Performance of International Classification of Diseases–based injury severity measures used to predict in-hospital mortality: A systematic review and meta-analysis

Comparison of Trauma Mortality Prediction Models With Updated Survival Risk Ratios in Korea

BACKGROUND

Despite the considerable disease burden due to trauma injury, sufficient effort has not been made for the assessment of nationwide trauma care status in Korea. We explored the feasibility of a diagnosis code-based injury severity measuring method in light of its real-world usage.

METHODS

We used datasets from the National Emergency Department Information System to calculate the survival risk ratios (SRRs) and the Korean Trauma Data Bank to predict models, respectively. The target cohort was split into training and validation datasets using stratified random sampling in an 8:2 ratio. We established six major mortality prediction models depending on the included parameters: 1) the Trauma and Injury Severity Score (TRISS) (age, sex, original Revised Trauma Score [RTS], Injury Severity Score [ISS]), 2) extended International Classification of Diseases-based Injury Severity Score (ICISS) 1 (age, sex, original RTS, ICISS using international SRRs), 3) extended ICISS 2 (age, sex, original RTS, ICISS using Korean SRRs based on 4-digit diagnosis codes), 4) extended ICISS 3 (age, sex, original RTS, ICISS using Korean SRRs based on full-digit diagnosis codes), 5) extended ICISS 4 (age, sex, modified RTS, and ICISS using Korean SRRs based on 4-digit diagnosis codes), 6) extended ICISS 5 (age, sex, modified RTS, and ICISS using Korean SRRs based on full-digit diagnosis codes). We estimated the model using training datasets and fitted it to the validation datasets. We measured the area under the receiver operating characteristic curve (AUC) for discriminative ability. Overall performance was also evaluated using the Brier score.

RESULTS

We observed the feasibility of the extended ICISS models, though their performance was slightly lower than the TRISS model (training cohort, AUC 0.936-0.938 vs. 0.949). Regarding SRR calculation methods, we did not find statistically significant differences. The alternative use of the Alert, Voice, Pain, Unresponsive Scale instead of the Glasgow Coma Scale in the RTS calculation did not degrade model performance.

CONCLUSION

The availability of the practical ICISS model was observed based on the model performance. We expect our ICISS model to contribute to strengthening the Korean Trauma Care System by utilizing mortality prediction and severity classification.

Performance of ICD-10-based injury severity scores in pediatric trauma patients using the ICD-AIS map and survival rate ratios

OBJECTIVES

The performance of injury severity scores (ISSs), used widely to quantify injury severity and predict outcomes, has not been investigated in German pediatric cases. This study aims to identify the most feasible and accurate injury score predictor of mortality in German children with trauma using International Classification of Diseases 10 (ICD-10).

STUDY DESIGN AND SETTING

Between 2014 and 2020, a retrospective observational cohort study of hospital admissions cases aged <18 years with injury-related ICD-10 codes, using the German hospital database (GHD), was conducted. The maximum abbreviated injury scale and ISS were calculated using the International Classification of Diseases-Abbreviated Injury Scale (ICD-AIS) map provided by the Association for the Advancement of Automotive Medicine, adjusted to the German modification of the ICD-10 classification. The survival risk ratio was used to calculate the single-worst ICD-derived injury (single International Classification of Disease Injury Severity Score [ICISS]) and a multiplicative ICISS. Logistic regressions were conducted for each of the four above-mentioned scores (predictors) to predict in-hospital mortality (outcome) in the selected trauma population and within four clinically relevant subgroups using discrimination and calibration.

RESULTS

1,720,802 were trauma patients, and ICD-AIS mapping was possible in 1,328,377 cases. Cases with mapping failure (n = 392,425; 22.8%) were younger and had a higher mortality rate were excluded from the performance analysis. ICISS-derived scores had a better discrimination and calibration than ICD-AIS based scores in the overall cohort and all four subgroups (area under the curve [AUC] ranges between 0.985 and 0.998 vs 0.886 and- 0.972, respectively).

CONCLUSION

Empirically derived measures of injury severity were superior to ICD-AIS mapped scores in the GHD to predict mortality in pediatric trauma patients. Given the high percentage of mapping failure and high mortality among cases with single-coded injury, the single ICISS may be the most suitable measure of injury severity in this group of patients.

External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter retrospective cohort study

Artificial intelligence (AI) is being increasingly applied in healthcare to improve patient care and clinical outcomes. We previously developed an AI model using ICD-10 (International Classification of Diseases, Tenth Revision) codes with other clinical variables to predict in-hospital mortality among trauma patients from a nationwide database. This study aimed to externally validate the performance of the AI model. Validation was conducted using a multicenter retrospective cohort study design, analyzing patient data from January 2020 to December 2021. The study included trauma patients based on specific ICD-10 codes, with other clinical variables. The performance of the AI model was evaluated against conventional metrics, including the ISS, and the ICISS (ICD-based ISS), using sensitivity, specificity, accuracy, balanced accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUROC) analyses. Data from 4,439 patients were analyzed. The AI model demonstrated high overall performance, achieving an AUROC of 0.9448 and a balanced accuracy of 85.08%, thereby outperforming traditional scoring systems such as ISS, or ICISS. Furthermore, the model accurately predicted mortality across datasets from each hospital (AUROCs of 0.9234 and 0.9653, respectively) despite significant differences in hospital characteristics. In the subset of patients with ISS < 9, the model showed a robust AUROC of 0.9043, indicating its effectiveness in predicting mortality, even in cases with lower-severity injuries. For patients with ISSs ≥ 9, the model maintained high sensitivity (93.60%) and balanced accuracy (77.08%), proving its reliability in more severe injury cases. External validation demonstrated the AI model's high predictive accuracy and reliability in assessing in-hospital mortality risk among trauma patients across different injury severities and heterogeneous cohorts. These findings support the model's potential integration into emergency departments and offer a significant tool for enhancing patient triage and treatment protocols.

An update of the severe trauma scoring system using the Korean National Emergency Department Information System (NEDIS) database

BACKGROUND

Various scoring systems are utilized to assess severe trauma patients, with one of the most commonly used tools being the International Classification of Diseases Injury Severity Score (ICISS) criteria derived from the Survival Risk Ratio (SRR) calculated using diagnostic codes. This study aimed to redefine the severe trauma scoring system in Korea based on the SRR for diagnostic codes, and subsequently evaluate its performance in predicting survival outcomes for trauma patients.

METHODS

This study included trauma patients who visited Level 1 and 2 emergency departments (EDs) between January 2016 and December 2019, utilizing the Korean National Emergency Department Information System (NEDIS) database. The primary outcome of this study was in-hospital mortality. The new SRR-2020 value was calculated for each of the 865 trauma diagnosis codes (Korean Standard Classification of Diseases [KCD-7] codes, 4-digit format), and the patient-specific ICISS-2020 value was derived by multiplying the corresponding SRR-2020 value based on patient diagnosis. We compared the predictive performance for in-hospital mortality between severe trauma patients with an ICISS <0.9 based on the newly developed ICISS-2020 version and those defined by the previously used ICISS-2015 version.

RESULTS

A total of 3,841,122 patients were enrolled, with an in-hospital mortality rate of 0.5 %. Severe trauma patients with ICISS-2020 < 0.9 accounted for 5.3 % (204,897 cases) that was lower than ICISS-2015 < 0.9 accounting for 15.3 % (587,801 cases). Among the 20,619 in-hospital mortality cases, 81.4 % had ICISS-2020 < 0.9, and 88.6 % had ICISS-2015 < 0.9. When comparing predictive performance for in-hospital mortality between the two ICISS versions, ICISS-2020 showed higher accuracy (0.95), specificity (0.95), positive predictive value (PPV) (0.08), positive likelihood ratio (LR+) (16.53), and area under the receiver operating characteristic curve (AUROC) (0.96) than ICISS-2015 for accuracy (0.85), sensitivity (0.88), specificity (0.85), PPV (0.03), LR+ (5.94), and AUROC (0.94). However, regarding sensitivity, ICISS-2020 < 0.9 showed a lower value of 0.81 compared to ICISS-2015 < 0.9, which was 0.88. The negative predictive value (NPV) was 1.00 for both versions.

