Trauma Mortality Prediction Model Research Articles

Outcomes of Hospitalized Injured Suspects Sustaining Gunshot Wounds From Law Enforcement Action.

Although firearms are implicated in the majority of law enforcement intervention (LEI)-related deaths, scientific research is lacking. The present study sought to characterize clinical and financial outcomes between injured suspects and other gunshot wound (GSW) patients. The 2016-2020 National Inpatient Sample was queried for patients ≥16years old admitted following GSW. Patients were categorized as injured suspects (ISs) if they were injured in LEI and non-IS otherwise. The primary outcome was in-hospital mortality with complications, hospitalization duration (LOS), and costs secondarily considered. Multivariable regression models were used to adjust for patient characteristics, injury burden using the Trauma Mortality Prediction Model (TMPM), and hospital factors. Of 143,125 hospitalizations, 1575 (1.10%) were IS. Compared to non-IS, ISs were less frequently Black (24.4% vs 54.3%) but had a higher proportion of psychiatric conditions (19.4% vs 6.4%) (P < .05). Although having a similar requirement for major operations and TMPM score, ISs more frequently underwent thoracic (11.4% vs 4.1%) and gastrointestinal operations (33.0% vs 25.7%) (P < .05). After adjustment, IS was associated with similar odds of mortality but was associated with greater odds of cardiac complications, respiratory failure, and need for intensive care. While LOS was similar, IS was associated with greater costs (β: +$14,300, 95% CI: 6,200-22,400). Suspects injured during law enforcement intervention have similar in-hospital mortality but greater complication rates and costs. Through the quantification of the clinical and financial burden of IS, our findings may help inform further policy discussions regarding use of potentially lethal force in law enforcement intervention.

National Trends and Clinical Outcomes after Scooter Injury in the US: 2016 to 2020.

In recent years, the adoption of electric scooters has been accompanied by a surge of scooter-related injuries in the US, raising concerns for their severity and associated healthcare costs. This study aimed to assess temporal trends and outcomes of scooter-related hospital admissions compared with bicycle-related hospitalizations. This was a retrospective cohort study using the 2016 to 2020 National Inpatient Sample for patients younger than 65 years who were hospitalized after bicycle- and scooter-related injuries. The Trauma Mortality Prediction Model was used to quantify injury severity. The primary outcomes of interest were temporal trends of micromobility injuries. In-hospital mortality, rates of long bone fracture, traumatic brain injury, paralysis, length of stay, hospitalization costs, and nonhome discharge were secondarily assessed. Among 92,815 patients included in the study, 6,125 (6.6%) had scooter-related injuries. Compared with patients with bicycle-related injuries, patients with scooter-related injuries were more commonly younger than 18 years (26.7% vs 16.4%, p < 0.001) and frequently underwent major operations (55.8% vs 48.1%, p < 0.001). After risk adjustment, scooter-related injuries were associated with greater risks of long bone fracture (adjusted odds ratio 1.40, 95% CI 1.15 to 1.70) and paralysis (adjusted odds ratio 2.06, 95% CI 1.16 to 3.69) compared with bicycle-related injuries. Additionally, patients with bicycle- or scooter-related injuries had comparable index hospitalization durations of stay and costs. The prevalence and severity of scooter-related injuries have significantly increased in the US, thereby attributing to a substantial cost burden on the healthcare system. Multidisciplinary efforts to inform safety policies and enact targeted interventions are warranted to reduce scooter-related injuries.

