Comparison of National Surgical Quality Improvement Program Surgical Risk Calculator, Trauma and Injury Severity Score, and American Society of Anesthesiologists Physical Status to predict operative trauma mortality in elderly patients

Standard spontaneous breathing trial parameters may not predict unplanned reintubation for trauma patients

BACKGROUND

The applicability of spontaneous breathing trial (SBT) factors such as negative inspiratory force (NIF) and rapid shallow breathing index (RSBI) as predictors of reintubation in trauma patients (TPs) is unclear. This study aimed to identify predictors of unplanned reintubation (UR) in TPs.

METHODS

A single center, retrospective (1/2017-12/2023) study of TPs ≥18 years-old extubated from endotracheal mechanical ventilation was performed. Patients with UR during admission were compared to patients without UR. A multivariable logistic regression was performed to identify risk factors associated with UR.

RESULTS

39 of 424 ​TPs (9.2 ​%) had UR. UR patients were older (median: 55 vs 39 years-old, p ​= ​0.012) and more often had congestive heart failure (10.3 ​% vs 1.6 ​%, p ​< ​0.001), cirrhosis (7.7 ​% vs 1.9 ​%, p ​= ​0.025), end stage renal disease (7.7 ​% vs 1.6 ​%, p ​= ​0.044), and a higher injury severity scores (ISS) (median: 27 vs 18, p ​< ​0.001). UR patients had increased ventilator days (median: 6 vs 2, p ​< ​0.001) prior to extubation, whereas RSBI and NIF were similar (median: 36 vs 32, p ​= ​0.508) and (median: -24.0 vs -27.0 ​cm ​H2O, p ​= ​0.190). On multivariable analysis, RSBI <50 or <105 and NIF ​< ​-20 were not associated with UR. Age (OR 1.03, CI 1.01-1.05, p ​= ​0.006) and ISS (OR 1.04, CI 1.01-1.08, p ​= ​0.022) were independently associated with increased risk of UR.

CONCLUSIONS

SBT parameters (RSBI and NIF) were not associated with UR. Age and ISS were independently associated with UR. This suggests additional patient-specific factors should help guide extubation decisions for TPs.

Early Care of Polytraumatized Patients: A Framework for Orthopaedic Surgeons

The role of orthopaedic surgeons during trauma activations is vague and often underused. Advanced trauma life support (ATLS) is a training program and framework for performing initial life- and limb-threatening interventions. ATLS was created by Dr. James Styner, an orthopaedic surgeon, to systematically evaluate and treat trauma patients after his family received suboptimal initial care following a plane crash in 1976. There are numerous orthopaedic assessments done during the ATLS primary and secondary surveys. Understanding hierarchy and sequencing of these interventions may enhance orthopaedic integration into the broader resuscitation and surgical efforts. ATLS training is not standard in US orthopaedic residency programs. Fundamental understanding of ventilation parameters and resuscitative protocols enhance decision making for the extent of orthopaedic surgical intervention acutely. Defining indications for emergent interventions among other surgical specialties improves multidisciplinary surgical planning. This review aims to answer the question, "What needs to be done now using the ATLS survey framework and how can an orthopaedic surgeon contribute?" Furthermore, this review intends to introduce ATLS for orthopaedic surgeons in supportive roles with surgical and nonsurgical responsibilities by describing basic protocols and evidence of benefit.

An Abdominal Seat Belt Sign is Associated With Similar Incidence of Hollow Viscus Injury but Increased In-Hospital Mortality in Older Adult Trauma Patients: A PCSA Multicenter Study

BACKGROUND

The abdominal seat belt sign (SBS) is associated with an increased risk of hollow viscus injury (HVI). Older age is associated with worse outcomes in trauma patients. Thus, older trauma patients ≥65 years of age (OTPs) may be at an increased risk of HVI with abdominal SBS. Therefore, we hypothesized an increased incidence of HVI and mortality for OTPs vs younger trauma patients (YTPs) with abdominal SBS.