CONCLUSIONS

The newly developed ICISS-2020, utilizing a nationwide emergency patient database, demonstrated relatively good performance (accuracy, specificity, PPV, LR+, and AUROC) in predicting survival outcomes for patients with trauma.

International Classification of Disease based Injury Severity Score (ICISS): a comparison of methodologies applied to linked data from New South Wales, Australia

BACKGROUND

The International Classification of Disease Injury Severity Score (ICISS) provides an efficient method to determine injury severity in hospitalised injury patients. Injury severity metrics are of particular interest for the tracking of road transport injury rates and trends. The aims of this study were to calculate ICISS using linked morbidity and mortality datasets and to compare predictive ability of various methods and metrics.

METHODS

This was a retrospective analysis of Admitted Patient Data Collection records from New South Wales, Australia, linked with mortality data. Using a split sample approach, design data (2008-2014; n=1 035 174 periods of care) was used to derive survival risk ratios and calculate various ICISS scales based on in-hospital death and 3-month death. These scales were applied to testing data (2015-2017; n=575 306). Logistic regression modelling was used to determine model discrimination and calibration.

RESULTS

There were 12 347 (1.19%) in-hospital deaths and 29 275 (2.83%) 3-month deaths in the design data. Model discrimination ranged from acceptable to excellent (area under the curve 0.75-0.88). Serious injury (ICISS≤0.941) rates in the testing data varied, with a range of 10%-31% depending on the methodology. The 'worst injury' ICISS was always superior to 'multiplicative injury' ICISS in model discrimination and calibration.

CONCLUSIONS

In-hospital death and 3-month death were used to generate ICISS; the former is recommended for settings with a focus on short-term threat to life, such as in trauma care settings. The 3-month death approach is recommended for outcomes beyond immediate clinical care, such as injury compensation schemes.

Hospitalization costs of road traffic injuries in Hunan, China: A quantile regression analysis

BACKGROUND

Healthcare expenditure of road traffic injuries in China has not been adequately investigated so far. We aim to provide comprehensive information about the hospitalization costs of inpatients who suffered road traffic injuries, and explore the components and influencing factors of costs.

METHODS

We extracted the data of all inpatients (n = 60535) with road traffic injuries during the year 2019 from Chinese National Health Statistics Network Reporting System database in Hunan, China. We calculated the components of hospitalization costs and analyzed the association between hospitalization costs and patient characteristics using quantile regression models.

RESULTS

The median hospitalization cost was $853.48, and the median length of hospital stay was 9 days. Vulnerable road users accounted for 84.9 % of all cases. Medicine cost is the first driver of hospitalization cost, accounting for 25.94 %. In the low- and medium-cost groups, hospitalization costs were highly concentrated on diagnosis, medicine, and medical services, while in the high-cost groups, consumable cost constituted the highest percentage. Male, a longer length of stay, more severe injuries, two or more comorbidities, surgical treatment, and admission to tertiary hospitals were significantly associated with higher hospitalization costs, and the regression coefficients increased with increasing of quartile points. Costs were lower in the 0-14 years group than in the other groups across all quartiles. At the median, occupants of heavy transport vehicle incurred the highest costs, $44.18 higher than pedestrians; injuries at lower extremities generated higher costs than those at any other site; and vascular injuries caused the greatest costs, $786.24 higher than superficial injuries.

CONCLUSIONS

Road traffic injuries cause huge healthcare costs for victims, most of whom are vulnerable road users. The total cost of hospitalization is incurred mainly for medicine, consumables, diagnosis, medical services, and treatment. Patients' demographic factors (gender and age), clinical factors (injury severity, location, nature, and number of comorbidities), treatment factors (surgery, length of stay, and hospital level), and road user type are all significantly associated with hospitalization costs.

The effect of sleep disorders on dementia risk in patients with traumatic brain injury: A large-scale cohort study

INTRODUCTION

We investigated the association between sleep disorders (SDs) and incident dementia in adults with traumatic brain injury (TBI).

METHODS

Adults with a TBI between 2003 and 2013 were followed until incident dementia. Sleep disorders at TBI were predictors in Cox regression models, controlling for other dementia risks.

RESULTS

Over 52 months, 4.6% of the 712,708 adults (59% male, median age 44, <1% with SD) developed dementia. An SD was associated with a 26% and a 23% of increased risk of dementia in male and female participants (hazard ratio [HR] 1.26, 95% confidence interval [CI] 1.11-1.42 and HR 1.23, 95% CI 1.09-1.40, respectively). In male participants, SD was associated with a 93% increased risk of early-onset dementia (HR 1.93, 95% CI 1.29-2.87); this did not hold in female participants (HR 1.38, 95% CI 0.78-2.44).

DISCUSSION

In a province-wide cohort, SDs at TBI were independently associated with incident dementia. Clinical trials testing sex-specific SD care after TBI for dementia prevention are timely.

HIGHLIGHTS

TBI and sleep disorders are linked to each other, and to dementia.It is unclear if sleep disorders pose a sex-specific dementia risk in brain injury.In this study, presence of a sleep disorder increased dementia risk in both sexes.The risk differed by type of sleep disorder, which differed between the sexes.Sleep disorder awareness and care in persons with brain injury is vital for dementia prevention.

Undertriage of Geriatric Trauma Patients in Florida

INTRODUCTION

Elderly undertriage rates are estimated up to 55% in the United States. This study examined risk factors for undertriage among hospitalized trauma patients in a state with high volumes of geriatric trauma patients.

MATERIALS AND METHODS

This is a population-based retrospective cohort study of 62,557 patients admitted to Florida hospitals between 2016 and 2018 from the Agency for Healthcare Administration database. Severely injured trauma patients were defined by American College of Surgeons definitions and an International Classification of Disease Injury Severity Score <0.85. Undertriage was defined as definitive care of these severely injured patients at any Florida hospital other than a state-designated trauma center (TC). Univariate analyses were used to identify risk factors associated with inpatient mortality and undertriage. Multiple variable regression was used to estimate risk-adjusted odds of mortality after admission to either a designated or nondesignated TC.

RESULTS

Undertriaged patients were more likely to have isolated traumatic brain injuries, lower International Classification of Disease Injury Severity Scores, multiple comorbidities, and older age. Trauma patients aged 65 and older were more than twice as likely to be undertriaged (34% versus 15.7%, P < 0.0001). Undertriaged patients of all ages were also more likely to suffer from pneumonia, urinary tract infection, arrhythmias, and sepsis. After risk adjustment, severely injured trauma patients admitted to non-TC were also more likely to be at risk for mortality (adjusted odds ratio, 1.27; 95% confidence interval, 1.17-1.38).

CONCLUSIONS

Age and multiple comorbidities are significant predictors of mortality among undertriage of trauma patients. As a result, trauma triage guidelines should account for high-risk geriatric trauma patients who would benefit from definitive treatment at designated TCs.

The International Classification of Disease Critical Care Severity Score demonstrates that pediatric burden of injury is similar to that of adults: Validation using the National Trauma Databank☆

BACKGROUND/PURPOSE

Resource-based severity of injury (SOI) measures, such as the International Classification of Disease (ICD) Critical Care Severity Score (ICASS), may characterize traumatic burden better than standard mortality-based measures. The purpose of this study was to validate the ICASS in a representative national-level trauma cohort and compare SOI measures between children and adults.