Pediatric penetrating thoracic trauma: Examining the impact of trauma center designation and penetrating trauma volume on outcomes

IntroductionWe analyzed the impact of treating center designation and case volume of penetrating trauma on outcomes after pediatric penetrating thoracic injuries (PTI). MethodsPTI patients <18 years were identified from the National Trauma Data Bank (2013–2016). Centers were categorized by type (Pediatric or Adult) and designation status (Level I, Level II, and other). Performance was calculated as the difference between observed and expected mortality and standardized using the total penetrating trauma volume per center. Expected mortality was calculated using the Trauma Mortality Prediction Model. Pearson correlation and linear mixed-effects models evaluated the association between variables and performance. ResultsWe identified 4,134 PTI patients treated at 596 trauma centers: 879 (21%) at Adult Level I, 608 (15%) at Adult Level II, 531 (13%) at Pediatric Level I, 320 (8%) at Pediatric Level II, and 1,796 (43%) at other centers. Primary injury mechanisms were firearm-related (58%) and cut/piercing (42%). Overall mortality was 16% and median predicted mortality was 3.6% (IQR: 1.5% - 11.2%). Among patients with thoracic firearm-related injuries, centers with lower penetrating case volume and total trauma care demonstrated significantly worse outcomes. Multivariable analysis revealed Adult Level I centers had superior outcomes compared with all other non-Level I centers. There was no difference in mortality between Pediatric and Adult Level I centers. DiscussionAdult Level I trauma center designation and annual case volume of penetrating thoracic trauma are associated with improved mortality after pediatric firearm-related thoracic injuries. Further study is needed to identify factors in higher volume centers that improve outcomes. Level of evidenceLevel III.

Traumatic injury mortality prediction (TRIMP-ICDX): A new comprehensive evaluation model according to the ICD-10-CM codes.

Various assessment methods based on the International Classification of Diseases, Tenth Edition, Clinical Modification (ICD-10-CM), such as ICD-10-CM Injury Severity Score (ICISS), trauma mortality prediction model (TMPM-ICD10), and injury mortality prediction (IMP-ICDX), are purely anatomic trauma assessment, which need to be further improved. Traumatic injury mortality prediction (TRIMP-ICDX) is a comprehensive assessment method based on anatomic injuries and incorporating available information to determine whether it is superior to Trauma and Injury Severity Score (TRISS) and IMP-ICDX in predicting trauma outcomes. This retrospective cohort study was based on data from 704,287 trauma patients admitted to 710 trauma centers in the National Trauma Data Bank of the United States in 2016. The TRIMP-ICDX was established using anatomical injury, physiological reserves, and physiological response indicators. Its performance was compared with the IMP-ICDX and TRISS by examining the area under the receiver operating characteristic curve (AUC), calibration (Hosmer-Lemeshow goodness-of-fit test, HL), and the Akaike information criterion (AIC). The TRIMP-ICDX showed significantly better discrimination (AUCTRIMP-ICDX 0.968; 95% confidence interval (CI), 0.966–0.970, AUCTRISS 0.922; 95% CI, 0.918–0.925, and AUCIMP-ICDX 0.894; 95% CI, 0.890–0.899), better calibration (HLTRIMP-ICDX 5.6; 95% CI, 3.0–8.0, HLTRISS 72.7; 95% CI, 38.4–104.5, and HLIMP-ICDX 53.1; 95% CI, 26.6–77.8), and a lower AIC (AICTRIMP-ICDX 24,774, AICTRISS 30,753, and AICIMP-ICDX 32,780) compared with TRISS and IMP-ICDX. Similar results were found in statistical comparisons among different body regions. As a comprehensive evaluation method based on the ICD-10-CM lexicon TRIMP-ICDX is significantly better than IMP-ICDX and TRISS with respect to both discriminative power and calibration. The TRIMP-ICDX should become a research method for the comprehensive evaluation of trauma severity.

Traumatic Brain Injury and Risk of Alzheimer's Disease and Related Dementias in the Population.

