A comparison of prognosis calculators for geriatric trauma: A Prognostic Assessment of Life and Limitations After Trauma in the Elderly consortium study

Enhancing geriatric trauma mortality prediction: Modifying and assessing the Geriatric Trauma Outcome Score with net benefit and decision curve analysis

OBJECTIVE

Calibration and discrimination indicators alone are insufficient for evaluating the clinical usefulness of prediction models, as they do not account for the cost of misclassification errors. This study aimed to modify the Geriatric Trauma Outcome Score (GTOS) and assess the clinical utility of the modified model using net benefit (NB) and decision curve analysis (DCA) for predicting in-hospital mortality.

METHODS

The Trauma Quality Improvement Program (TQIP) 2017 was used to identify geriatric trauma patients (≥ 65 years) treated at Level I trauma centers. The outcome of interest was in-hospital mortality. The GTOS was modified to include additional patient, injury, and treatment characteristics identified through machine learning methods, focusing on early risk stratification. Calibration and discrimination indicators, along with NB and DCA, were utilized for evaluation.

RESULTS

Of the 67,222 admitted geriatric trauma patients, 5.6% died in the hospital. The modified GTOS score included the following variables with associated weights: initial airway intervention (5), Glasgow Coma Scale ≤13 (5), packed red blood cell transfusion within 24 h (3), penetrating injury (2), age ≥ 75 years (2), preexisting comorbidity (1), and torso injury (1), with a total range from 0 to 19. The modified GTOS demonstrated a significantly higher area under the curve (0.92 vs. 0.84, p < 0.0001), lower misclassification error (4.9% vs. 5.2%), and lower Brier score (0.036 vs. 0.042) compared to the original GTOS. DCA showed that using the modified GTOS for predicting in-hospital mortality resulted in higher NB than treating all, treating none, and treating based on the original GTOS across a wide range of clinician preferences.

CONCLUSIONS

The modified GTOS model exhibited superior predictive ability and clinical utility compared to the original GTOS. NB and DCA offer valuable complementary methods to calibration and discrimination indicators, comprehensively evaluating the clinical usefulness of prediction models and decision strategies.

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.

Trauma Centre admission criteria for elderly patients

<b>Introduction:</b> Injuries are among the three most common causes of sudden death in Poland, and patients particularly at risk of fatal outcomes of trauma are elderly. Geriatric age is associated with pathological changes that determine a worse response to trauma. In order to improve treatment outcomes of elderly trauma patients, it is essential for them to have access to specialized healthcare units i.e. Trauma Centers (TC). In Poland, admission criteria for TC are determined in the Regulation of the Ministry of Health published in 2010. Those criteria do not include age. According to recent research, such admission criteria lead to undertriage i.e., underestimation of injuries of elderly trauma patients and referred to a healthcare unit of lower reference level.<b>Aim:</b> Analyze the current national admission criteria of elderly trauma patients admitted to TCs.<b>Materials and methods:</b> TC admission criteria were subject to analysis in referral to available scientific publications in the field of medical segregation of elderly trauma patients, available in PubMed, Medline-EBSCO.<b>Results:</b> TC admission criteria in current form are fulfilled only by elderly patients with minimal survival chance. As a result, majority of elderly trauma patients are referred to healthcare units of lower reference level. Those patients are deprived of professional trauma care in TC. Such discrepancies in medical segregation often stem from lack of anatomical changes or shifts in physiological parameters typically observed in trauma patients.<b>Conclusions:</b> It is essential to develop national research to find the optimal system of triage for elderly trauma patient and an adequate tool for appropriate admitted them to TC.

Geriatric Trauma Outcome Score as a Mortality Predictor in Isolated Moderate to Severe Traumatic Brain Injury: A Single-Center Retrospective Study

BACKGROUND

Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide, with severe cases significantly increasing the risk of complications and long-term mortality. The Geriatric Trauma Outcome Score (GTOS), based on age, injury severity, and transfusion need, has been validated for predicting mortality in older trauma patients, but its utility in predicting mortality for TBI patients remains unexplored.

METHODS

This retrospective study included 5543 adult trauma patients with isolated moderate to severe TBI, defined by head Abbreviated Injury Scale (AIS) scores of ≥ 3, from 1998 to 2021. GTOS was calculated with the following formula: age + (Injury Severity Score × 2.5) + 22 (if transfused within 24 h). The area under the receiver operating characteristic curve (AUROC) assessed GTOS's ability to predict mortality. The optimal GTOS cutoff value was determined using Youden's index. Mortality rates were compared between high- and low-GTOS groups, separated by the optimal GTOS cutoff value, including a propensity score-matched analysis adjusting for baseline characteristics.