STUDY DESIGN

This post hoc analysis of a multi-institutional, prospective, observational study (8/2020-10/2021) included patients >18 years old with an abdominal SBS who underwent abdominal computed tomography (CT) imaging. Older trauma patients were compared to YTPs (18-64 years old) with bivariate analyses.

RESULTS

Of the 754 patients included in this study from nine level-1 trauma centers, there were 110 (14.6%) OTPs and 644 (85.4%) YTPs. Older trauma patients were older (mean 75.3 vs 35.8 years old, P < .01) and had a higher mean Injury Severity Score (10.8 vs 9.0, P = .02). However, YTPs had an increased abdominal abbreviated-injury scale score (2.01 vs 1.63, P = .02). On CT imaging, OTPs less commonly had intraabdominal free fluid (21.7% vs 11.9%, P = .02) despite a similar rate of abdominal soft tissue contusion (P > .05). Older trauma patients also had a statistically similar rate of HVI vs YTPs (5.5% vs 9.8%, P = .15). Despite this, OTPs had increased mortality (5.5% vs 1.1%, P < .01) and length of stay (LOS) (5.9 vs 4.9 days P < .01).

CONCLUSION

Despite a similar rate of HVI, OTPs with an abdominal SBS had an increased rate of mortality and LOS. This suggests the need for heightened vigilance when caring for OTPs with abdominal SBS.

Lack of Concordance Between Abbreviated Injury Scale and American Association for the Surgery of Trauma Organ Injury Scale in Patients with High-Grade Solid Organ Injury

BACKGROUND

The Abbreviated Injury Scale (AIS) is widely used for body region-specific injury severity. The American Association for the Surgery of Trauma Organ Injury Scale (AAST-OIS) provides organ-specific injury severity but is not included in trauma databases. Previous researchers have used AIS as a surrogate for OIS. This study aims to assess AIS-abdomen concordance with AAST-OIS grade for liver and spleen injuries, hypothesizing concordance in terms of severity (grade of OIS and AIS) and patient outcomes.

STUDY DESIGN

This retrospective study (July 2020 to June 2022) was performed at 3 trauma centers. Adult trauma patients with AAST-OIS grade III to V liver and spleen injury were included. AAST-OIS grade for each organ was compared with AIS-abdomen by evaluating the percentage of AAST-OIS grade correlating with each AIS score as well as rates of operative intervention for these injuries. Analysis was performed with chi-square tests and univariate analysis.

RESULTS

Of 472 patients, 274 had liver injuries and 205 had spleen injuries grades III to V. AAST-OIS grade III to V liver injuries had concordances rates of 85.5%, 71%, and 90.9% with corresponding AIS 3 to 5 scores. AAST-OIS grade III to V spleen injuries had concordances rates of 89.7%, 87.8%, and 87.3%, respectively. There was a statistical lack of concordance for both liver and spleen injuries (both p < 0.001). Additionally, there were higher rates of operative intervention for AAST-OIS grade IV and V liver injuries and grade III and V spleen injuries vs corresponding AIS scores (p < 0.05).

CONCLUSIONS

AIS should not be used interchangeably with OIS due to lack of concordance. AAST-OIS should be included in trauma databases to facilitate improved organ injury research and quality improvement projects.

Immediate Operative Trauma Assessment Score: A Simple and Reliable Predictor of Mortality in Trauma Patients Undergoing Urgent/Emergent Surgery