Epidemiological studies examining associations between traumatic brain injury (TBI) and Alzheimer's disease and related dementias (ADRD) have yielded conflicting results, which may be due to methodological differences. To examine the relationship between the presence and severity of TBI and risk of ADRD using a population-based cohort with medical record abstraction for confirmation of TBI and ADRD. All TBI events among Olmsted County, Minnesota residents aged > 40 years from 1985-1999 were confirmed by manual review and classified by severity. Each TBI case was randomly matched to two age-, sex-, and non-head injury population-based referents without TBI. For TBI events with non-head trauma, the Trauma Mortality Prediction Model was applied to assign an overall measure of non-head injury severity and corresponding referents were matched on this variable. Medical records were manually abstracted to confirm ADRD diagnosis. Cox proportional hazards models examined the relationship between TBI and severity with risk of ADRD. A total of 1,418 residents had a confirmed TBI (865 Possible, 450 Probable, and 103 Definite) and were matched to 2,836 referents. When combining all TBI severities, the risk of any ADRD was significantly higher for those with a confirmed TBI compared to referents (HR = 1.32, 95% CI: 1.11, 1.58). Stratifying by TBI severity, Probable (HR = 1.42, 95% CI: 1.05, 1.92) and Possible (HR = 1.29, 95% CI: 1.02-1.62) TBI was associated with an increased risk of ADRD, but not Definite TBI (HR = 1.22, 95% CI: 0.68, 2.18). Our analyses support including TBI as a potential risk factor for developing ADRD.

A traumatic injury mortality prediction (TRIMP) based on a comprehensive assessment of abbreviated injury scale 2005 predot codes

Abbreviated Injury Scale (AIS)-based systems such as injury severity score (ISS), exponential injury severity score (EISS), trauma mortality prediction model (TMPM), and injury mortality prediction (IMP), classify anatomical injuries with limited accuracy. The widely accepted alternative, trauma and injury severity score (TRISS), improves the prediction rate by combining an anatomical index of ISS, physiological index (the Revised Trauma Score, RTS), and the age of patients. The study introduced the traumatic injury mortality prediction (TRIMP) with the inclusion of extra clinical information and aimed to compare the ability against the TRISS as predictors of survival. The hypothesis was that TRIMP would outperform TRISS in prediction power by incorporating clinically available data. This was a retrospective cohort study where a total of 1,198,885 injured patients hospitalized between 2012 and 2014 were subset from the National Trauma Data Bank (NTDB) in the United States. A TRIMP model was computed that uses AIS 2005 (AIS_05), physiological reserve and physiological response indicators. The results were analysed by examining the area under the receiver operating characteristic curve (AUC), the Hosmer–Lemeshow (HL) statistic, and the Akaike information criterion. TRIMP gave both significantly better discrimination (AUCTRIMP, 0.964; 95% confidence interval (CI), 0.962 to 0.966 and AUCTRISS, 0.923; 95% CI, 0.919 to 0.926) and calibration (HLTRIMP, 14.0; 95% CI, 7.7 to 18.8 and HLTRISS, 411; 95% CI, 332 to 492) than TRISS. Similar results were found in statistical comparisons among different body regions. TRIMP was superior to TRISS in terms of accurate of mortality prediction, TRIMP is a new and feasible scoring method in trauma research and should replace the TRISS.

The derivation of an International Classification of Diseases, Tenth Revision-based trauma-related mortality model using machine learning.

Existing mortality prediction models have attempted to quantify injury burden following trauma-related admissions with the most notable being the Injury Severity Score (ISS). Although easy to calculate, it requires additional administrative coding. International Classification of Diseases (ICD)-based models such as the Trauma Mortality Prediction Model (TMPM-ICD10) circumvent these limitations, but they use linear modeling, which may not adequately capture the intricate relationships of injuries on mortality. Using ICD-10 coding and machine learning (ML) algorithms, the present study used the National Trauma Data Bank to develop mortality prediction models whose performance was compared with logistic regression, ISS, and TMPM-ICD10. The 2015 to 2017 National Trauma Data Bank was used to identify adults following trauma-related admissions. Of 8,021 ICD-10 codes, injuries were categorized into 1,495 unique variables. The primary outcome was in-hospital mortality. eXtreme Gradient Boosting (XGBoost), a ML technique that uses iterations of decision trees, was used to develop mortality models. Model discrimination was compared with logistic regression, ISS, and TMPM-ICD10 using receiver operating characteristic curve and probabilistic accuracy with calibration curves. Of 1,611,063 patients, 54,870 (3.41%) experienced in-hospital mortality. Compared with those who survived, those who died more frequently suffered from penetrating trauma and had a greater number of injuries. The XGBoost model exhibited superior receiver operating characteristic curve (0.863 [95% confidence interval (CI), 0.862-0.864]) compared with logistic regression (0.845 [95% CI, 0.844-0.846]), ISS (0.828 [95% CI, 0.827-0.829]), and TMPM-ICD10 (0.861 [95% CI, 0.860-0.862]) (all p < 0.001). Importantly, the ML model also had significantly improved calibration compared with other methodologies (XGBoost, coefficient of determination (R2) = 0.993; logistic regression, R2 = 0.981; ISS, R2 = 0.649; TMPM-ICD10, R2 = 0.830). Machine learning models using XGBoost demonstrated superior performance and calibration compared with logistic regression, ISS, and TMPM-ICD10. Such approaches in quantifying injury severity may improve its utility in mortality prognostication, quality improvement, and trauma research. Prognostic and Epidemiologic; level III.

