor of mortality [7, 9]. Researchers concluded that the TMPM-ICD9 outperforms the ISS and NISS in mortality prediction [7, 12]. TMPM-ICD9 is statistically rigorous, but it is not accurate enough in mathematics. There is a tendency to overestimate the severity of the injury [12].

We propose a new ICD-10-CM injury model which replaces the sole regression-based approach. Then we compare the performance of injury mortality prediction (IMP-ICDX), a new mortality prediction model based on these empiric injury severities, with ISS and single worst injury (SWI) models. Our objective was that the IMP-ICDX would provide a more accurate prediction of mortality than other existing scoring systems.


Data source

The patients came from the National Trauma Data Bank (NTDB) hospitalized between 2015 and 2016. Available information included patient demographics, ICD-10-CM diagnostic and injury codes (national clinical revision in American), mechanism of injury (according to ICD-10-CM E-codes), ISS (version 2005), in-hospital mortality, Glasgow Coma Score (GCS), and encrypted hospital identifiers. This dataset consisted of 967,978 patients with 1 or more ICD-10-CM injury codes and AIS codes. Patients with non-traumatic diagnoses (e.g., drowning, poisoning, and suffocation) or burns (47,184), missing or invalid data (data missing on length of hospital stay, age, gender, or outcome) (26,177), missing cause of trauma (8938), or age younger than 1 year (3900) and older than 89 years (60,917) were excluded from our analysis. The reason is that patients over the age of 89 were a separate age category in the NDTB and were assigned the value of − 99 for their age. Patients who transferred to another facility (37,014) or were dead on arrival to the hospital (10,388) were also excluded. Some patients were excluded from the analysis because they have more than 1 exclusion criteria. ICD-10-CM E-codes were mapped to 1 of the 6 injury mechanisms by an experienced trauma surgeon: fall, motor vehicle crash, violence, gunshot wound, stab wound, and blunt injury. The final dataset included 794,098 patients admitted to 738 trauma centers. The details for recruitment are shown in Fig. 1.

Flowchart for data analyzed

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Overview of IMP-ICDX development

In this research, 60% of the total dataset is used to evaluate trauma mortality rate (TMR) of different ICD-10-CM codes. The TMR values are calculated in Additional file 1. Based on TMR, number of body region (NBR), and body region (BR), we created three separate probit regression models by adding six additional variables: age, gender, GCS, ventilator, mechanism of injury, and hospital fixed effects to each of them. In the meantime, we applied optimal ratio of death probability for NBR and BR to modify the traumatic death probability (TDP) for TMR, to achieve an optimal value. The median of the three greatest (worst) TDP values was adopted as a weighted median death probability (WMDP) for each specific ICD-10-CM code (see Additional file 2).

Twenty percent of the dataset (IMP-ICDX development dataset) is used to evaluate IMP-ICDX. We apply logistic regression model to calculate coefficient of IMP-ICDX (Table 4) and deduce specific formula for the IMP-ICDX (see Additional file 3). Twenty percent of the dataset (internal validation dataset) is not used for the development of WMDP and IMP-ICDX to estimate the statistical performance of IMP-ICDX.

Customization of trauma models

This internal validation dataset enables us to test the performance of the ISS, SWI, and IMP-ICDX. ISS was computed according to Baker et al. [1]. A single worst injury (SWI) model was defined as the WMDP value for the worst injury (i.e., the greatest WMDP value). IMP-ICDX comprises the five most severe WMDP values according to injury severity; the product of the WMDP values for the two worst injuries is used as a variable and determines whether or not the two worst injuries are in the same BR and NBR (as ln (NBR) and NBR0.382, suggested by fractional polynomial analysis [13]) of each individual injury patient. The probability of death was calculated with the specific IMP-ICDX formula. At the same time, we then re-estimate all three models after adding age, gender, and injury mechanism to simple injury models, which only include the information on anatomic injury. Robust variance estimators [14] were applied because of the possible correlate outcomes of patients treated at the same trauma center.

