Predictors of elderly mortality after trauma: A novel outcome score

Efficacy of the Geriatric Trauma Outcome Score (GTOS) in Predicting Mortality in Trauma Patients: A Retrospective Cross-Sectional Study

BACKGROUND

Trauma has a profound impact on mortality as well as short- and long-term health outcomes. For trauma patients to receive medical care in a timely manner, early identification and risk assessment are essential. The Geriatric Trauma Outcome Score (GTOS), which was created by combining age, the Injury Severity Score (ISS), and the requirement for packed red blood cell transfusion, has proven to be a valuable prognostic tool for elderly trauma patients, though its applicability to general trauma patients is still understudied.

METHODS

This retrospective study analyzed data from the Trauma Registry System at a Level I trauma center in southern Taiwan, covering the period from 1 January 2009 to 31 December 2021. This study included 40,068 trauma patients aged 20 years and older. Statistical analyses included chi-square tests, ANOVA, Mann-Whitney U tests, and multivariate analyses to identify independent risk factors for mortality. The predictive performance of the GTOS was assessed using the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

The final study population included 40,068 patients, with 818 deaths and 39,250 survivors. Deceased patients had higher GTOS scores (mean 132.8 vs. 76.1, p < 0.001) and required more blood transfusions (mean 4.0 vs. 0.3 units, p < 0.001) compared to survivors. The optimal GTOS cut-off value for predicting mortality was 104.5, with a sensitivity of 82.6% and a specificity of 84.3% (AUC = 0.917). A high GTOS score was associated with increased mortality (9.6 vs. 0.4%, p < 0.001) compared with a low GTOS score, even after adjusting for confounding factors (adjusted mortality rate of 2.86, p < 0.001), and a longer hospital stay (14.0 vs. 7.7 days, p < 0.001).

CONCLUSIONS

The GTOS is a valuable prognostic tool for predicting mortality in trauma patients, providing a simple and rapid assessment method. Its high predictive accuracy supports its use in broader trauma patient populations beyond the elderly. Further studies are recommended to refine and validate the GTOS in diverse trauma settings to enhance its clinical utility.

Development and Validation of Futility of Resuscitation Measure in Older Adult Trauma Patients

INTRODUCTION

This study aimed to develop and validate Futility of Resuscitation Measure (FoRM) for predicting the futility of resuscitation among older adult trauma patients.

METHODS

This is a retrospective analysis of the American College of Surgeons-Trauma Quality Improvement Program database (2017-2018) (derivation cohort) and American College of Surgeons level I trauma center database (2017-2022) (validation cohort). We included all severely injured (injury severity score >15) older adult (aged ≥60 y) trauma patients. Patients were stratified into decades of age. Injury characteristics (severe traumatic brain injury [Glasgow Coma Scale ≤ 8], traumatic brain injury midline shift), physiologic parameters (lowest in-hospital systolic blood pressure [≤1 h], prehospital cardiac arrest), and interventions employed (4-h packed red blood cell transfusions, emergency department resuscitative thoracotomy, resuscitative endovascular balloon occlusion of the aorta, emergency laparotomy [≤2 h], early vasopressor requirement [≤6 h], and craniectomy) were identified. Regression coefficient-based weighted scoring system was developed using the Schneeweiss method and subsequently validated using institutional database.

RESULTS

A total of 5562 patients in derivation cohort and 873 in validation cohort were identified. Mortality was 31% in the derivation cohort and FoRM had excellent discriminative power to predict mortality (area under the receiver operator characteristic = 0.860; 95% confidence interval [0.847-0.872], P < 0.001). Patients with a FoRM score of >16 had a less than 10% chance of survival, while those with a FoRM score of >20 had a less than 5% chance of survival. In validation cohort, mortality rate was 17% and FoRM had good discriminative power (area under the receiver operator characteristic = 0.76; 95% confidence interval [0.71-0.80], P < 0.001).

CONCLUSIONS

FoRM can reliably identify the risk of futile resuscitation among older adult patients admitted to our level I trauma center.