Impact of opioid use disorder on resource utilization and readmissions after operative trauma

BackgroundAlthough patients with opioid use disorder have been shown to be more susceptible to traumatic injury, the impact of opioid use disorder after trauma-related admission remains poorly characterized. The present nationally representative study evaluated the association of opioid use disorder on clinical outcomes after traumatic injury warranting operative intervention. MethodsThe 2010 to 2018 Nationwide Readmissions Database was used to identify adult trauma victims who underwent major operative procedures. Injury severity was quantified using International Classification of Diseases Trauma Mortality Prediction Model. Entropy balancing was used to adjust for intergroup differences. Multivariable regression models were developed to assess the association of opioid use disorder on in-hospital mortality, perioperative complications, resource utilization, and readmissions. ResultsOf an estimated 5,089,003 hospitalizations, 54,097 (1.06%) had a diagnosis of opioid use disorder with increasing prevalence during the study period. Compared with others, opioid use disorder had a lower proportion of extremity injuries and falls but greater predicted mortality measured by Trauma Mortality Prediction Model. After adjustment, opioid use disorder was associated with decreased odds of in-hospital mortality (adjusted odds ratio: 0.61; 95% confidence interval, 0.53–0.70) but had greater likelihood of pneumonia, infectious complications, and acute kidney injury. Additionally, opioid use disorder was associated with longer hospitalization duration as well as greater index costs and risk of readmission within 30 days (adjusted odds ratio: 1.36; 95% confidence interval, 1.25–1.49). ConclusionOpioid use disorder in operative trauma has significantly increased in prevalence and is associated with decreased in-hospital index mortality but greater resource utilization and readmission.

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

BackgroundThe 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.MethodsWe 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.ResultsIMP-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.ConclusionsThe 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.

A trauma mortality prediction model based on the ICD-10-CM lexicon: TMPM-ICD10.

Outcome prediction models allow risk adjustment required for trauma research and the evaluation of outcomes. The advent of ICD-10-CM has rendered risk adjustment based on ICD-9-CM codes moot, but as yet no risk adjustment model based on ICD-10-CM codes has been described. The National Trauma Data Bank provided data from 773,388 injured patients who presented to one of 747 trauma centers in 2016 with traumatic injuries ICD-10-CM codes and Injury Severity Score (ISS). We constructed an outcome prediction model using only ICD-10-CM acute injury codes and compared its performance with that of the ISS. Compared with ISS, the TMPM-ICD-10 discriminated survivors from non-survivors better (ROC TMPM-ICD-10 = 0.861 [0.860-0.872], ROC [reviever operating curve] ISS = 0.830 [0.823-0.836]), was better calibrated (HL [Hosmer-Lemeshow statistic] TMPM-ICD-10 = 49.01, HL ISS = 788.79), and had a lower Akaike information criteria (AIC TMPM-ICD10 = 30579.49; AIC ISS = 31802.18). Because TMPM-ICD10 provides better discrimination and calibration than the ISS and can be computed without recourse to Abbreviated Injury Scale coding, the TMPM-ICD10 should replace the ISS as the standard measure of overall injury severity for data coded in the ICD-10-CM lexicon. Prognostic/Epidemiologic, level II.