METHODS

The National Trauma Databank was used to derive (2008-12) and validate (2013-15) ICASS and ICD Injury Severity Scores (ICISS, standard mortality-based SOI measure). SOI metrics and outcomes were compared between pediatric, adult, and elderly age groups. Logistic regression modeling evaluated predictors of critical care resource utilization.

RESULTS

Derivation and validation cohorts consisted of 3.90 and 1.97 million patients, respectively. ICASS strongly predicted actual critical care utilization (OR 1.04, 95% CI 1.04-1.04, p<0.0001). Mean ICASS was 24.4 for children and 33.0 for adults (ratio 0.74), indicating predicted critical care utilization in children was three-quarters that of adults. In contrast, predicted pediatric mortality was less than half that of adults.

CONCLUSIONS

Mortality-based SOI measures underestimate pediatric burden of injury. This study validates ICASS and demonstrates that pediatric resource-based SOI is more similar to that of adults. ICASS is easily calculated without a trauma registry and complements mortality-based measures. Level of evidence III, retrospective comparative study.

Injury severity and increased socioeconomic differences: A population-based cohort study

BACKGROUND

Several studies have documented an inverse gradient between socioeconomic status (SES) and injury mortality, but the evidence is less consistent for injury morbidity. The aim of this study was to investigate the association between SES and injury severity for acute hospitalizations in a nationwide population-based cohort.

METHODS

We conducted a registry-based cohort study of all individuals aged 25-64 years residing in Norway by 1st of January 2008. This cohort was followed from 2008 through 2014 using inpatient registrations for acute hospitalizations due to all-cause injuries. We derived two measures of severity: threat-to-life using the International Classification of Disease-based Injury Severity Score (ICISS), and threat of disability using long-term disability weights from the Injury-VIBES project. Robust Poisson regression models, with adjustment for age, sex, marital status, immigrant status, municipality population size and healthcare region of residence, were used to calculate incidence rate ratios (IRRs) by SES measured as an index of education, income, and occupation.

RESULTS

We identified 177,663 individuals (7% of the population) hospitalized with at least one acute injury in the observation period. Two percent (n = 4,186) had injuries categorized with high threat-to-life, while one quarter (n = 43,530) had injuries with high threat of disability. The overall adjusted IRR of hospitalization among people with low compared to high SES was 1.57 (95% CI 1.55, 1.60). Comparing low to high SES, injuries with low threat-to-life were associated with an IRR of 1.56 (95% CI 1.54, 1.59), while injuries with high threat-to-life had an IRR of 2.25 (95% CI 2.03, 2.51). Comparing low to high SES, injuries with low, medium, and high threat of disability were associated with IRRs of respectively, 1.15 (95% CI 1.11, 1.19), 1.70 (95% CI 1.66, 1.73) and 1.99 (95% CI 1.92, 2.07).

DISCUSSION

We observed an inverse gradient between SES and injury morbidity, with the steepest gradient for the most severe injuries. This suggests a need for targeted preventive measures to reduce the magnitude and burden of severe injuries for patients with low socioeconomic status.

International classification of disease based injury severity score (ICISS): A data linkage study of hospital and death data in Victoria, Australia

BACKGROUND

Surveillance of severe injury incidence and prevalence using ICD-based injury severity scores (ICISS) requires valid, locally applicable diagnosis-specific survival probabilities (DSPs). This study aims to derive and validate ICISS in Victoria, Australia, and compare various ICISS methodologies in terms of accuracy and calculated severe injury prevalence.

METHODS

This study used injury admissions (ICD-10-AM coded) from the Victorian Admitted Episodes Database (VAED) linked with death data (Cause of Death - Unit Record Files: CODURF). Using design data (July 2008 - June 2014; n = 720,759), various ICISS scales were derived, based on (i) in-hospital and (ii) three-month mortality. These scales were applied to testing data (July 2014 - December 2016; n = 334,363). Logistic regression modelling was used to determine model discrimination and calibration.

RESULTS

In the design data, there were 6,337(0.9%) hospital deaths and 17,514(2.4%) three-months deaths; in the testing data, there were 2,700(0.8%) hospital deaths and 8,425(2.5%) three-month deaths. Newly developed ICISS scales had acceptable to outstanding discrimination, with Area Under the Curve ranging from 0.758 to 0.910. Age-specific ICISS scales were superior to general ICISS scales in model discrimination but inferior in model calibration. Calculated severe injury (ICISS ≤0.941) prevalence in the testing data ranged from 2% to 24%, depending on which mortality outcomes were used to calculate DRGs.

CONCLUSIONS

This study provides local, validated ICISS scores that can be used in Victoria. It is recommended that age group stratified ICISS based on the worst-injury method is used. From the comparison of various ICISS scores, reflecting the range of ICISS permutations that are currently in use, care should be taken to compare ICISS methodology before comparing severe injury prevalence per population, injury cause, and time trends.

The use of computed tomography in the management of injury in tertiary emergency departments in Western Australia: Evidence of overtesting?

BACKGROUND

This study investigated trends in computed tomography (CT) utilization across different triage categories of injury presentations to tertiary emergency departments (EDs) and associations with diagnostic yield measured by injury severity, hospitalization and length of stay (LOS), and mortality.

METHODS

A total of 411,155 injury-related ED presentations extracted from linked records from Western Australia from 2004 to 2015 were included in the retrospective study. The use of CT scanning and diagnostic yield measured by rate of diagnosis with severe injury, hospitalizations and LOS, and mortality were captured annually for injury-related ED presentations. Multivariable regression models were used to calculate the annual adjusted rate of CT scanning for injury presentations and hospitalizations across triage categories, diagnosis with severe injury, LOS, and mortality. The significance of changes observed was compared among patients with CT imaging relative to those without CT.

RESULTS

While the number of ED presentations with injury increased by 65% from 2004 to 2015, the use of CT scanning in these presentations increased by 176%. The largest increase in CT use was among ED presentations triaged as semi-/nonurgent (+256%). Injury presentations with CT, compared to those without, had a higher rate of diagnosis with moderate/severe injury and hospitalization but no difference in LOS and mortality. The probability/rate observed in the outcomes of interest had a greater decrease over time in those with CT scanning compared with those without CT scanning across triage categories.

CONCLUSIONS

The reduction in diagnostic yield in terms of injury severity and hospitalization found in our study might indicate a shift toward overtesting using CT in ED for injury or a higher use of CT to assist in the management of injuries. This helps health care policymakers consider whether the current increase in CT use meets the desired levels of quality and efficient care.

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.

Quantifying injury severity for traumatic brain injury with routinely collected health data

BACKGROUND

Routinely collected health data (RCHD) offers many opportunities for traumatic brain injury (TBI) research, in which injury severity is an important factor.

OBJECTIVE

The use of clinical injury severity indices in a context of RCHD is explored, as are alternative measures created for this specific purpose. To identify useful scales for full body injury severity and TBI severity this study focuses on their performance in predicting these currently used indices, while accounting for age and comorbidities.

DATA

This study utilized an extensive population-based RCHD dataset consisting of all patients with TBI admitted to any Belgian hospital in 2016.

METHODS

Full body injury severity is scored based on the (New) Injury Severity Score ((N)ISS) and the ICD-based Injury Severity Score (ICISS). For TBI specifically, the Abbreviated Injury Scale (AIS) Head, Loss of Consciousness and the ICD-based Injury Severity Score for TBI injuries (ICISS) were used in the analysis. These scales were used to predict three outcome variables strongly related to injury severity: in-hospital death, admission to intensive care and length of hospital stay. For the prediction logistic regressions of the different injury severity scales and TBI severity indices were used, and error rates and the area under the receiver operating curve were evaluated visually.

RESULTS

In general, the ICISS had the best predictive performance (error rate between 0.06 and 0.23; AUC between 0.82 [0.81;0.83] and 0.86 [0.85;0.86]). A clearly increasing error rate can be noticed with advancing age and accumulating comorbidity.