Markers of Futile Resuscitation in Traumatic Hemorrhage: A Review of the Evidence and a Proposal for Futility Time-Outs during Massive Transfusion

The reduction in the blood supply following the 2019 coronavirus pandemic has been exacerbated by the increased use of balanced resuscitation with blood components including whole blood in urban trauma centers. This reduction of the blood supply has diminished the ability of blood banks to maintain a constant supply to meet the demands associated with periodic surges of urban trauma resuscitation. This scarcity has highlighted the need for increased vigilance through blood product stewardship, particularly among severely bleeding trauma patients (SBTPs). This stewardship can be enhanced by the identification of reliable clinical and laboratory parameters which accurately indicate when massive transfusion is futile. Consequently, there has been a recent attempt to develop scoring systems in the prehospital and emergency department settings which include clinical, laboratory, and physiologic parameters and blood products per hour transfused as predictors of futile resuscitation. Defining futility in SBTPs, however, remains unclear, and there is only nascent literature which defines those criteria which reliably predict futility in SBTPs. The purpose of this review is to provide a focused examination of the literature in order to define reliable parameters of futility in SBTPs. The knowledge of these reliable parameters of futility may help define a foundation for drawing conclusions which will provide a clear roadmap for traumatologists when confronted with SBTPs who are candidates for the declaration of futility. Therefore, we systematically reviewed the literature regarding the definition of futile resuscitation for patients with trauma-induced hemorrhagic shock, and we propose a concise roadmap for clinicians to help them use well-defined clinical, laboratory, and viscoelastic parameters which can define futility.

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.

Prognostic Association between Injury Severity Score and the Outcomes of Elderly Patients with Trauma in South Korea

This study investigated the impact of the Injury Severity Score (ISS) on treatment approaches and survival outcomes in trauma patients, focusing on comparing elderly (≥65 years) with non-elderly patients. It analyzed adult trauma cases with abnormal Revised Trauma Scores from January to December 2019, categorizing patients into three severity groups based on ISS: mild (1-8), moderate (9-15), and severe (≥16). The study examined how ISS influenced therapeutic interventions and survival among elderly patients, comparing these outcomes to non-elderly patients using multivariable logistic regression analysis. In 16,336 adult trauma cases out of 52,262 patients, including 4886 elderly and 11,450 non-elderly patients, findings revealed that in the severe group, elderly patients had a lower, though not statistically significant, incidence of surgical or embolization interventions compared to the moderate group, differing from non-elderly patients. No significant differences were observed in the mild group between elderly and non-elderly patients. However, elderly patients had higher intervention rates in the moderate group and lower in the severe group, with significantly lower survival-to-discharge rates in the severe group. The ISS is insufficient for assessing trauma severity in elderly patients. Additional tools are needed for better evaluation and treatment decisions.

The 2023 WSES guidelines on the management of trauma in elderly and frail patients

BACKGROUND

The trauma mortality rate is higher in the elderly compared with younger patients. Ageing is associated with physiological changes in multiple systems and correlated with frailty. Frailty is a risk factor for mortality in elderly trauma patients. We aim to provide evidence-based guidelines for the management of geriatric trauma patients to improve it and reduce futile procedures.

METHODS

Six working groups of expert acute care and trauma surgeons reviewed extensively the literature according to the topic and the PICO question assigned. Statements and recommendations were assessed according to the GRADE methodology and approved by a consensus of experts in the field at the 10th international congress of the WSES in 2023.

RESULTS

The management of elderly trauma patients requires knowledge of ageing physiology, a focused triage, including drug history, frailty assessment, nutritional status, and early activation of trauma protocol to improve outcomes. Acute trauma pain in the elderly has to be managed in a multimodal analgesic approach, to avoid side effects of opioid use. Antibiotic prophylaxis is recommended in penetrating (abdominal, thoracic) trauma, in severely burned and in open fractures elderly patients to decrease septic complications. Antibiotics are not recommended in blunt trauma in the absence of signs of sepsis and septic shock. Venous thromboembolism prophylaxis with LMWH or UFH should be administrated as soon as possible in high and moderate-risk elderly trauma patients according to the renal function, weight of the patient and bleeding risk. A palliative care team should be involved as soon as possible to discuss the end of life in a multidisciplinary approach considering the patient's directives, family feelings and representatives' desires, and all decisions should be shared.