The trauma severity model: An ensemble machine learning approach to risk prediction

Statistical theory indicates that a flexible model can attain a lower generalization error than an inflexible model, provided that the setting is appropriate. This is highly relevant for mortality risk prediction with trauma patients, as researchers have focused exclusively on the use of generalized linear models for trauma risk prediction, and generalized linear models may be too inflexible to capture the potentially complex relationships in trauma data. To improve trauma risk prediction, we propose a machine learning model, the Trauma Severity Model (TSM). In order to validate TSM's performance, this study compares TSM to three established risk prediction models: the Bayesian Logistic Injury Severity Score, the Harborview Assessment for Risk of Mortality, and the Trauma Mortality Prediction Model. Our results indicate that TSM has superior predictive performance on National Trauma Data Bank data and on Nationwide Readmission Database data.

Using similarity measures for medical event sequences to predict mortality in trauma patients

We extend a similarity measure for medical event sequences (MESs) and evaluate its classification performance for retrospective mortality prediction of trauma patient outcomes. Retrospective mortality prediction is a benchmarking task used by trauma care governance bodies to assist with policy decisions. We extend the similarity measure, the Optimal Temporal Common Subsequence for MESs (OTCS-MES), by generalizing the event-matching component with a plug-in weighting element. The extended OTCS-MES uses an event prevalence weight developed in our previous study and an event severity weight developed for this study. In the empirical evaluation of classification performance, we provide a more complete evaluation than previous studies. We compare the predictive performance of the Trauma Mortality Prediction Model (TMPM), an accepted regression approach for mortality prediction in trauma data, to nearest neighbor algorithms using similarity measures for MESs. Using a data set from the National Trauma Data Bank, our results indicate improved predictive performance for an ensemble of nearest neighbor classifiers over TMPM. Our analysis reveals a superior Receiver Operating Characteristics (ROC) curve, larger AUC, and improved operating points on a ROC curve. We also study methods to adjust for uncommon class prediction: weighted voting, neighborhood size, and case base size. Results provide strong evidence that similarity measures for medical event sequences are a powerful and easily adapted method assisting with health care policy advances.

Validation of the trauma mortality prediction scores from a Malaysian population

BackgroundWell-known trauma mortality prediction scores such as New Injury Severity Score (NISS), Revised Trauma Score (RTS), and Trauma and Injury Severity Score (TRISS) have been externally validated from high-income countries with established trauma databases. However, these scores were never used in Malaysian population. In this current study, we attempted to validate these scoring systems using our regional trauma surgery database.MethodsA retrospective analysis of the regional Malaysian Trauma Surgery Database was performed over a period of 3 years from May 2011 to April 2014. NISS, RTS, Major Trauma Outcome Study (MTOS)-TRISS, and National Trauma Database (NTrD)-TRISS scores were recorded and calculated. Individual scoring system’s performance in predicting trauma mortality was compared by calculating the area under the receiver operating characteristic (AUC) curve. Youden index and associated optimal cutoff values for each scoring system was calculated to predict mortality. The corresponding positive predictive value, negative predictive value, and accuracy of the cutoff values were calculated.ResultsA total of 2208 trauma patients (2004 blunt and 204 penetrating injuries) with mean age of 36 (SD = 16) years were included. There were 239 deaths with a corresponding mortality rate of 10.8%. The AUC calculated for the NISS, RTS, MTOS-TRISS, and NTrD-TRISS were 0.878, 0.802, 0.812, and 0.848, respectively. The NISS score with a cutoff value of 24, sensitivity of 86.6% and specificity of 74.3%, outperformed the rest (p < 0.001). Mortality was predicted by NISS with an overall accuracy of 75.6%; its positive predictive value was at 29.02% and negative predictive value at 97.86%.ConclusionAmongst the four scores, the NISS score is the best trauma mortality prediction model suited for a local Malaysian trauma population. Further validation with multicentre data in the country may require to ascertain the finding.

Impact of atrial fibrillation on outcomes with motor vehicle accidents.