CONCLUSION

Both for full body injury severity and TBI severity, the ICISS tends to outperform other scales. It is therefore the preferred scale for use in research on TBI in the context of RCHD. In their current form, the severity scales are not suitable for use in older populations.

The Effect of Hospital and Surgeon Volumes on Complication Rates After Fixation of Peritrochanteric Hip Fractures

OBJECTIVE

This study evaluates the relationship between hospital and surgeon volumes of peritrochanteric hip fracture fixation and complication rates.

METHODS

Adults (60 years of age or older) who underwent surgical fixation for closed peritrochanteric fractures from 2009 to 2015 were identified using International Classification of Diseases 9 and 10 Clinical Modification and Procedural codes in the New York Statewide Planning and Research Cooperative System database. Readmission, reoperations, in-hospital mortality, and other adverse events were compared across surgeon and facility volumes. Statistical significance was set at P < 0.05.

RESULTS

A total of 29,656 patients were included in the study. Low-volume (LV) facilities had higher rates of readmission [hazard ratio (HR) 1.07, 95% confidence interval (CI), 1.05-1.17], pneumonia (HR 1.36, 95% CI, 1.22-1.51), wound complications (HR 1.24, 95% CI, 1.03-1.49), and mortality (HR 1.15, 95% CI, 1.04-1.27) but lower rates of acute renal failure (HR 0.90, 95% CI, 0.83-0.98), deep vein thrombosis (HR 0.66, 95% CI, 0.55-0.78), and acute respiratory failure (HR 0.77, 95% CI, 0.62-0.95) than high-volume (HV) facilities. Patients treated by LV surgeons had lower rates of readmission (HR 0.92, 95% CI, 0.87-0.97) and deep vein thrombosis (HR 0.78, 95% CI, 0.66-0.94) but higher rates of acute renal failure (HR 1.13, 95% CI, 1.04-1.22) than those treated by HV surgeons.

CONCLUSIONS

There are increased rates of mortality, readmission, and certain complications when peritrochanteric femur fractures are surgically managed at LV hospitals compared with those managed at HV hospitals. Thus, the benefit of a high-volume surgical facility is apparent in mortality and readmissions but not all complications. There was no significant decrease in complications if fixation was performed by HV surgeons relative to LV surgeons.

LEVEL OF EVIDENCE

Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

ICD-11: an international classification of diseases for the twenty-first century

BACKGROUND

The International Classification of Diseases (ICD) has long been the main basis for comparability of statistics on causes of mortality and morbidity between places and over time. This paper provides an overview of the recently completed 11th revision of the ICD, focusing on the main innovations and their implications.

MAIN TEXT

Changes in content reflect knowledge and perspectives on diseases and their causes that have emerged since ICD-10 was developed about 30 years ago. Changes in design and structure reflect the arrival of the networked digital era, for which ICD-11 has been prepared. ICD-11's information framework comprises a semantic knowledge base (the Foundation), a biomedical ontology linked to the Foundation and classifications derived from the Foundation. ICD-11 for Mortality and Morbidity Statistics (ICD-11-MMS) is the primary derived classification and the main successor to ICD-10. Innovations enabled by the new architecture include an online coding tool (replacing the index and providing additional functions), an application program interface to enable remote access to ICD-11 content and services, enhanced capability to capture and combine clinically relevant characteristics of cases and integrated support for multiple languages.

CONCLUSIONS

ICD-11 was adopted by the World Health Assembly in May 2019. Transition to implementation is in progress. ICD-11 can be accessed at icd.who.int.

The value of trauma patients' centralization: an analysis of a regional Italian Trauma System performance with TMPM-ICD-9

BACKGROUND

In recent years, many studies showed that the Trauma Mortality Probability Model (TMPM-ICD-9) had better calibration compared to other ICD-9-based models and to the ones based to the Abbreviated Injury Scale (AIS). The study aims to assess the validity of TMPM-ICD-9 in predicting injury severity in an Italian region and, through this model, to assess the performances of the Trauma Systems SIAT Romagna.

METHODS

Administrative data of trauma patients admitted in the Trauma System of SIAT Romagna, in Northern Italy, from 2014 to 2018 were obtained. The XISS, an indirect indicator of Injury Severity Score (ISS) and the TMPM-POD (Probability of Death) were calculated from ICD-9-CM codes. Only patients with XISS > 15 were included. Student t-test, Mann-Whitney test and Chi-square test were used for univariate analyses, while logistic regression for multivariate analyses.

RESULTS

3907 trauma patients with XISS > 15 were included. The Hub hospital (HUB) received 47.1% of these patients. Patients treated in HUB had higher TMPM-POD than in SPOKE + PST (mean TMPM-POD ± SD: HUB 0.093 ± 0.091, SPOKE + PST 0.082 ± 0.90, p < 0.027), but only age and sex were significant risk factors for centralization at multivariate analyses. Higher age (73.1 ± 21.2 vs 66.9 ± 21.2, p < 0.001), higher XISS (16(9) vs 16(4), p < 0.001) and higher TMPM-POD (0.15 ± 0.14 vs 0.08 ± 0.08, p < 0.001) resulted significant risk factors for mortality at multivariate analysis. Lower age, higher XISS and lower Trauma Centers (TC) level were significant risk factors for splenectomy at multivariate analysis. The splenectomy rate was 1.3% in HUB and of 2.2% in SPOKE + PST (Risk Ratio = 0.4, p = 0.002).

CONCLUSIONS

Present analysis proved the validity of TMPM-ICD-9 in predicting mortality of trauma patients in an Italian region. Furthermore, the usefulness of data extracted from an administrative database to assess the performance of a TS and the importance of an adequate centralization process have emerged. Even with a higher TMPM-POD and with the same mortality rate, HUB showed a higher spleen salvage rate compared to SPOKE + PST. However, thanks to this model, an improvable centralization process in SIAT Romagna was found in the study period. Probably, an enhanced centralization would have improved the spleen salvage rate, which is an important quality indicator in the evaluation of the performance of the TS.

Development and internal validation of China mortality prediction model in trauma based on ICD-10-CM lexicon: CMPMIT-ICD10

Background:

Models to predict mortality in trauma play an important role in outcome prediction and severity adjustment, which informs trauma quality assessment and research. Hospitals in China typically use the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) to describe injury. However, there is no suitable prediction model for China. This study attempts to develop a new mortality prediction model based on the ICD-10-CM lexicon and a Chinese database.

Methods:

This retrospective study extracted the data of all trauma patients admitted to the Beijing Red Cross Emergency Center, from January 2012 to July 2018 (n = 40,205). We used relevant predictive variables to establish a prediction model following logistic regression analysis. The performance of the model was assessed based on discrimination and calibration. The bootstrapping method was used for internal validation and adjustment of model performance.

Results:

Sex, age, new region-severity codes, comorbidities, traumatic shock, and coma were finally included in the new model as key predictors of mortality. Among them, coma and traumatic shock had the highest scores in the model. The discrimination and calibration of this model were significant, and the internal validation performance was good. The values of the area under the curve and Brier score for the new model were 0.9640 and 0.0177, respectively; after adjustment of the bootstrapping method, they were 0.9630 and 0.0178, respectively.

Conclusions:

The new model (China Mortality Prediction Model in Trauma based on the ICD-10-CM lexicon) showed great discrimination and calibration, and performed well in internal validation; it should be further verified externally.