Statistical analysis

This article assessed the statistical performance of all models using the area under the receiver operating characteristic (AUC) curve for discrimination, the Hosmer-Lemeshow (HL) statistic for calibration, and the Akaike information criterion (AIC) for proximity to the true model. Non-parametric bootstrapping resampling algorithm with 1000 replications provided 95% confidence intervals (CIs) for the AUC and HL statistic. A P < 0.05 was considered statistically significant. All statistical analyses were performed using STATA/MP version 14.0 for Windows. This paper was exempt from review by the Institutional Review Board of Hangzhou Normal University, People’s Republic of China.


In this text, the total of the WMDP values is 8534 different ICD-10-CM coded injuries (see Additional file 4). These WMDP values range from 0.009 for a minor injury (ICD-10-CM, S42.412A: “Displaced simple supracondylar fracture without intercondylar fracture of left humerus, initial encounter for closed fracture”) to a value of 1.927 for a severe injury (ICD-10-CM, S06.5X7A: “Traumatic subdural hemorrhage with loss of consciousness of any duration with death due to brain injury, initial encounter”). Although trauma ICD-10-CM codes are not set by experts and cannot show information of traumatic severity, which are different from AIS codes, this research calculates the WMDP values of different ICD-10-CM codes and uses them to react to the degree of severity of trauma. We believe that these WMDP values are appropriate and in accordance with the actual situation of clinical, not our subjective assume.

Patient demographics are summarized in Table 1. The median age of our cohort was 49 years. Males accounted for 61.3%, and 66.4% was non-Hispanic White. The majority of patients in this text were fall (44.4%) and motor vehicle collisions (35.8%). The overall mortality rate for the patients was 2.41%.

Full size table

The statistical performance of all models is shown in Tables 2 and 3. The IMP-ICDX displays significantly better discrimination, calibration, or AIC statistic compared with both the ISS and SWI models. Figure 2 graphically displays the superior calibration of IMP-ICDX. The ISS values were distributed to the right of the dotted reference line. The IMP-ICDX values were uniformly distributed much closer to the dotted reference line. The IMP-ICDX coefficients are shown in Table 4.

Full size table

Full size table

Calibration curves for IMP-ICDX and ISS. The dotted reference lines represent perfect calibration (95% binomial confidence intervals for IMP-ICDX and ISS models are based on the same validation dataset of 158,940 patients)

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Full size table


The probability of death from patient trauma depends on many factors. The most important condition is the patient’s trauma severity. With the progress of medical science and the improvement of the treatment level, the trauma mortality has decreased obviously. Most of the existing trauma scores are difficult to distinguish real severity of all trauma patients, and even if there are trauma patients with similar severity, the results of treatment in different hospitals are also significantly differences [15]. This research also has similar results. For any individual patients, the likelihood of death is always accompanied by the whole course of treatment.

At present, there are many trauma score methods. For instance, ISS, NISS, and TISS are rapid evaluation methods while TMPM and IMP are retrospective evaluation methods, and they are all based on AIS codes. These methods have been widely used in clinical practice. They require that all patients have their injuries described in the AIS lexicon. Otherwise, they cannot be used to calculate, which limits their application. The ICISS and IMPM-ICD9, which are based on ICD-9-CM code, have broken away from the AIS code and opened up a new way of scoring method. TMPM-ICD9 is better than ICISS in predicting death results [7, 9]. The data used in this study was derived from ICD-10-CM instead of ICD-9-CM. The above scoring methods are not suitable. Though ICD-10-CM encoding can be converted to ICD-9-CD code and AIS code can be generated, the result after conversion is bound to be biased. It is not in line with the original intention of this research. Therefore, it is sensible to compare IMP-ICDX with ISS in our study.

This text combines the large dataset of NTDB and the feasible scoring method to evaluate the results of the trauma. The NTDB has the world’s largest and the most credible trauma dataset and contains trauma data of different trauma centers in different regions of the USA. It includes information that offers us with research.

In this TMR development dataset, when the actual mortality rate of specific ICD-10-CM code is 0, the TMR value is based on the death trend of the National Vital Statistics Reports in the United States in 2015 [16]. It is set as the median of the possible mortality rate (PMR_M) ( see Additional file 1) because the data is not normally distributed. There are 105 (only contains 370 patients) single or multiple injuries with 100% mortality, but these single or multiple injuries each has 80 or fewer cases, and there is only 1 case when the majority of code pairs have 100% mortality rate. This paper assumed that there was additional one survivor. Then, we calculated the TMR value, and it seemed to decrease death cases. In fact, this modified approach is appropriate and more in conformity with clinical practice.