CONCLUSIONS

The management of elderly trauma patients requires knowledge of ageing physiology, a focused triage based on assessing frailty and early activation of trauma protocol to improve outcomes. Geriatric Intensive Care Units are needed to care for elderly and frail trauma patients in a multidisciplinary approach to decrease mortality and improve outcomes.

Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis

BACKGROUND

Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.

DESIGN

Systematic review and meta-analyses.

PARTICIPANTS

Older adults (≥ 65 years) in any setting.

INTERVENTION

Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.

OUTCOME MEASURES

Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.

RESULTS

Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.

CONCLUSION

The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.

Changes in patterns of traumatic brain injury in the Michigan Trauma Quality Improvement Program database early in the COVID-19 pandemic

OBJECTIVE

The authors' objective was to investigate the impact of the global COVID-19 pandemic on hospital presentation and process of care for the treatment of traumatic brain injuries (TBIs). Improved understanding of these effects will inform sociopolitical and hospital policies in response to future pandemics.

METHODS

The Michigan Trauma Quality Improvement Program (MTQIP) database, which contains data from 36 level I and II trauma centers in Michigan and Minnesota, was queried to identify patients who sustained TBI on the basis of head/neck Abbreviated Injury Scale (AIS) codes during the periods of March 13 through July 2 of 2017-2019 (pre-COVID-19 period) and March 13, 2020, through July 2, 2020 (COVID-19 period). Analyses were performed to detect differences in incidence, patient characteristics, injury severity, and outcomes.

RESULTS

There was an 18% decrease in the rate of encounters with TBI in the first 8 weeks (March 13 through May 7), followed by a 16% increase during the last 8 weeks (May 8 through July 2), of our COVID-19 period compared with the pre-COVID-19 period. Cumulatively, there was no difference in the rates of encounters with TBI between the COVID-19 and pre-COVID-19 periods. Severity of TBI, as measured with maximum AIS score for the head/neck region and Glasgow Coma Scale score, was also similar between periods. During the COVID-19 period, a greater proportion of patients with TBI presented more than a day after sustaining their injuries (p = 0.046). COVID-19 was also associated with a doubling in the decubitus ulcer rate from 1.0% to 2.1% (p = 0.002) and change in the distribution of discharge status (p = 0.01). Multivariable analysis showed no differences in odds of death/hospice discharge, intensive care unit stay of at least a day, or need for a ventilator for at least a day between the COVID-19 and pre-COVID-19 periods.

CONCLUSIONS

During the early months of the COVID-19 pandemic, the number of patients who presented with TBI was initially lower than in the years 2017-2019 prior to the pandemic. However, there was a subsequent increase in the rate of encounters with TBI, resulting in overall similar rates of TBI between March 13 through July 2 during the COVID-19 period and during the pre-COVID-19 period. The COVID-19 cohort was also associated with negative impacts on time to presentation, rate of decubitus ulcers, and discharge with supervision. Policies in response to future pandemics must consider the resources necessary to care for patients with TBI.

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

BACKGROUND

The Trauma and Injury Severity Score (TRISS) uses anatomical and physiologic variables to predict mortality. Elderly (65 years or older) trauma patients have increased mortality and morbidity for a given TRISS, in part because of functional status and comorbidities. These factors are incorporated into the American Society of Anesthesiologists Physical Status (ASA-PS) and National Surgical Quality Improvement Program Surgical Risk Calculator (NSQIP-SRC). We hypothesized scoring tools using comorbidities and functional status to be superior at predicting mortality, hospital length of stay (LOS), and complications in elderly trauma patients undergoing operation.

METHODS

Four level I trauma centers prospectively collected data on elderly trauma patients undergoing surgery within 24 hours of admission. Using logistic regression, five scoring models were compared: ASA-PS, NSQIP-SRC, TRISS, TRISS-ASA-PS, and TRISS-NSQIP-SRC.Brier scores and area under the receiver operator characteristics curve were calculated to compare mortality prediction. Adjusted R2 and root mean squared error were used to compare LOS and predictive ability for number of complications.