RESULTS

Among 5543 patients, mortality was 8.3% (462 deaths). Higher mortality is correlated with male sex, older age, higher GTOS, and comorbidities like hypertension, coronary artery disease, and end-stage renal disease. The optimal GTOS cut-off for mortality prediction was 121.5 (AUC = 0.813). Even when the study population was matched by propensity score, patients with GTOS ≥121.5 had much higher odds of death (odds ratio 2.64, 95% confidence interval 1.93-3.61, p < 0.001) and longer hospital stays (mean 16.7 vs. 12.2 days, p < 0.001) than those with GTOS < 121.5.

CONCLUSIONS

These findings support the idea that GTOS is a useful tool for risk stratification of in-hospital mortality in isolated moderate to severe TBI patients. However, we encourage further research to refine GTOS for better applicability in TBI patients.

Extremity fractures, attempted suicide, blood transfusion and thromboembolic events are independent risk factors for a prolonged hospital stay in severely injured elderly

METHODS

Due to demographic change, the number of polytraumatized geriatric patients (> 64 years) is expected to further increase in the coming years. In addition to the particularities of the accident and the associated injury patterns, prolonged inpatient stays are regularly observed in this group. The aim of the evaluation is to identify further factors that cause prolonged inpatient stays. A study of the data from the TraumaRegister DGU® from 2016-2020 was performed. Inclusion criteria were an age of over 64 years, intensive care treatment in the GAS-region, and an Injury Severity Score (ISS) of at least 16 points. All patients who were above the 80th percentile for the average length of stay or average intensive care stay of the study population were defined as so-called long-stay patients. This resulted in a prolonged inpatient stay of > 25 days and an intensive care stay of > 13 days. Among other, the influence of the cause of the accident, injury patterns according to body regions, the occurrence of complications, and the influence of numerous clinical parameters were examined.

RESULTS

A total of 23,026 patients with a mean age of 76.6 years and a mean ISS of 24 points were included. Mean ICU length of stay was 11 ± 12.9 days (regular length of stay: 3.9 ± 3.1d vs. prolonged length of stay: 12.8 ± 5.7d) and mean inpatient stay was 22.5 ± 18.9 days (regular length of stay: 20.7 ± 15d vs. 35.7 ± 22.3d). A total of n = 6,447 patients met the criteria for a prolonged length of stay. Among these, patients had one more diagnosis on average (4.6 vs. 5.8 diagnoses) and had a higher ISS (21.8 ± 6 pts. vs. 26.9 ± 9.5 pts.) Independent risk factors for prolonged length of stay were intubation duration greater than 6 days (30-fold increased risk), occurrence of sepsis (4x), attempted suicide (3x), presence of extremity injury (2.3x), occurrence of a thromboembolic event (2.7x), and administration of red blood cell concentrates in the resuscitation room (1.9x).

CONCLUSIONS

The present analysis identified numerous independent risk factors for significantly prolonged hospitalization of the geriatric polytraumatized patient, which should be given increased attention during treatment. In particular, the need for a smooth transition to psychiatric follow-up treatment or patient-adapted rehabilitative care for geriatric patients with prolonged immobility after extremity injuries is emphasized by these results.

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.

Predictors of adverse outcomes in elders hospitalised for isolated orthopaedic trauma: a multicentre cohort study

BACKGROUND

Patients >64 years of age now represent more than 51% of injury hospitalisations in Canada. The tools used to identify older patients who could benefit the most from an interdisciplinary approach include complex parameters difficult to collect in the ED, which suggests that better tools with higher accuracy and using items that can be derived from routinely collected data are needed. We aimed to identify variables that are associated with adverse outcomes in older patients admitted to a trauma centre for an isolated orthopaedic injury.

METHODS

We conducted a multicentre retrospective cohort study between 1 April 2013 and 31 March 2019 on older patients hospitalised with a primary diagnosis of isolated orthopaedic injury (n=19 928). Data were extracted from the provincial trauma registry (Registre des traumatismes du Québec). We used multilevel logistic regression to estimate the associations between potential predictors and adverse outcomes (extended length of stay, mortality, complications, unplanned readmission and adverse discharge destination).

RESULTS

Increasing age, male sex, specific comorbidities, type of orthopaedic injuries, increasing number of comorbidities, severe orthopaedic injury, head injuries and admission in the year before the injury were all significant predictors of adverse outcomes.

CONCLUSION

We identified eight predictors of adverse outcomes in patients >64 years of age admitted to a trauma centre for orthopaedic injury. These variables could eventually be used to develop a clinical decision rule to identify elders who may benefit the most from interdisciplinary care.

Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling

Introduction

Age-specific risk factors may delay posttraumatic functional recovery; complex interactions exist between these factors. In this study, we investigated the prediction ability of machine learning models for posttraumatic (6 months) functional recovery in middle-aged and older patients on the basis of their preexisting health conditions.

Methods

Data obtained from injured patients aged ≥45 years were divided into training-validation (n = 368) and test (n = 159) data sets. The input features were the sociodemographic characteristics and baseline health conditions of the patients. The output feature was functional status 6 months after injury; this was assessed using the Barthel Index (BI). On the basis of their BI scores, the patients were categorized into functionally independent (BI >60) and functionally dependent (BI ≤60) groups. The permutation feature importance method was used for feature selection. Six algorithms were validated through cross-validation with hyperparameter optimization. The algorithms exhibiting satisfactory performance were subjected to bagging to construct stacking, voting, and dynamic ensemble selection models. The best model was evaluated on the test data set. Partial dependence (PD) and individual conditional expectation (ICE) plots were created.