This study uses TMR, NBR, and BR to create three separate probit regression models respectively for the specific ICD-10-CM code on different individual patients. Meanwhile, we apply optimal ratio of death probability for NBR and BR to modify the TDP for TMR, in order to acquire optimal value. This is a combination of rigorous statistical regression models and mathematical properties to improve the prediction accuracy. As individual’s contribution to the death depends mainly on the three most severe traumas such as ISS, NISS, and TISS agents that have been confirmed, on a specific ICD-10-CM code using different individual patients, the three largest TDP weighted median as its final value (i.e., WMDP) (see Additional file 2).

This study, in IMP-ICDX, when only the death probability value of the most severe injury was used, the coefficient of the worst injury was about four times the coefficient of minor injuries (results not presented). The absolute value of IMP-ICDX and SWI only differs by 0.007, as well as overlapping confidence intervals. What is more, they are still statistically significant (P < 0.01), indicating that IMP-ICDX is better than SWI at predicting traumatic death (Table 2). In a sense, SWI model to predict the death is also better [17]. Trauma surgeons usually describe a patient’s clinical condition using the patient’s one or two worst injuries. The TMPM-ICD9 holds that a patient’s five worst injuries determine the possibility of mortality to a great extent [9], because in this dataset, only five coefficients of the most severe injuries in each patient were statistically significant (Table 4). Thus, IMP-ICDX is defined as the sum of the five worst WMDP values. The results greatly improve the accuracy of the predicted death, whether it is calibration, discrimination, or AIC statistics, far better than ISS (Table 2).

We found that the NBR and whether or not the use of mechanical ventilation in injured patients have intrinsic ability and useful parameters in predicting death due to trauma. They are better than patient’s age or gender discrimination. As the existing evaluation methods (e.g., ICISS and TMPM-ICD9) were not involved, we added NBR and ventilator to improve IMP-ICDX trauma result prediction.

In general, additional information (such as respiratory rate, systolic blood pressure, and GCS) to anatomical injury score can always improve the predicted outcomes [4, 9, 18]. The fundamental IMP-ICDX is extremely attractive because only anatomical trauma information is available. IMP-ICDX can also serve as a rich foundation in adding more sophisticated forecasting information to further enhance the accuracy of predicted results. The addition of the ventilator can enhance the AUC of the IMP-ICDX from 0.919 to 0.952 (no analysis). The IMP-ICDX had better discrimination and calibration than the ISS and the SWI models when we added age, gender, and injury mechanism (Table 3).

The goal of this research is to help people predict trauma death probability accurately according to the hospital diagnosis (ICD-10-CM coding), allocate medical resources rationally and effectively, guide clinical diagnosis and treatment, and ultimately improve the efficiency. This unique computing method can be applied to big data processing in other fields, which may lead to a revolutionary era of big data processing.


The main limitation of this article is to inherit defects of the NTDB data. Although the data is bigger, it is not a population-based dataset. In addition, ICD-10-CM coding may have differences because the data is derived from different trauma centers. At the same time, the ICD-10-CM code itself lacks the severity extent of the injury, which is different from the AIS code, and the prediction of the severity of traumatic death is not accurate; it is difficult to determine the injury severity of solid organs in particular, such as the liver, spleen, and kidney. ICD-10-CM codes have 8000 more variables and more than AIS codes, but they are still unable to make up for their defects. As there are too many encoding classifications, the number of single injury code of 60% data is 1988 and 689 codes are lost. If total data is used to calculate WMDP value or to increase the amount of data, the final AUC will be higher. ICD-10-CM-code-based IMP-ICDX outperforms ISS in predicting the death possibility. In this paper, the TMR value is used as a reference only; each TMR is required to be converted to WMDP by combining with the regression models and mathematical characteristics and then evaluating the probability of death of individual patients with different ICD-10-CM codes. Though the process of this calculation method is somewhat complicated, it can improve the ability to predict trauma death. A concurrent cohort study will likely have the same results, and those interested can test our results further.