Objective: Many current trauma mortality prediction tools are either too intricate or rely on data not readily available during a trauma patient's initial evaluation. Moreover, none are tailored to those necessitating urgent or emergent surgery. Our objective was to design a practical, user-friendly scoring tool using immediately available variables, and then compare its efficacy to the widely-known Revised Trauma Score (RTS). Methods: The adult 2017-2021 Trauma Quality Improvement Program (TQIP) database was queried to identify patients ≥18 years old undergoing any urgent/emergent operation (direct from Emergency Department to operating room). Patients were divided into derivation and validation groups. A three-step methodology was used. First, multiple logistic regression models were created to determine risk of death using only variables available upon arrival. Second, the weighted average and relative impact of each independent predictor was used to derive an easily calculated Immediate Operative Trauma Assessment Score (IOTAS). We then validated IOTAS using AUROC and compared it to RTS. Results: From 249 208 patients in the derivation-set, 14 635 (5.9%) died. Age ≥65, Glasgow Coma Scale score <9, hypotension (SBP <90 mmHg), and tachycardia (>120/min) on arrival were identified as independent predictors for mortality. Using these, the IOTAS was structured, offering scores between 0-8. The AUROC for this was .88. A clear escalation in mortality was observed across scores: from 4.4% at score 1 to 60.5% at score 8. For the validation set (250 182 patients; mortality rate 5.8%), the AUROC remained consistent at .87, surpassing RTS's AUROC of .83. Conclusion: IOTAS is a novel, accurate, and now validated tool that is intuitive and efficient in predicting mortality for trauma patients requiring urgent or emergent surgeries. It outperforms RTS, and thereby may help guide clinicians when determining the best course of action in patient management as well as counseling patients and their families.

Using machine learning to predict outcomes of patients with blunt traumatic aortic injuries

BACKGROUND

The optimal management of blunt thoracic aortic injury (BTAI) remains controversial, with experienced centers offering therapy ranging from medical management to TEVAR. We investigated the utility of a machine learning (ML) algorithm to develop a prognostic model of risk factors on mortality in patients with BTAI.

METHODS

The Aortic Trauma Foundation registry was utilized to examine demographics, injury characteristics, management and outcomes of patients with BTAI. A STREAMLINE (A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison) model as well as logistic regression (LR) analysis with imputation using chained equations was developed and compared.

RESULTS

From a total of 1018 patients in the registry, 702 patients were included in the final analysis. Of the 258 (37%) patients who were medically managed, 44 (17%) died during admission, 14 (5.4%) of which were aortic related deaths. Four hundred forty-four (63%) patients underwent TEVAR and 343 of which underwent TEVAR within 24 hours of admission. Among TEVAR patients, 39 (8.8%) patients died and 7 (1.6%) had aortic related deaths ( Table 1 ). Comparison of the STREAMLINE and LR model showed no significant difference in ROC curves and high AUCs of 0.869 (95% confidence interval, 0.813-0.925) and 0.840 (95% confidence interval, 0.779-0.900) respectively in predicting in-hospital mortality. Unexpectedly, however, the variables prioritized in each model differed between models. The top 3 variables identified from the LR model were similar to that from existing literature. The STREAMLINE model, however, prioritized location of the injury along the lesser curve, age and aortic injury grade.

CONCLUSION

Machine learning provides insight on prioritization of variables not typically identified in standard multivariable logistic regression. Further investigation and validation in other aortic injury cohorts are needed to delineate the utility of ML models.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

Performance of trauma scoring systems in predicting mortality in geriatric trauma patients: comparison of the ISS, TRISS, and GTOS based on a systemic review and meta-analysis

PURPOSE

This meta-analysis aimed to evaluate the performance of the Injury Severity Score (ISS), Trauma and Injury Severity Score (TRISS), and the Geriatric Trauma Outcome Score (GTOS) in predicting mortality in geriatric trauma patients.

METHODS

The MEDLINE, Web of Science, and EMBASE databases were searched for studies published from January 2008 to October 2023. Studies assessing the performance of the ISS, TRISS, or GTOS in predicting mortality in geriatric trauma patients (over 60 years old) and reporting data for the analysis of the pooled area under the receiver operating characteristic curve (AUROC) and the hierarchical summary receiver operating characteristic curve (HSROC) were included. Studies that were not conducted in a group of geriatric patients, did not consider mortality as the outcome variable, or had incomplete data were excluded. The Critical Appraisal Skills Programme (CASP) Clinical Prediction Rule Checklist was utilized to assess the risk of bias in included studies. STATA 16.0. was used for the AUROC analysis and HSROC analysis.