Results

In total, nineteen of twenty-seven features were selected. Logistic regression, linear discrimination analysis, and Gaussian Naive Bayes algorithms exhibited satisfactory performances and were, therefore, used to construct ensemble models. The k-Nearest Oracle Elimination model outperformed the other models when evaluated on the training-validation data set (sensitivity: 0.732, 95% CI: 0.702-0.761; specificity: 0.813, 95% CI: 0.805-0.822); it exhibited compatible performance on the test data set (sensitivity: 0.779, 95% CI: 0.559-0.950; specificity: 0.859, 95% CI: 0.799-0.912). The PD and ICE plots showed consistent patterns with practical tendencies.

Conclusion

Preexisting health conditions can predict long-term functional outcomes in injured middle-aged and older patients, thus predicting prognosis and facilitating clinical decision-making.

The value of compassion: Healthcare savings of palliative care consults in trauma

BACKGROUND

The effects of palliative care (PC) consultation on patient costs and hospitalization metrics in the adult trauma population are unclear.

STUDY DESIGN

We interrogated our Level I trauma center databases from 1/1/19 to 3/31/21 for patients age ≥18 admitted to the trauma service. Patients undergoing PC consult were matched using propensity scoring to those without PC consultation based on age, admission Glasgow Coma Scale score, Injury Severity Score and Head Abbreviated Injury Scale. Total costs, total cost per day, hospital length of stay (LOS), ICU LOS, intubation days, discharge disposition, and rates of nephrology consultation and tracheostomy/feeding tube placements were compared.

RESULTS

140 unique patients underwent PC consultation and were matched to a group not receiving PC consult during the same period. Median total costs in the PC cohort were $39,532 compared to $70,330 in the controls (p<0.01).  Median costs per day in the PC cohort were $3,495 vs $17,970 in the controls (p<0.01).  Median costs per ICU day in the PC cohort were $3,774 vs $17,127 in the controls (p<0.01).  Mean hospital LOS (15.7 vs 7 days), ICU LOS (7.9 vs 2.9 days), and ventilator days (5.1 vs 1.5) were significantly higher in the PC cohort (all p<0.01).  Rates of nephrology consultation (8.6 vs 2.1%, p = 0.03) and tracheostomy/feeding tube placements (12.1 vs 1.4%, p<0.01) were also higher in the PC group.  Patients were more likely to discharge to hospice if they received a PC consult (33.6 vs 2.1%, p<0.01).  Mean time to PC consult was 7.2 days (range 1 hour to 45 days). LOS post-consult correlated positively with time to PC consultation (r = 0.27, p<0.01).

CONCLUSION

Expert PC services are known to alleviate suffering and avert patient goal- and value-incongruent care. While trauma patients demand significant resources, PC consultation offered in concordance with life-sustaining interventions is associated with significant savings to patients and the healthcare system. Given the correlation between LOS following PC consult and time to PC consult, savings may be amplified by earlier PC consultation in appropriate patients.

Age itself or age-associated comorbidities? A nationwide analysis of outcomes of geriatric trauma

PURPOSE

Geriatric trauma patients present physiological challenges to care providers. A nationwide analysis was performed to evaluate the roles of age alone versus age-associated comorbidities in the morbidity and mortality of elderly patients with blunt abdominal trauma (BAT).

METHODS

Patients with BAT registered in the National Trauma Data Bank from 2013 to 2015 were analyzed using propensity score matching (PSM) to evaluate the mortality rate, complication rate, hospital length of stay (LOS), intensive care unit (ICU) LOS and ventilator days between young (age < 65) and elderly (age ≥ 65) patients. An adjusted multivariate logistic regression (MLR) model was also used to evaluate the effect of age itself and age-associated comorbidities on mortality.

RESULTS

There were 41,880 patients with BAT during the study period. In elderly patients, the injury severity score (ISS) decreased with age, but the mortality rate increased inversely (from 5.0 to 13.5%). Under a similar condition and proportion of age-associated comorbidities after a well-batched PSM analysis, elderly patients had significantly higher mortality rates (8.0% vs. 1.9%, p < 0.001), higher complication rates (35.1% vs. 30.6%, p < 0.001), longer hospital LOS (8.9 vs. 8.1 days, p < 0.001), longer ICU LOS (3.7 vs. 2.7 days, p < 0.001) and more ventilator days (1.1 vs. 0.5 days, p < 0.001) than young patients. Furthermore, the MLR analysis showed that age itself served as an independent factor for mortality (odds ratio: 1.049, 95% CI 1.043-1.055, p < 0.001), but age-associated comorbidity was not.