Gap in funding for specialist hospitals treating patients with traumatic spinal cord injury under an activity-based funding model in New South Wales, Australia

Objective The aim of this study was to estimate the difference between treatment costs in acute care settings and the level of funding public hospitals would receive under the activity-based funding model. Methods Patients aged ≥16 years who had sustained an incident traumatic spinal cord injury (TSCI) between June 2013 and June 2016 in New South Wales were included in the study. Patients were identified from record-linked health data. Costs were estimated using two approaches: (1) using District Network Return (DNR) data; and (2) based on national weighted activity units (NWAU) assigned to activity-based funding activity. The funding gap in acute care treatment costs for TSCI patients was determined as the difference in cost estimates between the two approaches. Results Over the study period, 534 patients sustained an acute incident TSCI, accounting for 811 acute care hospital separations within index episodes. The total acute care treatment cost was estimated at A$40.5 million and A$29.9 million using the DNR- and NWAU-based methods respectively. The funding gap in total costs was greatest for the specialist spinal cord injury unit (SCIU) colocated with a major trauma service (MTS), at A$4.4 million over the study period. Conclusions The findings of this study suggest a substantial gap in funding for resource-intensive patients with TSCI in specialist hospitals under current DRG-based funding methods. What is known about the topic? DRG-based funding methods underestimate the treatment costs at the hospital level for patients with complex resource-intensive needs. This underestimation of true direct costs can lead to under-resourcing of those hospitals providing specialist services. What does this paper add? This study provides evidence of a difference between true direct costs in acute care settings and the level of funding hospitals would receive if funded according to the National Efficient Price and NWAU for patients with TSCI. The findings provide evidence of a shortfall in the casemix funding to public hospitals under the activity-based funding for resource-intensive care, such as patients with TSCI. Specifically, depending on the classification system, the principal referral hospitals, the SCIU colocated with an MTS and stand-alone SCIU were underfunded, whereas other non-specialist hospitals were overfunded for the acute care treatment of patients with TSCI. What are the implications for practitioners? Although health care financing mechanisms may vary internationally, the results of this study are applicable to other hospital payment systems based on diagnosis-related groups that describe patients of similar clinical characteristics and resource use. Such evidence is believed to be useful in understanding the adequacy of hospital payments and informing payment reform efforts. These findings may have service redesign policy implications and provide evidence for additional loadings for specialist hospitals treating low-volume, resource-intensive patients.

Risk factors for avoidable transfer to a pediatric trauma center among patients 2 years and older

BACKGROUND

Effective and sustainable pediatric trauma care requires systems of regionalization and interfacility transfer. Avoidable transfer, also known as secondary overtriage, occurs when a patient is transferred to a regional trauma center after initial evaluation at another facility that is capable of providing definitive care. The purpose of this study was to identify risk factors for avoidable transfer among pediatric trauma patients in southwest Florida.

METHODS

All pediatric trauma patients 2 years and older transferred from outlying hospitals to the emergency department of a single state-designated pediatric trauma center between 2009 and 2017 were obtained from the institutional registry. Transfers were classified as avoidable if the patient suffered only minor injuries (International Classification of Diseases-9th Rev. Injury Severity Score > 0.9), did not require invasive procedures or intensive care unit monitoring, and was discharged within 48 hours. Demographics and injury characteristics were compared for avoidable and nonavoidable transfers. Logistic regression was used to estimate the independent effects of age, sex, insurance type, mechanism of injury, diagnosis, within region versus out-of-region residence, suspected nonaccidental trauma, and abnormal Glasgow Coma Scale score on the risk of avoidable transfer.

RESULTS

A total of 3,876 transfer patients met inclusion criteria, of whom 1,628 (42%) were classified as avoidable. Among avoidable transfers, 29% had minor head injuries (isolated skull fractures, concussions, and mild traumatic brain injury not otherwise specified), and 58% received neurosurgery consultation. On multivariable analysis, the strongest risk factors for avoidable transfer were diagnoses of isolated skull fracture or concussion. Suspected nonaccidental trauma was predictive of nonavoidable transfer.

CONCLUSION

Among injured children 2 years and older, those with minor head injuries were at greatest risk for avoidable transfer. Many were transferred because of a perceived need for evaluation by a pediatric neurosurgeon. Future projects seeking to reduce avoidable transfers should focus on children with isolated skull fractures and concussions, in whom there is no suspicion of nonaccidental trauma.

LEVEL OF EVIDENCE

Therapeutic/care management, level IV.

Correlating injury severity scores and major trauma volume using a state-wide in-patient administrative dataset linked to trauma registry data-A retrospective analysis from New South Wales Australia

INTRODUCTION

Trauma registries are used to analyse and report activity and benchmark quality of care at designated facilities within a trauma system. These capabilities may be enhanced with the incorporation of administrative and electronic medical record datasets, but are currently limited by the use of different injury coding systems between trauma and administrative datasets.

OBJECTIVES

Use an Abbreviated Injury Scale to International Classification of Disease (AIS-ICD) mapping tool to correlate estimated injury severity scores and major trauma volume based on administrative data collections with trauma registry data.

METHODS

Adult trauma cases were identified from the New South Wales Trauma Registry between 2012 and 2016 and linked probabilistically using age, facility and date of facility arrival to the Admitted Patient Data Collection (APDC). Estimated Injury Severity Scores (ISS) were derived using the AIS-ICD mapping tool applied to diagnoses contained in the APDC.

RESULTS

A total of eligible 13,439 cases were analysed. The overall correlation between trauma registry ISS and ISS estimated from APDC using the AIS-ICD mapping tool was low to moderate (Spearman Rho 0.41 95%CI 0.40, 0.43). Based on an estimated ISS cut-off value of 8, there was high correlation between estimated trauma volume and the number of major trauma cases at each facility (Spearman Rho 0.98, 95%CI 0.95, 0.99). Trauma Revised Injury Severity Score (TRISS) was associated with only slightly higher mortality prediction performance compared to estimated ISS (AUROC 0.76 95%CI 0.75, 0.78 versus AUROC 0.74 95%CI 0.73, 0.76).

CONCLUSION

A low to moderate correlation exists between individual patient ISS scores based on AIS to ICD mapping of in-patient data collection, but a high correlation for overall major trauma volume using the AIS-ICD mapping at facility level with comparable TRISS mortality prediction.

The role of the American Society of anesthesiologists physical status classification in predicting trauma mortality and outcomes

BACKGROUND

Trauma prediction scores such as Revised Trauma Score (RTS) and Trauma and Injury Severity Score (TRISS)) are used to predict mortality, but do not include comorbidities. We analyzed the American Society of Anesthesiologists physical status (ASA PS) for predicting mortality in trauma patients undergoing surgery.

METHODS

This multicenter, retrospective study compared the mortality predictive ability of ASA PS, RTS, Injury Severity Score (ISS), and TRISS using a complete case analysis with mixed effects logistic regression. Associations with mortality and AROC were calculated for each measure alone and tested for differences using chi-square.

RESULTS

Of 3,042 patients, 230 (8%) died. The AROC for mortality for TRISS was 0.938 (95%CI 0.921, 0.954), RTS 0.845 (95%CI 0.815, 0.875), and ASA PS 0.886 (95%CI 0.864, 0.908). ASA PS + TRISS did not improve mortality predictive ability (p = 0.18).

CONCLUSIONS

ASA PS was a good predictor of mortality in trauma patients, although combined with TRISS it did not improve predictive ability.

Identifying Predictors of Higher Acute Care Costs for Patients With Traumatic Spinal Cord Injury and Modeling Acute Care Pathway Redesign: A Record Linkage Study

STUDY DESIGN

Record linkage study using healthcare utilization and costs data.

OBJECTIVE

To identify predictors of higher acute-care treatment costs and length of stay for patients with traumatic spinal cord injury (TSCI).

SUMMARY OF BACKGROUND DATA

There are few current or population-based estimates of acute hospitalization costs, length of stay, and other outcomes for people with TSCI, on which to base future planning for specialist SCI health care services.

METHODS

Record linkage study using healthcare utilization and costs data; all patients aged more than or equal to 16 years with incident TSCI in the Australian state of New South Wales (June 2013-June 2016). Generalized Linear Model regression to identify predictors of higher acute care treatment costs for patients with TSCI. Scenario analysis quantified the proportionate cost impacts of patient pathway modification.