RESULTS

Nineteen studies involving 118,761 geriatric trauma patients were included. The pooled AUROC of the TRISS (AUC = 0.82, 95% CI: 0.77-0.87) was higher than ISS (AUC = 0.74, 95% CI: 0.71-0.79) and GTOS (AUC = 0.80, 95%CI: 0.77-0.83). The diagnostic odds ratio (DOR) calculated from HSROC curves also suggested that the TRISS (DOR = 21.5) had a better performance in predicting mortality in geriatric trauma patients than the ISS (DOR = 6.27) and GTOS (DOR = 4.76).

CONCLUSION

This meta-analysis suggested that the TRISS showed better accuracy and performance in predicting mortality in geriatric trauma patients than the ISS and GTOS.

Comparison of National Surgical Quality Improvement Program Surgical Risk Calculator and Trauma and Injury Severity Score Risk Assessment Tools in Predicting Outcomes in High-Risk Operative Trauma Patients

BACKGROUND

The Trauma and Injury Severity Score (TRISS) uses anatomic/physiologic variables to predict outcomes. The National Surgical Quality Improvement Program Surgical Risk Calculator (NSQIP-SRC) includes functional status and comorbidities. It is unclear which of these tools is superior for high-risk trauma patients (American Society of Anesthesiologists Physical Status (ASA-PS) class IV or V). This study compares risk prediction of TRISS and NSQIP-SRC for mortality, length of stay (LOS), and complications for high-risk operative trauma patients.

METHODS

This is a prospective study of high-risk (ASA-PS IV or V) trauma patients (≥18 years-old) undergoing surgery at 4 trauma centers. We compared TRISS vs NSQIP-SRC vs NSQIP-SRC + TRISS for ability to predict mortality, LOS, and complications using linear, logistic, and negative binomial regression.

RESULTS

Of 284 patients, 48 (16.9%) died. The median LOS was 16 days and number of complications was 1. TRISS + NSQIP-SRC best predicted mortality (AUROC: .877 vs .723 vs .843, P = .0018) and number of complications (pseudo-R2/median error (ME) 5.26%/1.15 vs 3.39%/1.33 vs 2.07%/1.41, P < .001) compared to NSQIP-SRC or TRISS, but there was no difference between TRISS + NSQIP-SRC and NSQIP-SRC with LOS prediction (P = .43).

DISCUSSION

For high-risk operative trauma patients, TRISS + NSQIP-SRC performed better at predicting mortality and number of complications compared to NSQIP-SRC or TRISS alone but similar to NSQIP-SRC alone for LOS. Thus, future risk prediction and comparisons across trauma centers for high-risk operative trauma patients should include a combination of anatomic/physiologic data, comorbidities, and functional status.

Predicting the Future in Trauma: Trauma and Injury Severity Score Loses Accuracy and Validity for Late Deaths After Injury

BACKGROUND

The Trauma and Injury Severity Score (TRISS) is widely used to predict mortality in trauma patients, but its performance metrics have not been analyzed for early vs later deaths. Therefore, we aimed to investigate the impact of time to death on the accuracy of TRISS.

METHODS

Patients from 2013 to 2018 American College of Surgeons Trauma Quality Improvement Program database were included. We compared predicted survival by TRISS using the areas under receiver operating characteristic curves (AUCs) and calibration curves between different cut-off times and subgroups. We further compared early (≤72 hr) and late (>72 hr) deaths based on mechanisms and severity.

RESULTS

Among the 1,180,745 patients, the total mortality rate was 6.4%, with 59% early deaths and 41% late deaths. The AUC of TRISS for all patients was .919 (95% CI: .918-.921) for ≤72 hr survival and .845 (95% CI: .843-.848) for >72 hr survival. Significant discrepancies in AUCs between the early and late death groups existed in all cohorts based on blunt/penetrating mechanisms and severity. TRISS predicted well in early survival of penetrating injury but was less reliable in late survival of penetrating injury and all blunt injury. TRISS tended to underestimate survival, particularly for patients with lower probability of survival, with increased discrepancies seen for predicting late deaths.

CONCLUSIONS

The predictive ability of TRISS varies significantly based on the timing of deaths and key injury factors. TRISS may be best utilized in predicting early survival in penetrating injury, but its reliability and accuracy diminish when predicting late deaths for all kinds of injury.