We examined the effect of AF a commonly encountered arrhythmia with significant morbidity on mortality following a motor vehicle accident (MVA) related hospitalization. The Nationwide Inpatient Sample (NIS) was queried to identify patients with AF (ICD-9 CM 427.31) and MVA (ICD-9 CM E810.0-E819.9), considered separately and together, from 2003 through 2012. Baseline characteristics were identified and multilevel mixed model multivariate analysis was employed to verify the impact of AF on in-patient mortality in survivors. Of an estimated 2,978,630 MVA admissions reported, 79,687 (2.6%) hospitalizations also had a diagnosis of AF. The in-hospital mortality was 2.6% in MVA alone and 7.6% in MVA and AF. In multivariate analysis, after adjustment for age, gender, Charlson Comorbidity Index (CCI), the Trauma Mortality Prediction Model (TMPM), and hospital characteristics, AF was independently associated with in-hospital mortality [Odds ratio (OR) 1.52, confidence interval (CI) 1.41-1.69, P value<0.0001]. In patients with MVA and AF, increasing age, CCI, and TMPM were associated with higher mortality. Female gender is associated with lower mortality (OR 0.84, CI 0.81-0.88, P -0.0016). Most patients with MVA and AF had a CHADS2 score of 2 (34.6%). Mortality and transfusion rates were higher in MVA and AF patients compared to patients with MVA alone across all CHADS2 scores. In patients with a MVA, the presence of AF is an independent risk factor for in-hospital mortality.

An injury mortality prediction based on the anatomic injury scale.

To determine whether the injury mortality prediction (IMP) statistically outperforms the trauma mortality prediction model (TMPM) as a predictor of mortality.The TMPM is currently the best trauma score method, which is based on the anatomic injury. Its ability of mortality prediction is superior to the injury severity score (ISS) and to the new injury severity score (NISS). However, despite its statistical significance, the predictive power of TMPM needs to be further improved.Retrospective cohort study is based on the data of 1,148,359 injured patients in the National Trauma Data Bank hospitalized from 2010 to 2011. Sixty percent of the data was used to derive an empiric measure of severity of different Abbreviated Injury Scale predot codes by taking the weighted average death probabilities of trauma patients. Twenty percent of the data was used to create computing method of the IMP model. The remaining 20% of the data was used to evaluate the statistical performance of IMP and then be compared with the TMPM and the single worst injury by examining area under the receiver operating characteristic curve (ROC), the Hosmer-Lemeshow (HL) statistic, and the Akaike information criterion.IMP exhibits significantly both better discrimination (ROC-IMP, 0.903 [0.899-0.907] and ROC-TMPM, 0.890 [0.886-0.895]) and calibration (HL-IMP, 9.9 [4.4-14.7] and HL-TMPM, 197 [143-248]) compared with TMPM. All models show slight changes after the extension of age, gender, and mechanism of injury, but the extended IMP still dominated TMPM in every performance.The IMP has slight improvement in discrimination and calibration compared with the TMPM and can accurately predict mortality. Therefore, we consider it as a new feasible scoring method in trauma research.

Comparison of modified Kampala trauma score with trauma mortality prediction model and trauma-injury severity score: A National Trauma Data Bank Study

BackgroundMortality prediction of trauma patients relies on anatomical, physiological or combined scores. The purpose of this study is to compare the diagnostic accuracy of the modified Kampala Trauma Score (M-KTS) with the Trauma Mortality Prediction Model (TMPM), and Trauma-Injury Severity Score (TRISS) using data from a large dataset from a developed registry, the National Trauma Data Bank (NTDB). MethodsUsing 2011 and 2012 data from NTDB, patient based trauma scores (M-KTS, TMPM, and TRISS) were calculated and predictive ability of M-KTS for mortality was compared with other trauma scores using receiver operating characteristics (ROC) curves. ResultsA total of 841089 patients were included in the study. TRISS outperformed other scores (AUC=0.922, %95 CI 0.920–0.924) with M-KTS as the second best score (AUC=0.901, %95 CI 0.899–0.903) followed by TMPM (AUC=0.887, 95% CI 0.844–0.889). For blunt trauma, TRISS (AUC=0.917, 95% CI 0.915–0.919) performed better than M-KTS (AUC=0.891, %95 CI 0.889–0.893) and TMPM (AUC=0.874, 95% CI 0.871–0.877). For penetrating trauma, M-KTS (AUC=0.956, 95% CI 0.954–0.959) and TMPM (AUC=0.955, 95% CI 0.951–0.958) had similar performance after TRISS (AUC=0.969, 95% CI 0.967–0.971). ConclusionM-KTS performed worse than TRISS although its' main advantage is simple use in resource-limited settings.