RESULTS

Five hundred thirty-four incident cases of TSCI (74% male). Total cost of all acute index episodes approximately AUD$40.5 (95% confidence interval [CI] ±4.5) million; median cost per patient was AUD$45,473 (Interquartile Range: $15,535-$94,612). Patient pathways varied; acute care was less costly for patients admitted directly to a specialist spinal cord injury unit (SCIU) compared with indirect transfer within 24 hours. Over half (53%) of all patients experienced at least one complication during acute admission; their care was less costly if they had been admitted directly to SCIU. Scenario analysis demonstrated that a reduction of indirect transfers to SCIU by 10% yielded overall cost savings of AUD$3.1 million; an average per patient saving of AUD$5,861.

CONCLUSION

Direct transfer to SCIU for patients with acute TSCI resulted in lower treatment costs, shorter length of stay, and less costly complications. Modeling showed that optimizing patient-care pathways can result in significant acute-care cost savings. Reducing potentially preventable complications would further reduce costs and improve longer-term patient outcomes.

LEVEL OF EVIDENCE

3.

Unintentional injuries: A profile of hospitalization and risk factors for in-hospital mortality in Beijing, China

INTRODUCTION

Unintentional injuries (UIs) impose a significant burden on low- and middle-income countries (LMICs). However, available UI epidemiological data are limited for LMICs, including China. This article aimed to provide an overview of the UI hospitalization profile, identify risk factors for in-hospital mortality and provide diagnosis-specific survival risk ratios (SRRs) for reference by LMICs using hospital discharge abstract data (DAD) from Beijing, China.

PATIENTS AND METHODS

A cross-sectional study was conducted for patients sustaining UIs requiring admission. Information was retrieved from 138 hospitals in Beijing to describe the demographics, injury nature, mechanisms, severity and hospital outcomes. Multivariate logistic regression was performed to identify and evaluate risk factors for in-hospital mortality for UIs.

RESULTS

Falls (57.1%), transport accidents (19.9%) and exposure to inanimate mechanical forces (16.4%) were the leading causes of UI hospitalization. Falls and transport accidents were responsible for 94.2% of the in-hospital deaths caused by UIs. Injury mechanisms differed among sex (χ² = 5322.1, P <  0.001) and age (χ² = 24,143.3, P <  0.001) groups. Male sex (OR: 1.50, 95% confidence interval (CI): 1.23-1.79), age ≥ 85 years (OR: 16.39, 95% CI: 7.46-36.00), Barthel Index at admission ≤ 60 (OR: 25.78, 95% CI: 13.30-49.95), modified Charlson comorbidity index ≥ 6 (OR: 2.60, 95% CI: 1.91-3.55), International Classification of Diseases-based injury severity score (ICISS) < 0.85 (OR: 15.17, 95% CI: 12.57-18.30), sustaining injuries to the head/neck (OR: 23.20, 95% CI: 7.31-73.64), injuries caused by foreign body entering through natural orifice (OR: 34.00, 95%CI: 6.37-181.54) and injuries resulting from transport accidents (OR: 1.71, 95% CI: 1.41-2.07) were important risk factors for in-hospital mortality for UIs.

CONCLUSIONS

Hospital DAD are an objective and cost-effective data source that allows for a hospital-based perspective of UI epidemiology. Sex, age, functional status at admission, comorbidities, injury nature, severity and mechanism are significantly associated with the in-hospital mortality of UIs in China. This study generates a reference dataset of diagnosis-specific SRRs from a large trauma population in China, which may be more applicable in injury severity estimation using ICISS in LMICs.

Performance of injury severity measures in trauma research: a literature review and validation analysis of studies from low-income and middle-income countries

INTRODUCTION

Characterisation of injury severity is an important pillar of scientific research to measure and compare the outcomes. Although majority of injury severity measures were developed in high-income countries, many have been studied in low-income and middle-income countries (LMICs). We conducted this study to identify and characterise all injury severity measures, describe how widely and frequently they are used in trauma research from LMICs, and summarise the evidence on their performance based on empirical and theoretical validation​ analysis.

METHODS

First, a list of injury measures was identified through PubMed search. Subsequently, a systematic search of PubMed, Global Health and EMBASE was undertaken on LMIC trauma literature published from January 2006 to June 2016, in order to assess the application and performance of injury severity measures to predict in-hospital mortality. Studies that applied one or more global injury severity measure(s) on all types of injuries were included, with the exception of war injuries and isolated organ injuries.

RESULTS

Over a span of 40 years, more than 55 injury severity measures were developed. Out of 3862 non-duplicate citations, 597 studies from 54 LMICs were listed as eligible studies. Full-text review revealed 37 studies describing performance of injury severity measures for outcome prediction. Twenty-five articles from 13 LMICs assessed the validity of at least one injury severity measure for in-hospital mortality. Injury severity score was the most commonly validated measure in LMICs, with a wide range of performance (area under the receiver operating characteristic curve (AUROC) between 0.9 and 0.65). Trauma and Injury Severity Score validation studies reported AUROC between 0.80 and 0.98.

CONCLUSION

Empirical studies from LMICs frequently use injury severity measures, however, no single injury severity measure has shown a consistent result in all settings or populations and thus warrants validation studies for the diversity of LMIC population.

Effect of preadmission beta-blockade on mortality in multiple trauma

Background

High levels of circulating catecholamines after multiple trauma have been associated with increased morbidity and mortality. Beta‐adrenergic receptor antagonist (beta‐blocker) therapy has emerged as a potential treatment option, but the effect of preinjury beta‐blockade on trauma‐induced mortality is unclear. The aim of this study was to assess whether preinjury beta‐blocker therapy is associated with reduced mortality after multiple trauma.

Methods

Severely injured patients, aged at least 50 years, admitted to a level one trauma centre over a 10‐year interval were linked to national and local registries of co‐morbidities, prescription drug use and level of education. The association between preinjury beta‐blocker use and 30‐day mortality was explored using logistic regression analysis.

Results

Some 1376 patients were included; 338 (24·6 per cent) were receiving beta‐blockers at the time of trauma. Beta‐blocker users had an increased crude 30‐day mortality rate compared with that for non‐users: 32·8 versus 19·7 per cent respectively (P < 0·001). After adjustment for baseline imbalances and injury‐related factors, there was no association between preinjury beta‐blocker use and mortality (OR 1·09, 95 per cent c.i. 0·70 to 1·70). Separate analyses of individuals with or without severe head injury did not significantly change this association. There was no significant difference in the rate of shock between beta‐blocker users and non‐users.

Conclusion

Pretrauma beta‐blockade is not associated with 30‐day mortality beyond the effects of age, co‐morbidity and injury severity.

Identification and internal validation of models for predicting survival and ICU admission following a traumatic injury

Background

Measures to improve the accuracy of determining survival and intensive care unit (ICU) admission using the International Classification of Injury Severity Score (ICISS) are not often conducted on a population-wide basis. The aim is to determine if the predictive ability of survival and ICU admission using ICISS can be improved depending on the method used to derive ICISS and incremental inclusion of covariates.

Method

A retrospective analysis of linked injury hospitalisation and mortality data during 1 January 2010 to 30 June 2014 in New South Wales, Australia was conducted. Both multiplicative-injury and single-worst-injury ICISS were calculated. Logistic regression examined 90-day mortality and ICU admission with a range of predictor variables. The models were assessed in terms of their ability to discriminate survivors and non-survivors, model fit, and variation explained.

Results

There were 735,961 index injury admissions, 13,744 (1.9%) deaths within 90-days and 23,054 (3.1%) ICU admissions. The best predictive model for 90-day mortality was single-worst-injury ICISS including age group, gender, all comorbidities, trauma centre type, injury mechanism, and nature of injury as covariates. The multiplicative-injury ICISS with age group, gender, all comorbidities, injury mechanism, and nature of injury was the best predictive model for ICU admission.