CONCLUSION

In patients with BAT, age itself appeared to have an independent and deleterious effect on mortality, but age-associated comorbidity did not.

Predicting mortality in elderly trauma patients: a review of the current literature

PURPOSE OF REVIEW

Advances in medical care allow patients to live longer, translating into a larger geriatric patient population. Adverse outcomes increase with older age, regardless of injury severity. Age, comorbidities, and physiologic deterioration have been associated with the increased mortality seen in geriatric trauma patients. As such, outcome prediction models are critical to guide clinical decision making and goals of care discussions for this population. The purpose of this review was to evaluate the various outcome prediction models for geriatric trauma patients.

RECENT FINDINGS

There are several prediction models used for predicting mortality in elderly trauma patients. The Geriatric Trauma Outcome Score (GTOS) is a validated and accurate predictor of mortality in geriatric trauma patients and performs equally if not better to traditional scores such as the Trauma and Injury Severity Score. However, studies recommend medical comorbidities be included in outcome prediction models for geriatric patients to further improve performance.

SUMMARY

The ideal outcome prediction model for geriatric trauma patients has not been identified. The GTOS demonstrates accurate predictive ability in elderly trauma patients. The addition of medical comorbidities as a variable in outcome prediction tools may result in superior performance; however, additional research is warranted.

Comprehensive comparison between geriatric and nongeriatric patients with trauma

The incidence of geriatric trauma is increasing due to the growing elderly population. Healthcare providers require a global perspective to differentiate critical factors that might alter patients' prognosis.We retrospectively reviewed all adult patients admitted to a trauma center during a 4-year period. We identified 655 adult trauma patients aged from 18 to 64 (nongeriatric group) and 273 trauma patients ≥65 years (geriatric group). Clinical data were collected and compared between the 2 groups.The geriatric group had a higher incidence of trauma and higher Injury Severity Scores than did the nongeriatric group. Fewer geriatric patients underwent surgical treatment (all patients: geriatric vs nongeriatric: 65.9% vs 70.7%; patients with severe trauma: geriatric vs nongeriatric: 27.6% vs 44.5%). Regarding prognosis, the geriatric group exhibited higher mortality rate and less need for long-term care (geriatric vs nongeriatric: mortality: 5.5% vs 1.8%; long-term care: 2.2% vs 5.0%).We observed that geriatric patients had higher trauma incidence and higher trauma mortality rate. Aging is a definite predictor of poor outcomes for trauma patients. Limited physiological reserves and preference for less aggressive treatment might be the main reasons for poor outcomes in elderly individuals.

Factors related to early and rapid assessment of in-hospital mortality among older adult trauma patients in an earthquake

BACKGROUND

There is limited evidence for emergency physicians and emergency trauma surgeons regarding the determinants of early and rapid assessment of older adult in-hospital mortality due to earthquakes. This study explored factors related to the early and rapid assessment of the mortality among older adult earthquake trauma patients (OAETPs) and created a screening model.

METHODS

Data on 7,308 OAETPs from the West China Earthquake Patients Database were analyzed retrospectively. The 35 variables that can be obtained rapidly on arrival at the hospital were collected. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed. Then, the nomogram for assessing the mortality of OAETPs was constructed.

RESULTS

We identified 10 independent mortality-related factors that contributed to the in-hospital mortality of OAETPs. The 10 factors included age (odds ratio [OR]=1.061, 95% confidence interval [CI]:1.031-1.090), dementia (OR=5.146, 95%CI: 1.169-17.856), coronary heart disease (CHD; OR=23.441, 95%CI: 4.799-83.927), malignant tumor (OR=8.497, 95%CI: 3.583-17.967), deep vein thrombosis (DVT; OR=7.110, 95%CI: 1.369-27.168), chronic kidney disease(CKD; OR=11.783, 95%CI: 5.419-24.407), pulse rate (PR; OR=1.036, 95%CI: 1.022-1.048), mean artery pressure (MAP; OR=0.960, 95%CI: 0.945-0.975), Glasgow Coma Scale (GCS; OR=0.864, 95%CI: 0.760-0.972), and Triage Revised Trauma Score (T-RTS, OR=0.485, 95%CI: 0.351-0.696).

CONCLUSION

The 10 mortality-related factors could be quickly obtained on hospital arrival and should be the focal point of future earthquake response strategies regarding hospitalized older adults with trauma. A nomogram was constructed based on the factors for screening OAETPs with a higher risk of in-hospital mortality.

Validation of the artificial intelligence-based trauma outcomes predictor (TOP) in patients 65 years and older

Background

The Trauma Outcomes Predictor tool was recently derived using a machine learning methodology called optimal classification trees and validated for prediction of outcomes in trauma patients. The Trauma Outcomes Predictor is available as an interactive smartphone application. In this study, we sought to assess the performance of the Trauma Outcomes Predictor in the elderly trauma patient.