Risk factors for venous thromboembolism after acute trauma: A population-based case-cohort study

BackgroundPredictors of venous thromboembolism (VTE) after trauma are uncertain. ObjectiveTo identify independent predictors of VTE after acute trauma. MethodsUsing Rochester Epidemiology Project (REP) resources, we identified all Olmsted County, MN residents with objectively-diagnosed incident VTE within 92days after hospitalization for acute trauma over the 18-year period, 1988–2005. We also identified all Olmsted County residents hospitalized for acute trauma over this time period and chose one to two residents frequency-matched to VTE cases on sex, event year group and ICD-9-CM trauma code predictive of surgery. In a case-cohort study, demographic, baseline and time-dependent characteristics were tested as predictors of VTE after trauma using Cox proportional hazards modeling. ResultsAmong 200 incident VTE cases, the median (interquartile range) time from trauma to VTE was 18 (6, 41) days. Of these, 62% cases developed VTE after hospital discharge. In a multiple variable model including 370 cohort members, patient age at injury, male sex, increasing injury severity as reflected by the Trauma Mortality Prediction Model (TMPM) Mortality Score, immobility prior to trauma, soft tissue leg injury, and prior superficial vein thrombosis were independent predictors of VTE (C-statistic=0.78). ConclusionsWe have identified clinical characteristics which can identify patients at increased risk for VTE after acute trauma, independent of surgery. Almost two thirds of all incident VTE events occurred after initial hospital discharge (18day median time from trauma to VTE) which questions current practice of not extending VTE prophylaxis beyond hospital discharge.

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

The International Classification of Diseases (ICD) is the main classification system used for population-based injury surveillance activities but does not contain information on injury severity. ICD-based injury severity measures can be empirically derived or mapped, but no single approach has been formally recommended. This study aimed to compare the performance of ICD-based injury severity measures to predict in-hospital mortality among injury-related admissions. A systematic review and a meta-analysis were conducted. MEDLINE, EMBASE, and Global Health databases were searched from their inception through September 2014. Observational studies that assessed the performance of ICD-based injury severity measures to predict in-hospital mortality and reported discriminative ability using the area under a receiver operating characteristic curve (AUC) were included. Metrics of model performance were extracted. Pooled AUC were estimated under random-effects models. Twenty-two eligible studies reported 72 assessments of discrimination on ICD-based injury severity measures. Reported AUC ranged from 0.681 to 0.958. Of the 72 assessments, 46 showed excellent (0.80 ≤ AUC < 0.90) and 6 outstanding (AUC ≥ 0.90) discriminative ability. Pooled AUC for ICD-based Injury Severity Score (ICISS) based on the product of traditional survival proportions was significantly higher than measures based on ICD mapped to Abbreviated Injury Scale (AIS) scores (0.863 vs. 0.825 for ICDMAP-ISS [p = 0.005] and ICDMAP-NISS [p = 0.016]). Similar results were observed when studies were stratified by the type of data used (trauma registry or hospital discharge) or the provenance of survival proportions (internally or externally derived). However, among studies published after 2003 the Trauma Mortality Prediction Model based on ICD-9 codes (TMPM-9) demonstrated superior discriminative ability than ICISS using the product of traditional survival proportions (0.850 vs. 0.802, p = 0.002). Models generally showed poor calibration. ICISS using the product of traditional survival proportions and TMPM-9 predict mortality more accurately than those mapped to AIS codes and should be preferred for describing injury severity when ICD is used to record injury diagnoses. Systematic review and meta-analysis, level III.