RESULTS

From 122 subjects, 9 (7.4%) died, and the average LOS was 12.9 days (range, 1-110 days). National Surgical Quality Improvement Program Surgical Risk Calculator was superior to ASA-PS and TRISS at predicting mortality (area under the receiver operator characteristics curve, 0.978 vs. 0.768 vs. 0.903; p = 0.007). Furthermore, NSQIP-SRC was more accurate predicting LOS (R2, 25.9% vs. 13.3% vs. 20.5%) and complications (R2, 34.0% vs. 22.6% vs. 29.4%) compared with TRISS and ASA-PS. Adding TRISS to NSQIP-SRC improved predictive ability compared with NSQIP-SRC alone for complications (R2, 35.5% vs. 34.0%; p = 0.046). However, adding ASA-PS or TRISS to NSQIP-SRC did not improve the predictive ability for mortality or LOS.

CONCLUSION

The NSQIP-SRC, which includes comorbidities and functional status, had superior ability to predict mortality, LOS, and complications compared with TRISS alone in elderly trauma patients undergoing surgery.

LEVEL OF EVIDENCE

Prognostic and Epidemiologic; Level III.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Traumatic brain injury in the elderly with high Glasgow coma scale and low injury severity scores: Factors influencing outcomes

BACKGROUND

Current trauma activation guidelines do not clearly address age as a risk factor when leveling trauma patients. Glasgow coma scale (GCS) and mode of injury play a major role in leveling trauma patients. We studied the above relationship in our elderly patients presenting with traumatic head injury.

METHODS

This study was a retrospective analysis of patients who presented to the emergency department with traumatic brain injuries. We classified the 270 patients into two groups. Group A was 64 years and younger, and group B was 65 years and older. Their GCS, ISS, age, sex, comorbidities, and anticoagulant use were abstracted. The primary outcome was mortality and length of stay. The groups were compared using an independent student's t-test and Chi-square analysis. The Cox regression analysis was used to analyze differences in the outcome while adjusting for the above factors.

RESULTS

There were 140 patients in group A, and 130 patients in group B who presented to the ED with a GCS of 14-15 and an ISS of below 15. The mean ISS significantly differed between group A (6.2 ± 6.8) vs (7.9 ± 3.2) in group B (p < 0.0001). The most common diagnosis in group A was concussion (57.3%), while in group B was subdural and subarachnoid hemorrhage (55%). In group B, 13.8% presented as a level one or level two trauma activation. The mean hospital and intensive care stay for group A was 2.1 (±1.9) days and 0.9 (±1.32) days, respectively, versus 4.2 (±3.04) days and 2.4 (±2.02 days) for the elderly group B. Mortality in group A was zero and in group B was 3.8%. Cox regression analysis showed age as an independent predictor of death as well as length of stay.

CONCLUSION

Elderly traumatic brain injury patients presenting to the ED with minor trauma and high GCS should be triaged at a higher level in most cases.

Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm

BACKGROUND

The ability to reliably predict outcomes after trauma in older adults (age ≥ 65 y) is critical for clinical decision making. Using novel machine-learning techniques, we sought to design a nonlinear, competing risks paradigm for prediction of older adult discharge disposition following injury.

MATERIALS AND METHODS

The National Trauma Databank (NTDB) was used to identify patients 65+ y between 2007 and 2014. Training was performed on an enriched cohort of diverse patients. Factors included age, comorbidities, length of stay, and physiologic parameters to predict in-hospital mortality and discharge disposition (home versus skilled nursing/long-term care facility). Length of stay and discharge status were analyzed via competing risks survival analysis with Bayesian additive regression trees and a multinomial mixed model.

RESULTS

The resulting sample size was 47,037 patients. Admission GCS and age were important in predicting mortality and discharge disposition. As GCS decreased, patients were more likely to die (risk ratio increased by average of 1.4 per 2-point drop in GCS, P < 0.001). As GCS decreased, patients were also more likely to be discharged to a skilled nursing or long-term care facility (risk ratio decreased by 0.08 per 2-point decrease in GCS, P< 0.001). The area under curve for prediction of discharge home was improved in the competing risks model 0.73 versus 0.43 in the traditional multinomial mixed model.