Methods

All patients aged 65 years and older in the American College of Surgeons–Trauma Quality Improvement Program 2017 database were included. The performance of the Trauma Outcomes Predictor in predicting in-hospital mortality and combined and specific morbidity based on incidence of 9 specific in-hospital complications was assessed using the c-statistic methodology, with planned subanalyses for patients 65 to 74, 75 to 84, and 85+ years.

Results

A total of 260,505 patients were included. Median age was 77 (71–84) years, 57% were women, and 98.8% had a blunt mechanism of injury. The Trauma Outcomes Predictor accurately predicted mortality in all patients, with excellent performance for penetrating trauma (c-statistic: 0.92) and good performance for blunt trauma (c-statistic: 0.83). Its best performance was in patients 65 to 74 years (c-statistic: blunt 0.86, penetrating 0.93). Among blunt trauma patients, the Trauma Outcomes Predictor had the best discrimination for predicting acute respiratory distress syndrome (c-statistic 0.75) and cardiac arrest requiring cardiopulmonary resuscitation (c-statistic 0.75). Among penetrating trauma patients, the Trauma Outcomes Predictor had the best discrimination for deep and organ space surgical site infections (c-statistics 0.95 and 0.84, respectively).

Conclusion

The Trauma Outcomes Predictor is a novel, interpretable, and highly accurate predictor of in-hospital mortality in the elderly trauma patient up to age 85 years. The Trauma Outcomes Predictor could prove useful for bedside counseling of elderly patients and their families and for benchmarking the quality of geriatric trauma care.

SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care

Background

Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer.

Methods and findings

All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance.

The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891–0.916]), c = 0.877 [95% CI:0.864–0.890]), and c = 0.869 [95% CI:0.857–0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4–5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751–0.921], 0.815 [95% CI: 0.730–0.900], and 0.794 [95% CI: 0.725–0.864], respectively).

Conclusions

The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.

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.

A statistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit

BACKGROUND

Trauma patients admitted to critical care are at high risk of mortality because of their injuries. Our aim was to develop a machine learning-based model to predict mortality using Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework. We hypothesized machine learning could be applied to critically ill patients and would outperform currently used mortality scores.

METHODS

The current Deep-FLAIM model evaluates the statistically significant risk factors and then supply these risk factors to deep neural network to predict mortality in trauma patients admitted to the intensive care unit (ICU). We analyzed adult patients (≥18 years) admitted to the trauma ICU in the publicly available database Medical Information Mart for Intensive Care III version 1.4. The first phase selection of risk factor was done using Cox-regression univariate and multivariate analyses. In the second phase, we applied deep neural network and other traditional machine learning models like Linear Discriminant Analysis, Gaussian Naïve Bayes, Decision Tree Model, and k-nearest neighbor models.

RESULTS

We identified a total of 3,041 trauma patients admitted to the trauma surgery ICU. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being serum anion gap (hazard ratio [HR], 2.46; 95% confidence interval [CI], 1.94-3.11), sodium (HR, 2.11; 95% CI, 1.61-2.77), and chloride (HR, 2.11; 95% CI, 1.69-2.64) abnormalities on laboratories, while clinical variables included the diagnosis of sepsis (HR, 2.03; 95% CI, 1.23-3.37), Quick Sequential Organ Failure Assessment score (HR, 1.52; 95% CI, 1.32-3.76). And Systemic Inflammatory Response Syndrome criteria (HR. 1.41; 95% CI, 1.24-1.26). After we used these clinically significant variables and applied various machine learning models to the data, we found out that our proposed DNN outperformed all the other methods with test set accuracy of 92.25%, sensitivity of 79.13%, and specificity of 94.16%; positive predictive value, 66.42%; negative predictive value, 96.87%; and area under the curve of the receiver-operator curve of 0.91 (1.45-1.29).

CONCLUSION

Our novel Deep-FLAIM model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.

LEVEL OF EVIDENCE

Prognostic study, level II.

The incidence of geriatric trauma is increasing and comparison of different scoring tools for the prediction of in-hospital mortality in geriatric trauma patients

Purpose

The study aimed to examine the changing incidence of geriatric trauma and evaluate the predictive ability of different scoring tools for in-hospital mortality in geriatric trauma patients.

Methods

Annual reports released by the National Trauma Database (NTDB) in the USA from 2005 to 2015 and the Trauma Register DGU® in Germany from 1994 to 2012 were analyzed to examine the changing incidence of geriatric trauma. Secondary analysis of a single-center cohort study conducted among 311 severely injured geriatric trauma patients in a level I trauma center in Switzerland was completed. According to the in-hospital survival status, patients were divided into the survival and non-survival group. The differences of the ISS (injury severity score), NISS (new injury severity score), TRISS (Trauma and Injury Severity Score), APACHE II (Acute Physiology and Chronic Health Evaluation II), and SPAS II (simplified acute physiology score II) between two groups were evaluated. Then, the areas under the receiver-operating characteristic curve (AUC-ROC) of different scoring tools for the prediction of in-hospital mortality in geriatric trauma patients were calculated.