Race and rehabilitation following spinal cord injury: equality of access for American Indians/Alaska Natives compared to other racial groups.

BackgroundRepresenting 2 % of the general population, American Indians/Alaska Natives (AIs/ANs) were associated with 0.5 % (63) of the estimated 12,500 new cases of spinal cord injury (SCI) reported to the National Spinal Cord Injury Statistic Center in 2013. To date, the trend in health care disparities among AIs/ANs in the SCI community has not been examined. We sought to compare the rate of discharge to rehabilitation facilities (DRF) following traumatic SCI among adult AIs/ANs to other racial/ethnic groups for patients 15 to 64 years old.MethodsUtilizing data from the National Trauma Data Bank (NTDB), we performed a retrospective analysis of SCI cases occurring between January 1, 2008 and December 31, 2012. SCI injuries were identified by International Classification of Diseases 9th Revision-Clinical Modification (ICD-9) codes or Abbreviated Injury Scale (AIS) scores. Injury severity was determined using the Trauma Mortality Prediction Model (TMPM) which empirically estimates each patient’s probability of death given their individual complement of injuries. A series of seven logistic regression models were used to predict DRF between racial groups.ResultsAmong the 29,443 patients in our cohort, 52.4 % were discharged to rehabilitation facilities. AIs/ANs comprised 1.1 % of the population, with 63.8 % dismissed to rehabilitation. AIs/ANs were significantly younger, had a higher probability of death, had longer hospital length of stay (HLOS), and were proportionately more likely to be discharged to rehabilitation compared to non-AIs. Regression models demonstrated increased odds of DRF for AIs/ANs compared to Hispanic and Asian racial/ethnic groups.ConclusionsAmerican Indians/Alaska Natives who sustain SCI access rehabilitative care at a rate equitable to or greater than other races when multiple factors are taken into account. Further research is needed to assess the effect of those patient, physician, and health care system determinants as they relate to a patient’s ability to access post-trauma rehabilitative care. Recommendations include advancing the level of racial, insurance, and geographic data necessary to adequately explore disparities related to such ubiquitously life-altering conditions as SCI.

The impact of solid organ injury management on the US health care system.

Since the 1980s, there has been a paradigm shift toward nonoperative management of stable patients with solid organ injury. The impact of this practice change on national health care expenditure has not been well characterized. Hospital discharge data from the Healthcare Cost Utilization Project Nationwide Inpatient Sample from every other year spanning 1994 to 2010 were studied using patients with a primary diagnosis of splenic and liver injury. Cost analysis was performed using cost-to-charge ratios, where actual costs of hospitalization with current management practices were compared with theoretical costs projecting 1994 practice patterns. Length of stay (LOS) was evaluated similarly to costs. Mortality risk was established using the validated Trauma Mortality Prediction Model. Data from 29,409 adult patients with splenic injury and 14,704 with liver injury were used for cost and LOS analysis. The proportion of patients undergoing nonoperative management increased from 38% to 67% for splenic injury and from 62% to 81% for liver injury. The mean cost for splenic injury dropped by $8,421 per patient, a net reduction in total costs per admission of 29.5% (p < 0.0001), resulting in a mean estimated $12 million per year reduction in cost of care in 2008 alone. For liver injury, cost has been reduced by $8,822 per patient, a 27.7% reduction (p < 0.0001), with a net $17 million per year savings. LOS has been reduced by a mean ± SE of 1.9 ± 0.7 days per splenic injury (p = 0.0001) and 2.2 ± 0.9 days for liver injury (p = 0.0001). Mortality rate of high-risk patients (Trauma Mortality Prediction Model > 0.3) treated conservatively for splenic injury fell from 30% to 20% and from 64% to 18% for liver injury. The trend toward nonoperative management of solid organ injury has resulted in a substantial decrease in health care expenditure and LOS while improving mortality for high-risk patients. Advances in trauma care can have significant impact on the cost of health care. Economic analysis, level III.