CONCLUSIONS

Predicting older adult discharge disposition after trauma is improved using machine learning over traditional regression analysis. We confirmed that a nonlinear, competing risks paradigm enhances prediction on any given hospital day post injury.

Trauma outcome predictor: An artificial intelligence interactive smartphone tool to predict outcomes in trauma patients

BACKGROUND

Classic risk assessment tools often treat patients' risk factors as linear and additive. Clinical reality suggests that the presence of certain risk factors can alter the impact of other factors; in other words, risk modeling is not linear. We aimed to use artificial intelligence (AI) technology to design and validate a nonlinear risk calculator for trauma patients.

METHODS

A novel, interpretable AI technology called Optimal Classification Trees (OCTs) was used in an 80:20 derivation/validation split of the 2010 to 2016 American College of Surgeons Trauma Quality Improvement Program database. Demographics, emergency department vital signs, comorbidities, and injury characteristics (e.g., severity, mechanism) of all blunt and penetrating trauma patients 18 years or older were used to develop, train then validate OCT algorithms to predict in-hospital mortality and complications (e.g., acute kidney injury, acute respiratory distress syndrome, deep vein thrombosis, pulmonary embolism, sepsis). A smartphone application was created as the algorithm's interactive and user-friendly interface. Performance was measured using the c-statistic methodology.

RESULTS

A total of 934,053 patients were included (747,249 derivation; 186,804 validation). The median age was 51 years, 37% were women, 90.5% had blunt trauma, and the median Injury Severity Score was 11. Comprehensive OCT algorithms were developed for blunt and penetrating trauma, and the interactive smartphone application, Trauma Outcome Predictor (TOP) was created, where the answer to one question unfolds the subsequent one. Trauma Outcome Predictor accurately predicted mortality in penetrating injury (c-statistics: 0.95 derivation, 0.94 validation) and blunt injury (c-statistics: 0.89 derivation, 0.88 validation). The validation c-statistics for predicting complications ranged between 0.69 and 0.84.

CONCLUSION

We suggest TOP as an AI-based, interpretable, accurate, and nonlinear risk calculator for predicting outcome in trauma patients. Trauma Outcome Predictor can prove useful for bedside counseling of critically injured trauma patients and their families, and for benchmarking the quality of trauma care.

Increased morbidity and mortality in elderly patients with lower extremity trauma and associated injuries: A review of 420,066 patients from the national trauma database

INTRODUCTION

There is a paucity of research addressing the morbidity and mortality associated with polytrauma in elderly patients. This study aimed to compare the outcomes of elderly trauma patients with an isolated lower extremity fracture, to patients lower extremity fractures and associated musculoskeletal injuries.

METHODS

This study is a retrospective review from the National Trauma Database (NTDB) between 2008 and 2014. ICD 9 codes were used to identify patients 65 years and older with lower extremity fractures. Patients were categorize patients into three sub groups: patients with isolated lower extremity fractures (ILE), patients with two or more (multiple) lower extremity fractures (MLE) and, patients with at least one upper and at least one lower extremity fracture (ULE). Groups were stratified into patients age 65-80 and patients >80 years of age.

RESULTS

A total 420,066 patients were included in analysis with 356,120 ILE fracture patients, 27,958 MLE fracture patients, and 35,988 ULE fracture patients. The MLE group reported the highest dispatch to ACS level 1 trauma centers at 31.8% followed by the ULE group at 28.5% and the ILE group at 24.7% of patients (p<0.001). The overall rate of complications was highest in the MLE group followed by the ULE and then the ILE group (41.4%, 40.3%, 36.1%, respectively p<0.001). Motility rates in patients >80 years old in the MLE group and ULE group were similar (1.483 vs 1.4432). However, in the 65-80 year group the odds of mortality was 1.260 in the MLE group and 1.450 in the ULE group (p<0.001), such that the odds of mortality after sustaining a MLE fracture increases with age, whereas this effect was not seen in the ULE group.

CONCLUSION

Patients who sustained MLE and ULE fractures, had increased mortality, complications and in hospital care requirements as compared to patients with isolated lower extremity injuries. These outcomes are comparable between ULE and MLE fracture patients over the age of 80 however patients 65-80 with ULE fractures had increased mortality as compared patients 65-80 with MLE fractures. Understanding the unique considerations and requirements of elderly trauma patients is vital to providing successful outcomes.