Results

The analysis of the NTDB showed that the increase in the number of geriatric trauma ranged from 18 to 30% between 2005 and 2015. The analysis of the DGU® showed that the mean age of trauma patients rose from 39.11 in 1993 to 51.10 in 2013, and the proportion of patients aged ≥ 60 years rose from 16.5 to 37.5%. The findings from the secondary analysis showed that 164 (52.73%) patients died in the hospital. The ISS, NISS, APACHE II, and SAPS II in the death group were significantly higher than those in the survival group, and the TRISS in the death group was significantly lower than those in the survival group. The AUCs of the ISS, NISS, TRISS, APACHE II, and SAPS II for the prediction of in-hospital mortality in geriatric trauma patients were 0.807, 0.850, 0.828, 0.715, and 0.725, respectively.

Conclusion

The total number of geriatric trauma is increasing as the population ages. The accuracy of ISS, NISS and TRISS was higher than the APACHE II and SAPS II for the prediction of in-hospital mortality in geriatric trauma patients.

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.

Predictors of elderly mortality after trauma: A novel outcome score

INTRODUCTION

Elderly trauma patients are at high risk for mortality, even when presenting with minor injuries. Previous prognostic models are poorly used because of their reliance on elements unavailable during the index hospitalization. The purpose of this study was to develop a predictive algorithm to accurately estimate in-hospital mortality using easily available metrics.

METHODS

The National Trauma Databank was used to identify patients 65 years and older. Data were split into derivation (2007-2013) and validation (2014-2015) data sets. There was no overlap between data sets. Factors included age, comorbidities, physiologic parameters, and injury types. A two-tiered scoring system to predict in-hospital mortality was developed: a quick elderly mortality after trauma (qEMAT) score for use at initial patient presentation and a full EMAT (fEMAT) score for use after radiologic evaluation. The final model (stepwise forward selection, p < 0.05) was chosen based on calibration and discrimination analysis. Calibration (Brier score) and discrimination (area under the receiving operating characteristic curve [AuROC]) were evaluated. Because National Trauma Databank did not include blood product transfusion, an element of the Geriatric Trauma Outcome Score (GTOS), a regional trauma registry was used to compare qEMAT versus GTOS. A mobile-based application is currently available for cost-free utilization.

RESULTS

A total of 840,294 patients were included in the derivation data set and 427,358 patients in the validation data set. The fEMAT score (median, 91; S.D., 82-102) included 26 factors, and the qEMAT score included eight factors. The AuROC was 0.86 for fEMAT (Brier, 0.04) and 0.84 for qEMAT. The fEMAT outperformed other trauma mortality prediction models (e.g., Trauma and Injury Severity Score-Penetrating and Trauma and Injury Severity Score-Blunt, age + Injury Severity Score). The qEMAT outperformed the GTOS (AuROC, 0.87 vs. 0.83).

CONCLUSION

The qEMAT and fEMAT accurately estimate the probability of in-hospital mortality and can be easily calculated on admission. This information could aid in deciding transfer to tertiary referral center, patient/family counseling, and palliative care utilization.

LEVEL OF EVIDENCE

Epidemiological Study, level IV.

One-year mortality in geriatric trauma patients: Improving upon the geriatric trauma outcomes score utilizing the social security death index

BACKGROUND

Geriatric Trauma Outcomes Score (GTOS) predicts in-patient mortality in geriatric trauma patients and has been validated in a prospective multicenter trial and expanded to predict adverse discharge (GTOS II). We hypothesized that these formulations actually underestimate the downstream sequelae of injury and sought to predict longer-term mortality in geriatric trauma patients.

METHODS

The Parkland Memorial Hospital Trauma registry was queried for patients 65 years or older from 2001 to 2013. Patients were then matched to the Social Security Death Index. The primary outcome was 1-year mortality. The original GTOS formula (variables of age, Injury Severity Score [ISS], 24-hour transfusion) was tested to predict 1-year mortality using receiver operator curves. Significant variables on univariate analysis were used to build an optimal multivariate model to predict 1-year mortality (GTOS III).

RESULTS

There were 3,262 patients who met inclusion. Inpatient mortality was 10.0% (324) and increased each year: 15.8%, 1 year; 17.8%, 2 years; and 22.6%, 5 years. The original GTOS equation had an area under the curve of 0.742 for 1-year mortality. Univariate analysis showed that patients with 1-year mortality had on average increased age (75.7 years vs. 79.5 years), ISS (11.1 vs. 19.1), lower GCS score (14.3 vs. 10.5), more likely to require transfusion within 24 hours (11.5% vs. 31.3%), and adverse discharge (19.5% vs. 78.2%; p < 0.0001 for all). Multivariate logistic regression was used to create the optimal equation to predict 1-year mortality: (GTOSIII = age + [0.806 × ISS] + 5.55 [if transfusion in first 24 hours] + 21.69 [if low GCS] + 34.36 [if adverse discharge]); area under the curve of 0.878.