In summary, IMP-ICDX is statistically significant compared to ISS, and its predictions of death, discrimination, and calibration are better than those of ISS. Therefore, in our opinion, IMP-ICDX could be a new feasible assessment method for trauma research.

Availability of data and materials

The data that support the findings of this study are available from NTDB databases of American College of Surgeons.


Akaike information criterion

Abbreviated Injury Scale

Area under the receiver operating characteristic curve

Body region

Confidence interval

Glasgow Coma Score


International Classification of Diseases Tenth Revision Clinical Modification External cause of injury codes

Injury mortality prediction

Injury mortality prediction for ICD-10-CM

Interquartile range

Injury severity score

Natural logarithm

Model-averaged regression coefficient

Multiple injuries mortality rate

Number of body region

New injury severity score

National Trauma Data Bank

Single injury mortality rate

Survival risk ratio

Single worst injury

Trauma death probability

Tangent injury severity score

Trauma mortality prediction model

Trauma mortality prediction model for ICD-9-CM

Trauma mortality rate

Weighted median death probability


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The authors greatly appreciate the assistance from American College of Surgeons for providing data of the National Trauma Data Bank.

Author information


  1. Department of Emergency Medicine, Affiliated Hospital of Hangzhou Normal University, 126 Wenzhou Road, Gongshu District, Hangzhou, 310015, Zhejiang, People’s Republic of China

    Muding Wang & Wenhui Fan

  2. Department of Neurosurgery, Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310015, Zhejiang, People’s Republic of China

    Wusi Qiu

  3. Department of Orthopedic, Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310015, Zhejiang, People’s Republic of China

    Yunji Zeng & Xiao Lian

  4. Department of Epidemiology and Health Statistics, School of Public Health, Zhejiang University, Hangzhou, 310058, Zhejiang, People’s Republic of China

    Yi Shen


MW contributed to the study concept and design. MW, WQ, and YS contributed to the analysis and interpretation of data. All authors contributed to the critical revision of the manuscript for important intellectual content. MW contributed to the acquisition of data. WQ and MW contributed to the drafting of the manuscript. WF, YZ, and XL contributed to the literature search. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Muding Wang.

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Wang, M., Qiu, W., Zeng, Y. et al. IMP-ICDX: an injury mortality prediction based on ICD-10-CM codes. World J Emerg Surg14, 46 (2019).

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  • International Classification of Diseases Tenth Edition (ICD-10-CM)
  • Injury mortality prediction for ICD-10-CM (IMP-ICDX)
  • Injury severity score (ISS)
  • Mortality prediction
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List of ICD-9 codes 800–999: injury and poisoning

Chapter Block Title I 001–139Infectious and Parasitic Diseases II 140–239Neoplasms III 240–279Endocrine, Nutritional and Metabolic Diseases, and Immunity Disorders IV 280–289Diseases of the Blood and Blood-forming Organs V 290–319Mental Disorders VI 320–389Diseases of the Nervous System and Sense Organs VII 390–459Diseases of the Circulatory System VIII 460–519Diseases of the Respiratory System IX 520–579Diseases of the Digestive System X 580–629Diseases of the Genitourinary System XI 630–679Complications of Pregnancy, Childbirth, and the Puerperium XII 680–709Diseases of the Skin and Subcutaneous Tissue XIII 710–739Diseases of the Musculoskeletal System and Connective Tissue XIV 740–759Congenital Anomalies XV 760–779Certain Conditions originating in the Perinatal Period XVI 780–799Symptoms, Signs and Ill-defined Conditions XVII 800–999Injury and Poisoning E800–E999Supplementary Classification of External Causes of Injury and PoisoningV01–V82Supplementary Classification of Factors influencing Health Status and Contact with Health ServicesM8000–M9970Morphology of Neoplasms
ICD-10 CM Coding (Injury, Poisoning and Certain Other Consequence of External Causes: S00-T88)

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Codes trauma icd

At that time, Anna ended the game in her favor with another feed through. 2: 0 in the second set. So, Swan, 4 more of these games and you can collect your belongings and get out of New York and never dream of. Coming back here again. wrapped in a towel again, Emma hid from prying prying eyes, cameras and others.

External Cause of Injury Coding with Examples

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