The GERtality Score: The Development of a Simple Tool to Help Predict in-Hospital Mortality in Geriatric Trauma Patients

Feasible and predictive scoring systems for severely injured geriatric patients are lacking. Therefore, the aim of this study was to develop a scoring system for the prediction of in-hospital mortality in severely injured geriatric trauma patients. The TraumaRegister DGU^® (TR-DGU) was utilized. European geriatric patients (≥65 years) admitted between 2008 and 2017 were included. Relevant patient variables were implemented in the GERtality score. By conducting a receiver operating characteristic (ROC) analysis, a comparison with the Geriatric Trauma Outcome Score (GTOS) and the Revised Injury Severity Classification II (RISC-II) Score was performed. A total of 58,055 geriatric trauma patients (mean age: 77 years) were included. Univariable analysis led to the following variables: age ≥ 80 years, need for packed red blood cells (PRBC) transfusion prior to intensive care unit (ICU), American Society of Anesthesiologists (ASA) score ≥ 3, Glasgow Coma Scale (GCS) ≤ 13, Abbreviated Injury Scale (AIS) in any body region ≥ 4. The maximum GERtality score was 5 points. A mortality rate of 72.4% was calculated in patients with the maximum GERtality score. Mortality rates of 65.1 and 47.5% were encountered in patients with GERtality scores of 4 and 3 points, respectively. The area under the curve (AUC) of the novel GERtality score was 0.803 (GTOS: 0.784; RISC-II: 0.879). The novel GERtality score is a simple and feasible score that enables an adequate prediction of the probability of mortality in polytraumatized geriatric patients by using only five specific parameters.

Validation of the geriatric trauma outcome scores in predicting outcomes of elderly trauma patients

BACKGROUND

Using three patient characteristics, including age, Injury Severity Score (ISS) and transfusion within 24 h of admission (yes vs. no), the Geriatric Trauma Outcome Score (GTOS) and Geriatric Trauma Outcome Score II (GTOS II) have been developed to predict mortality and unfavourable discharge (to a nursing home or hospice facility), of those who were ≥65 years old, respectively.

OBJECTIVES

This study aimed to validate the GTOS and GTOS II models. For the nested-cohort requiring intensive care, we compared the GTOS scores with two ICU prognostic scores - the Acute Physiology and Chronic Health Evaluation (APACHE) III and Australian and New Zealand Risk of Death (ANZROD).

METHODS

All elderly trauma patients admitted to the State Trauma Unit between 2009 and 2019 were included. The discrimination ability and calibration of the GTOS and GTOS II scores were assessed by the area under the receiver-operating-characteristic (AUROC) curve and a calibration plot, respectively.

RESULTS

Of the 57,473 trauma admissions during the study period, 15,034 (26.2%) were ≥65 years-old. The median age and ISS of the cohort were 80 (interquartile range [IQR] 72-87) and 6 (IQR 2-9), respectively; and the average observed mortality was 4.3%. The ability of the GTOS to predict mortality was good (AUROC 0.838, 95% confidence interval [CI] 0.821-0.855), and better than either age (AUROC 0.603, 95%CI 0.581-0.624) or ISS (AUROC 0.799, 95%CI 0.779-0.819) alone. The GTOS II's ability to predict unfavourable discharge was satisfactory (AUROC 0.707, 95%CI 0.696-0.719) but no better than age alone. Both GTOS and GTOS II scores over-estimated risks of the adverse outcome when the predicted risks were high. The GTOS score (AUROC 0.683, 95%CI 0.591-0.775) was also inferior to the APACHE III (AUROC 0.783, 95%CI 0.699-0.867) or ANZROD (AUROC 0.788, 95%CI 0.705-0.870) in predicting mortality for those requiring intensive care.

CONCLUSIONS

The GTOS scores had a good ability to discriminate between survivors and non-survivors in the elderly trauma patients, but GTOS II scores were no better than age alone in predicting unfavourable discharge. Both GTOS and GTOS II scores were not well-calibrated when the predicted risks of adverse outcome were high.