CONCLUSION

Traumatic injury in geriatric patients is associated with high mortality rates at 1 year to 5 years. GTOS III has robust test characteristics to predict death at 1 year and can be used to guide patient centered goals discussions with objective data.

LEVEL OF EVIDENCE

Prognostic, level III.

Comparing the Clinical Features and Trauma Scores of Trauma Patients Aged Under 65 Years with Those of Patients Aged over 65 Years in the Intensive Care Unit: A Retrospective Study for Last Ten Years

Objective

This retrospective study aimed to compare the clinical characteristics and trauma scores of Intensive Care Unit (ICU) trauma patients 65 years and older with the patients under 65 years old.

Materials and Methods

Trauma patients (n=161) who stayed at least 24 hours in ICU were included. Patients younger than 65 years were included into Group 1 (n=109) and patients aged ≥65 years (n=52) were included into Group 2. Patient characteristics and trauma index scores (GCS; APACHE II score, ISS; TRISS and RTS) at ICU admission were calculated.

Results

The patients in Group 2 had more comorbid disease compared with Group 1 (61.5%, 6.4%) (p=0.001). The Trauma-related Injury Severity Score score were higher in Group 1 (49.76±33.75) compared with Group 2 (35.38±34.93) (p=0.006). The APACHE II score were higher in Group 2 (20.08±7.60) compared with Group 1 (17.00±6.90) (p=0.007). The need for invasive mechanical ventilation and tracheostomy were more frequent in Group 2 trauma patients compared with those of patients in Group 1 (92.3%, 73.4%; p=0.003; 26.9%, 8.3%; p=0.002; respectively). The need for transfusion of packed red blood cell suspension (PRBC) was more frequent in Group 2 compared with Group 1 (92.3%, 55.0%; respectively) (p=0.001). The mortality rate was found to be higher in Group 2 compared with Group 1 (48.1%, 19.3%; respectively) (p=0.001).

Conclusion

The elderly trauma patients have more comorbid disease, higher scores for APACHE II and lower scores for TRISS, more mechanical ventilation and tracheostomy requirements and higher mortality rate compared with young trauma patients.

Preinjury Palliative Performance Scale predicts functional outcomes at 6 months in older trauma patients

BACKGROUND

Older trauma patients have increased risk of adverse in-hospital outcomes. We previously demonstrated that low preinjury Palliative Performance Scale (PPS) independently predicted poor discharge outcomes. We hypothesized that low PPS would predict long-term outcomes in older trauma patients.

METHODS

Prospective observational study of trauma patients aged ≥55 years admitted between July 2016 and April 2018. Preinjury PPS was assessed at admission; low PPS was defined as 70 or less. Primary outcomes were mortality and functional outcomes, measured by Extended Glasgow Outcome Scale (GOSE), at discharge and 6 months. Poor functional outcomes were defined as GOSE score of 4 or less. Secondary outcomes were patient-reported outcomes at 6 months: EuroQol-5D and 36-Item Short Form Survey. Adjusted relative risks (aRRs) were obtained for each primary outcome using multivariable modified Poisson regression, adjusting for PPS, age, race/ethnicity, sex, and injury severity.

RESULTS

In-hospital data were available for 516 patients; mean age was 70 years and median Injury Severity Score was 13. Thirty percent had low PPS. Six percent (n = 32) died in the hospital, and half of the survivors (n = 248) had severe disability at discharge. Low PPS predicted hospital mortality (aRR, 2.6; 95% confidence interval [CI], 1.2-5.3) and poor outcomes at discharge (aRR, 2.0; 95% CI, 1.7-2.3). Six-month data were available for 176 (87%) of 203 patients who were due for follow-up. Functional outcomes improved in 64% at 6 months. However, 63% had moderate to severe pain, and 42% moderate to severe anxiety/depression. Mean GOSE improved less over time in low PPS patients (7% vs. 24%; p < 0.01). Low PPS predicted poor functional outcomes at 6 months (aRR, 3.1; 95% CI, 1.8-5.3) while age and Injury Severity Score did not.

CONCLUSION

Preinjury PPS predicts mortality and poor outcomes at discharge and 6 months. Despite improvement in function, persistent pain and anxiety/depression were common. Low PPS patients fail to improve over time compared to high PPS patients. Preinjury PPS can be used on admission for prognostication of short- and long-term outcomes and is a potential trigger for palliative care in older trauma patients.

LEVEL OF EVIDENCE

Prognostic study, Therapeutic level IV.

Risk factors and outcome of acute kidney injury in elderly trauma patients

BACKGROUND

Acute Kidney Injury (AKI) is associated with significant morbidity. The risk factors for AKI in elderly trauma patients have not been defined.

METHODS

Injured patients 75 years old or older from 2014 to 2016 were evaluated. AKI was identified by RIFLE criteria. Patients with and without AKI were compared with chi square, ANOVA, and logistic regression.