Conclusions

The inclusion of comorbid conditions, injury mechanism and nature of injury, improved discrimination for both 90-day mortality and ICU admission. Moves to routinely use ICD-based injury severity measures, such as ICISS, should be considered for hospitalisation data replacing more resource-intensive injury severity classification measures.

Electronic supplementary material

The online version of this article (10.1186/s13049-018-0563-5) contains supplementary material, which is available to authorized users.

Predicting mortality with the international classification of disease injury severity score using survival risk ratios derived from an Indian trauma population: A cohort study

Background

Trauma is predicted to become the third leading cause of death in India by 2020, which indicate the need for urgent action. Trauma scores such as the international classification of diseases injury severity score (ICISS) have been used with great success in trauma research and in quality programmes to improve trauma care. To this date no valid trauma score has been developed for the Indian population.

Study design

This retrospective cohort study used a dataset of 16047 trauma-patients from four public university hospitals in urban India, which was divided into derivation and validation subsets. All injuries in the dataset were assigned an international classification of disease (ICD) code. Survival Risk Ratios (SRRs), for mortality within 24 hours and 30 days were then calculated for each ICD-code and used to calculate the corresponding ICISS. Score performance was measured using discrimination by calculating the area under the receiver operating characteristics curve (AUROCC) and calibration by calculating the calibration slope and intercept to plot a calibration curve.

Results

Predictions of 30-day mortality showed an AUROCC of 0.618, calibration slope of 0.269 and calibration intercept of 0.071. Estimates of 24-hour mortality consistently showed low AUROCCs and negative calibration slopes.

Conclusions

We attempted to derive and validate a version of the ICISS using SRRs calculated from an Indian population. However, the developed ICISS-scores overestimate mortality and implementing these scores in clinical or policy contexts is not recommended. This study, as well as previous reports, suggest that other scoring systems might be better suited for India and other Low- and middle-income countries until more data are available.

Decreased risk adjusted 30-day mortality for hospital admitted injuries: a multi-centre longitudinal study

Background

The interpretation of changes in injury-related mortality over time requires an understanding of changes in the incidence of the various types of injury, and adjustment for their severity. Our aim was to investigate changes over time in incidence of hospital admission for injuries caused by falls, traffic incidents, or assaults, and to assess the risk-adjusted short-term mortality for these patients.

Methods

All patients admitted to hospital with injuries caused by falls, traffic incidents, or assaults during the years 2001–11 in Sweden were identified from the nationwide population-based Patient Registry. The trend in mortality over time for each cause of injury was adjusted for age, sex, comorbidity and severity of injury as classified from the International Classification of diseases, version 10 Injury Severity Score (ICISS).

Results

Both the incidence of fall (689 to 636/100000 inhabitants: p = 0.047, coefficient − 4.71) and traffic related injuries (169 to 123/100000 inhabitants: p < 0.0001, coefficient − 5.37) decreased over time while incidence of assault related injuries remained essentially unchanged during the study period. There was an overall decrease in risk-adjusted 30-day mortality in all three groups (OR 1.00; CI95% 0.99–1.00). Decreases in traffic (OR 0.95; 95% CI 0.93 to 0.97) and assault (OR 0.93; 95% CI 0.87 to 0.99) related injuries was significant whereas falls were not during this 11-year period.

Discussion

Risk-adjustment is a good way to use big materials to find epidemiological changes. However after adjusting for age, year, sex and risk we find that a possible factor is left in the pre- and/or in-hospital care.

Conclusions

The decrease in risk-adjusted mortality may suggest changes over time in pre- and/or in-hospital care. A non-significantdecrease in risk-adjusted mortality was registered for falls, which may indicate that low-energy trauma has not benefited for the increased survivability as much as high-energy trauma, ie traffic- and assault related injuries.

Development and validation of an ICD-10-based disability predictive index for patients admitted to hospitals with trauma

BACKGROUND

There was no established disability predictive measurement for patients with trauma that could be used in administrative claims databases. The aim of the present study was to develop and validate a diagnosis-based disability predictive index for severe physical disability at discharge using the International Classification of Diseases, 10th revision (ICD-10) coding.

METHODS

This retrospective observational study used the Diagnosis Procedure Combination database in Japan. Patients who were admitted to hospitals with trauma and discharged alive from 01 April 2010 to 31 March 2015 were included. Pediatric patients under 15 years old were excluded. Data for patients admitted to hospitals from 01 April 2010 to 31 March 2013 was used for development of a disability predictive index (derivation cohort), while data for patients admitted to hospitals from 01 April 2013 to 31 March 2015 was used for the internal validation (validation cohort). The outcome of interest was severe physical disability defined as the Barthel Index score of <60 at discharge. Trauma-related ICD-10 codes were categorized into 36 injury groups with reference to the categorization used in the Global Burden of Diseases study 2013. A multivariable logistic regression analysis was performed for the outcome using the injury groups and patient baseline characteristics including patient age, sex, and Charlson Comorbidity Index (CCI) score in the derivation cohort. A score corresponding to a regression coefficient was assigned to each injury group. The disability predictive index for each patient was defined as the sum of the scores. The predictive performance of the index was validated using the receiver operating characteristic curve analysis in the validation cohort.

RESULTS

The derivation cohort included 1,475,158 patients, while the validation cohort included 939,659 patients. Of the 939,659 patients, 235,382 (25.0%) were discharged with severe physical disability. The c-statistics of the disability predictive index was 0.795 (95% confidence interval [CI] 0.794-0.795), while that of a model using the disability predictive index and patient baseline characteristics was 0.856 (95% CI 0.855-0.857).

CONCLUSIONS

Severe physical disability at discharge may be well predicted with patient age, sex, CCI score, and the diagnosis-based disability predictive index in patients admitted to hospitals with trauma.

Impact of hospital volume on mortality in patients with severe torso injury

BACKGROUND

Whether a positive volume-outcome relationship exists in the context of trauma remains controversial. Heterogeneity in the definition of hospital volume in previous studies is one of the main reasons for this inconclusiveness. We investigated whether hospital volume is associated with mortality in patients with severe torso injury using two different definitions of hospital volume.

MATERIALS AND METHODS

This retrospective cohort study used the Diagnosis Procedure Combination database in Japan. Patients who were admitted to tertiary emergency centers with severe torso injury and underwent emergency surgery or interventional radiology treatment for the torso injury upon admission from April 1, 2010 to March 31, 2014 were included. Hospital volume was defined as the annual number of admissions with severe torso injury (HV-torso) or the annual number of total trauma admissions (HV-all). The main outcome was 28-d mortality. Multivariable logistic regression models fitted with generalized estimating equations were used to evaluate relationships between hospital volume and 28-d mortality.

RESULTS

Overall, 7725 patients were included. The 28-d mortality rate was 15.3%. The HV-torso was significantly associated with reduced 28-d mortality (adjusted odds ratio = 0.59; 95% confidence interval = 0.44-0.79). However, there was no significant association between the HV-all and mortality (adjusted odds ratio = 1.02; 95% confidence interval = 0.72-1.46).

CONCLUSIONS

The HV-torso was significantly associated with reduced mortality in patients with severe torso injury. In contrast, the HV-all had no significant relationship with their mortality. Regionalization of trauma care for severe torso injury may be beneficial for patients with severe torso injury.

Performance of International Classification of Diseases-based injury severity measures used to predict in-hospital mortality and intensive care admission among traumatic brain-injured patients

BACKGROUND

The International Classification of Diseases (ICD) is the main classification system used for population-based traumatic brain injury (TBI) surveillance activities but does not contain direct information on injury severity. International Classification of Diseases-based injury severity measures can be empirically derived or mapped to the Abbreviated Injury Scale, but no single approach has been formally recommended for TBI.