The Effect of Organ System Surgery on Intensive Care Unit Mortality in a Cohort of Critically Ill Surgical Patients

BACKGROUND

The critical illness burden in the United States is growing with an aging population obtaining surgical intervention despite age-related comorbidities. The effect of organ system surgical intervention on intensive care units (ICUs) mortality is unknown.

METHODS

We performed an 8-year retrospective analysis of surgical ICU patients. Poisson regression analysis was performed assessing the relative risk of in-hospital mortality based on surgical intervention.

RESULTS

Of 468 000 ICU patients included, 97 968 (20.9%) were surgical admissions and 97 859 (99.9%) had complete outcomes data. Nonsurvivors were older (68.8 ± 15.4 vs. 62.7 ± 15.8 years, P < .001) with higher Acute Physiology, Age, Chronic Health Evaluation (APACHE) III Scores (81.4 ± 33.6 vs. 46.7 ± 20.1, P < .001. Patients with gastrointestinal (GI) (n = 1,558, 7.8%), musculoskeletal (n = 277, 5.5%), and neurological (n = 884, 4.6%) system operations had the highest mortality. Upon Poisson regression model, patients undergoing emergent operative interventions on the neurologic system (RR 1.86, 95% CI 1.67-2.07, P < .001) had increased relative risk of mortality when compared to emergent operative interventions on the cardiovascular system after controlling for pertinent covariates. Elective operative interventions on the respiratory (RR 2.39, 95% CI 2.03-2.80, P < .001), GI (RR 2.34, 95% CI 2.10-2.61, P < .001), and skin and soft tissue (RR 2.26, 95% CI 1.77-2.89, P < .001) systems had increased risk of mortality when compared to elective cardiovascular system surgery after controlling for pertinent covariates.

CONCLUSION

We found significant differences in the risk of mortality based on organ system of operative intervention. The prognostication of critically ill patients undergoing surgical intervention is currently not accounted for in prognostic scoring systems.

Type-2 muscle fiber atrophy is associated with sarcopenia in elderly men with hip fracture

Sarcopenia is a common geriatric syndrome and can lead to falls and fragility fractures. It is associated with a decline of muscle fiber numbers and size. Muscle biopsies of the vastus lateralis muscle were taken from thirty-two patients with hip fracture (18 women and 14 men; mean age: 82.2 ± 6.2 years). Serial cross sections of skeletal muscle were labeled with myosin heavy chain slow (fiber type-1) and fast (fiber type-2) antibodies in order to measure the size, ratio and percentage of mixed fiber types. The presence of sarcopenia was defined according to the EWGSOP2 criteria by using BIA and handgrip strength measurement. Sarcopenia was identified in 5 patients (3 women and 2 men), probable-sarcopenia in 11 patients (4 women and 7 men). Significant differences in fiber diameter were found for fiber type-2 in men but not in women. Only 1-3% mixed fiber types were found in sarcopenic patients, indicating a final stage where reinnervation is not possible to occur anymore. Muscle fiber type-2 atrophy seems to be a histological marker for sarcopenia in men.

The role of palliative care in acute trauma: When is it appropriate?

INTRODUCTION

We hypothesized that trauma providers are reticent to consider palliative measures in acute trauma care.

METHODS

An electronic survey based on four patient scenarios with identical vital signs and serious blunt injuries, but differing ages and frailty scores was sent to WTA and EAST members.

RESULTS

509 (24%) providers completed the survey. Providers supported early transition to comfort care in 85% old-frail, 53% old-fit, 77% young-frail, and 30% young-fit patients. Providers were more likely to transition frail vs. fit patients with (OR = 4.8 [3.8-6.3], p < 0.001) or without (OR = 16.7 [12.5-25.0], p < 0.001) an advanced directive (AD) and more likely to transition old vs. young patients with (OR = 2.0 [1.6-2.6], p < 0.001) or without (OR = 4.2 [2.8-5.0], p < 0.001) an AD.

CONCLUSIONS

In specific clinical situations, there was wide acceptance among trauma providers for the early institution of palliative measures. Provider decision-making was primarily based on patient frailty and age. ADs were helpful for fit or young patients. Provider demographics did not impact decision-making.