Factors Influencing Geriatric Orthopaedic Trauma Mortality

Fracture related infection and sepsis in orthopedic trauma: A review

Trauma is a leading cause of death in the United States for people under 45. Amongst trauma-related injuries, orthopedic injuries represent a significant component of trauma-related morbidity. In addition to the potential morbidity and mortality secondary to the specific traumatic injury or injuries sustained, sepsis is a significant cause of morbidity and mortality in trauma patients as well, and infection related to orthopedic trauma can be especially devastating. Therefore, infection prevention and early recognition of infections is crucial to lowering morbidity and mortality in trauma. Risk factors for fracture-related infection include obesity, tobacco use, open fracture, and need for flap coverage, as well as fracture of the tibia and the degree of contamination. Timely administration of prophylactic antibiotics for patients presenting with open fractures has been shown to decrease the risk of fracture-related infection, and in patients that do experience sepsis from an orthopedic injury, prompt source control is critical, which may include the removal of implanted hardware in infections that occur more than 6 weeks from operative fixation. Given that orthopedic injury constitutes a significant proportion of traumatic injuries, and will likely continue to increase in number in the future, surgeons caring for patients with orthopedic trauma must be able to promptly recognize and manage sepsis secondary to orthopedic injury.

Development and validation of an artificial intelligence mobile application for predicting 30-day mortality in critically ill patients with orthopaedic trauma

BACKGROUND

Given the intricate and grave nature of trauma-related injuries in ICU settings, it is imperative to develop and deploy reliable predictive tools that can aid in the early identification of high-risk patients who are at risk of early death. The objective of this study is to create and validate an artificial intelligence (AI) model that can accurately predict early mortality among critical fracture patients.

METHODS

A total of 2662 critically ill patients with orthopaedic trauma were included from the MIMIC III database. Early mortality was defined as death within 30 days in this study. The patients were randomly divided into a model training cohort and a model validation cohort. Various algorithms, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), support vector machine (SVM), random forest (RF), and neural network (NN), were employed. Evaluation metrics, including discrimination and calibration, were used to develop a comprehensive scoring system ranging from 0 to 60, with higher scores indicating better prediction performance. Furthermore, external validation was carried out using 131 patients. The optimal model was deployed as an internet-based AI tool.

RESULTS

Among all models, the eXGBM demonstrated the highest area under the curve (AUC) value (0.974, 95%CI: 0.959-0.983), followed by the RF model (0.951, 95%CI: 0.935-0.967) and the NN model (0.922, 95%CI: 0.905-0.941). Additionally, the eXGBM model outperformed other models in terms of accuracy (0.915), precision (0.906), recall (0.926), F1 score (0.916), Brier score (0.062), log loss (0.210), and discrimination slope (0.767). Based on the scoring system, the eXGBM model achieved the highest score (53), followed by RF (42) and NN (39). The LR, DT, and SVM models obtained scores of 28, 18, and 32, respectively. Decision curve analysis further confirmed the superior clinical net benefits of the eXGBM model. External validation of the model achieved an AUC value of 0.913 (95%CI: 0.878-0.948). Consequently, the model was deployed on the Internet at https://30-daymortalityincriticallyillpatients-fnfsynbpbp6rgineaspuim.streamlit.app/, allowing users to input patient features and obtain predicted risks of early mortality among critical fracture patients. Furthermore, the AI model successfully stratified patients into low or high risk of early mortality based on a predefined threshold and provided recommendations for appropriate therapeutic interventions.

CONCLUSION

This study successfully develops and validates an AI model, with the eXGBM algorithm demonstrating the highest predictive performance for early mortality in critical fracture patients. By deploying the model as a web-based AI application, healthcare professionals can easily access the tool, enabling them to predict 30-day mortality and aiding in the identification and management of high-risk patients among those critically ill with orthopedic trauma.

Thoracic injuries in trauma patients: epidemiology and its influence on mortality

BACKGROUND

Thoracic injuries are common among trauma patients. Studies on trauma patients with thoracic injuries have reported considerable differences in morbidity and mortality, and there is limited research on comparison between trauma patients with and without thoracic injuries, particularly in the Scandinavian population. Thoracic injuries in trauma patients should be identified early and need special attention since the differences in injury patterns among patient population are important as they entail different treatment regimens and influence patient outcomes. The aim of the study was to describe the epidemiology of trauma patients with and without thoracic injuries and its influence on 30-day mortality.

METHODS

Patients were identified through the Karolinska Trauma Register. The Abbreviated Injury Scale (AIS) system was used to find patients with thoracic injuries. Logistic regression analysis was performed to evaluate factors [age, gender, ASA class, GCS (Glasgow Coma Scale), NISS (New Injury Severity Score) and thoracic injury] associated with 30-day mortality.

RESULTS

A total of 2397 patients were included. Of those, 768 patients (32%) had a thoracic injury. The mean (± SD, range) age of all patients (n = 2397) was 46 (20, 18-98) years, and the majority (n = 1709, 71%) of the patients were males. There was a greater proportion of patients with rib fractures among older (≥ 60 years) patients, whereas younger patients had a higher proportion of injuries to the internal thoracic organs. The 30-day mortality was 11% (n = 87) in patients with thoracic injury and 4.3% (n = 71) in patients without. After multivariable adjustment, a thoracic injury was found to be associated with an increased risk of 30-day mortality (OR 1.9, 95% CI 1.3-3.0); as was age ≥ 60 years (OR 3.7, 95% CI 2.3-6.0), ASA class 3-4 (OR 2.3, 95% CI 1.4-3.6), GCS 1-8 (OR 21, 95% CI 13-33) and NISS > 15 (OR 4.2, 2.4-7.3).

CONCLUSION

Thoracic injury was an independent predictor of 30-day mortality after adjustment for relevant key variables. We also found a difference in injury patterns with older patients having a higher proportion of rib fractures, whilst younger patients suffered more internal thoracic organ injuries.

Comorbidity and Management of Concurrent Psychiatric and Medical Disorders

Aging increases susceptibility to medical and psychiatric comorbidity via interrelated biological, psychological, and social mechanisms. Mental status changes or other psychiatric symptoms occurring in older adults with medical disorders most often result from delirium, depression, or the onset of Alzheimer's disease and related dementias (ADRD). Clinicians can use evidence-based tools to evaluate such symptoms including the 4A's Test for delirium, the Saint Louis University Mental Status Exam, and the Geriatric Depression Scale. Innovative models such as collaborative care can improve the outcome of care of older adults with medical disorders requiring treatment for depression or ADRD..