OBJECTIVE

The aim of this study was to compare the accuracy of different ICD-based injury severity measures for predicting in-hospital mortality and intensive care unit (ICU) admission in TBI patients.

METHODS

We conducted a population-based retrospective cohort study. We identified all patients 16 years or older with a TBI diagnosis who received acute care between April 1, 2006, and March 31, 2013, from the Quebec Hospital Discharge Database. The accuracy of five ICD-based injury severity measures for predicting mortality and ICU admission was compared using measures of discrimination (area under the receiver operating characteristic curve [AUC]) and calibration (calibration plot and the Hosmer-Lemeshow goodness-of-fit statistic).

RESULTS

Of 31,087 traumatic brain-injured patients in the study population, 9.0% died in hospital, and 34.4% were admitted to the ICU. Among ICD-based severity measures that were assessed, the multiplied derivative of ICD-based Injury Severity Score (ICISS-Multiplicative) demonstrated the best discriminative ability for predicting in-hospital mortality (AUC, 0.858; 95% confidence interval, 0.852-0.864) and ICU admissions (AUC, 0.813; 95% confidence interval, 0.808-0.818). Calibration assessments showed good agreement between observed and predicted in-hospital mortality for ICISS measures. All severity measures presented high agreement between observed and expected probabilities of ICU admission for all deciles of risk.

CONCLUSIONS

The ICD-based injury severity measures can be used to accurately predict in-hospital mortality and ICU admission in TBI patients. The ICISS-Multiplicative generally outperformed other ICD-based injury severity measures and should be preferred to control for differences in baseline characteristics between TBI patients in surveillance activities or injury research when only ICD codes are available.

LEVEL OF EVIDENCE

Prognostic study, level III.

Relationship between hospital volume and outcomes in patients with traumatic brain injury: A retrospective observational study using a national inpatient database in Japan

BACKGROUND

The relationship between hospital volume and outcome after traumatic brain injury (TBI) is not completely understood in a real clinical setting. We investigated whether patients admitted with TBI achieved better outcomes in high-volume hospitals than in low-volume hospitals using a national inpatient database in Japan.

METHODS

This retrospective cohort study used the Diagnosis Combination Procedure database in Japan. We included patients with TBI admitted to hospitals with a Japan Coma Scale (JCS) score ≥2 between April 1, 2013 and March 31, 2014. Hospital volume was defined as the annual number of all admissions with TBI in individual hospitals. The hospital volume was categorized into four volume groups: low (≤60 admissions per hospital), medium-low (61-120 admissions per hospital), medium-high (121-180 admissions per hospital) and high (≥181 admissions per hospital). The outcomes of interest included 28-day mortality and survival discharge with complete dependency defined as a Barthel Index score of 0 at discharge. We used multivariate logistic regression models fitted with generalized estimating equations to evaluate relationships between the hospital volume and the outcomes. The hospital volume was evaluated both as categorical variables defined above and as continuous variables.

RESULTS

The analysis dataset consisted of 20,146 eligible patients. Of these, 2,784 died within 28days (13.8%) and 3,409 were completely dependent among 16,996 patients discharged alive (20.1%). Multivariate analyses found that there was no significant difference between the high-volume and low-volume groups for 28-day mortality (adjusted odds ratio [OR] 0.79, 95% confidence interval [CI] 0.58-1.06 for the high-volume group) or complete dependency at discharge (adjusted OR 0.94, 95% CI 0.71-1.23 for the high-volume group). The results were the same when the hospital volume was evaluated as a continuous variable.

CONCLUSIONS

Hospital volume did not appear to influence outcomes in patients with TBI. High-volume hospitals may not be necessarily beneficial for patients with TBI exhibiting impaired consciousness as a whole.

A closer look at non-accidental trauma: Caregiver assault compared to non-caregiver assault

PURPOSE

The purpose of this study was to examine the outcomes of non-accidental trauma (NAT) patients compared to other trauma (OT) patients across the state of Florida. In addition, NAT and OT patients with a mechanism of injury of assault were further analyzed.

METHODS

A statewide database was reviewed from January 2010 to December 2014 for patients aged 0-18years who presented following trauma. Patients were sorted by admitting diagnosis into two groups: rule out NAT and all other diagnoses. Patients with a mechanism of assault were subanalyzed and outcomes were compared.

RESULTS

There were 46,557 patients included. NAT patients were younger, had more severe injuries and had a higher mortality rate compared to OT patients. Assault was the mechanism of injury in 95% of NAT patients. NAT assault patients were younger, required more intensive care unit (ICU) resources, and had a higher mortality rate compared to other assault patients.

CONCLUSION

Non-accidental trauma patients require more resources and have a higher mortality rate compared to accidental trauma patients, and these differences remain even when controlling for the mechanism of injury.

LEVEL OF EVIDENCE

III.

Mortality prediction models in the general trauma population: A systematic review

BACKGROUND

Trauma is the leading cause of death in individuals younger than 40 years. There are many different models for predicting patient outcome following trauma. To our knowledge, no comprehensive review has been performed on prognostic models for the general trauma population. Therefore, this review aimed to describe (1) existing mortality prediction models for the general trauma population, (2) the methodological quality and (3) which variables are most relevant for the model prediction of mortality in the general trauma population.

METHODS

An online search was conducted in June 2015 using Embase, Medline, Web of Science, Cinahl, Cochrane, Google Scholar and PubMed. Relevant English peer-reviewed articles that developed, validated or updated mortality prediction models in a general trauma population were included.

RESULTS

A total of 90 articles were included. The cohort sizes ranged from 100 to 1,115,389 patients, with overall mortality rates that ranged from 0.6% to 35%. The Trauma and Injury Severity Score (TRISS) was the most commonly used model. A total of 258 models were described in the articles, of which only 103 models (40%) were externally validated. Cases with missing values were often excluded and discrimination of the different prediction models ranged widely (AUROC between 0.59 and 0.98). The predictors were often included as dichotomized or categorical variables, while continuous variables showed better performance.

CONCLUSION

Researchers are still searching for a better mortality prediction model in the general trauma population. Models should 1) be developed and/or validated using an adequate sample size with sufficient events per predictor variable, 2) use multiple imputation models to address missing values, 3) use the continuous variant of the predictor if available and 4) incorporate all different types of readily available predictors (i.e., physiological variables, anatomical variables, injury cause/mechanism, and demographic variables). Furthermore, while mortality rates are decreasing, it is important to develop models that predict physical, cognitive status, or quality of life to measure quality of care.

Development and validation of a new ICD-10-based trauma mortality prediction scoring system using a Japanese national inpatient database

INTRODUCTION

To develop and validate a new trauma mortality prediction scoring system based on International Statistical Classification of Diseases (ICD)-10 codes, using a Japanese administrative claims and discharge abstract database.

METHODS

This retrospective observational study used the Japanese Diagnosis Procedure Combination database. Injuries were categorised into 33 groups with 5 additional groups based on injury sites and types. A multivariable logistic regression analysis was performed for in-hospital mortality in a derivation cohort after adjusting for the 38 groups, patient's sex, age and Charlson Comorbidity Index score. Each variable was assigned a score that was equal to the value of the regression coefficient. The new severity score was defined as the sum of the scores. The new scoring system was tested in a validation cohort.

RESULTS

The mortality rates were 2.4% (9270/393 395) and 2.5% (8778/349 285) in the derivation and validation cohorts, respectively. The area under the receiver operating curve (AUROC) of the new scoring system was 0.887 (95% CI 0.884 to 0.890) in the validation cohort. Subgroup analyses showed that the scoring system retained high predictive performance both for patients <65 years (AUROC 0.934, 95% CI 0.928 to 0.939) and for elderly patients at the age of ≥65 years (AUROC 0.825, 95% CI 0.820 to 0.829).

CONCLUSIONS

A new ICD-10-based injury severity scoring system was developed and validated. Further studies are required to validate the scoring system in other databases.