RESULTS

836 patients were 75 years old or older. Patients with AKI were more commonly male, hypotensive on admission with a greater Injury Severity Score but age, diabetes, hypertension and baseline creatinine were similar. Patients with AKI had a higher mortality that did not increase with RIFLE stage. Male sex, ISS, hypotension on admission and presence of an extremity injury were independently associated with AKI by logistic regression.

CONCLUSION

AKI in elderly trauma patients is associated with magnitude of injury and shock but not pre-existing medical comorbidities and it significantly increased the risk of death.

Significance of Blood Transfusion Units in Determining the Probability of Mortality among Elderly Trauma Patients Based on the Geriatric Trauma Outcome Scoring System: A Cross-Sectional Analysis Based on Trauma Registered Data

Background: For elderly trauma patients, a prognostic tool called the Geriatric Trauma Outcome Score (GTOS), where GTOS = (age) + (ISS × 2.5) + (22 if any packed red blood cells (pRBCs) were transfused within 24 h after admission), was developed for predicting mortality. In such calculation, a score of 22 was added in the calculation of GTOS regardless of the transfused units of blood. This study aimed to assess the effect of transfused blood units on the mortality outcomes of the elderly trauma patients who received blood transfusion (BT). Methods: Detailed data of 687 elderly trauma patients aged ≥65 years who were transfused with pRBCs within 24 h after admission into a level I trauma center between 1 January 2009 and 31 December 2016 were retrieved from the Trauma Registry System database. Based on the units of pRBCs transfused, the study population was divided into two groups to compare the mortality outcomes between these groups. Adjusted odds ratios (AORs) with its 95% confidence intervals (CIs) for mortality were calculated by adjusting sex, pre-existing comorbidities, and GTOS. Results: When the cut-off value of BT was set as 3 U of pRBCs, patients who received BT ≥ 3 U had higher odds of mortality than those who received BT < 3 U (OR, 3.0; 95% CI, 1.94–4.56; p < 0.001). Patients who received more units of pRBCs still showed higher odds of mortality than their counterparts. After adjusting for sex, pre-existing comorbidities, and GTOS, comparison revealed that the patients who received BT of 3 U to 6 U had a 1.7-fold adjusted odds of mortality than their counterparts. The patients who received BT ≥ 8 U and 10 U had a 2.1-fold (AOR, 2.1; 95% CI, 1.09–3.96; p < 0.001) and 4.4-fold (AOR, 4.4; 95% CI, 2.04–9.48; p < 0.001) adjusted odds of mortality than those who received BT < 8 U and <10 U, respectively. Conclusions: This study revealed that the units of BT did matter in determining the probability of mortality. For those who received more units of blood, the mortality may be underestimated according to the GTOS.

Suicide in the Elderly: A Multidisciplinary Approach to Prevention

Suicide in the elderly is a growing problem. The elderly population is increasing, and elderly patients have multiple issues that place them at higher risk of suicidality. These issues include physical illnesses, mental illness, loss of functional status, isolation, and family, financial, and social factors. Access to firearms is another significant risk factor, because elderly patients are more likely to use firearms in suicide attempts; interventions to reduce firearms mortality may save lives. Tackling the difficult problem of suicide in the elderly may require a multidisciplinary, community-based series of interventions.

Age and Other Risk Factors Influencing Long-Term Mortality in Patients With Traumatic Cervical Spine Fracture

Objective

To identify clinical or demographic variables that influence long-term mortality, as well as in-hospital mortality, with a particular focus on the effects of age.

Summary and Background Data

Cervical spine fractures with or without spinal cord injury (SCI) disproportionately impact the elderly who constitute an increasing percentage of the US population.

Methods

We analyzed data collected for 10 years at a state-designated level I trauma center to identify variables that influenced in-hospital and long-term mortality among elderly patients with traumatic cervical spine fracture with or without SCI. Acute in-hospital mortality was determined from hospital records and long-term mortality within the study period (2003-2013) was determined from the National Death Index. Univariate and multivariate regression analyses were used to identify factors influencing survival.

Results

Data from patients (N = 632) with cervical spine fractures were analyzed, the majority (66%) of whom were geriatric (older than age 64). Most patients (62%) had a mild/moderate injury severity score (ISS; median, interquartile range: 6, 5). Patients with SCI had significantly longer lengths of stay (14.1 days), days on a ventilator (3.5 days), and higher ISS (14.9) than patients without SCI (P < .0001 for all). Falls were the leading mechanism of injury for patients older than age 64. Univariate analysis identified that long-term survival decreased significantly for all patients older than age 65 (hazard ratio [HR]: 1.07; P < .0001). Multivariate analysis demonstrated age (HR: 1.08; P < .0001), gender (HR: 1.60; P < .0007), and SCI status (HR: 1.45, P < .02) significantly influenced survival during the study period.

Conclusion

This study identified age, gender, and SCI status as significant variables for this study population influencing long-term survival among patients with cervical spine fractures. Our results support the growing notion that cervical spine injuries in geriatric patients with trauma may warrant additional research.