Sarani, Babak

Background Studies have shown an increased risk of morbidity in elderly patients suffering rib fractures from blunt trauma. The association between frailty and rib fractures on adverse outcomes is still ill-defined. In the current investigation, we sought to delineate the association between frailty, measured using the Orthopedic Frailty Score (OFS), and outcomes in geriatric patients with isolated rib fractures. Methods All geriatric (aged 65 years or older) patients registered in the 2013–2019 Trauma Quality Improvement database with a conservatively managed isolated rib fracture were considered for inclusion. An isolated rib fracture was defined as the presence of ≥1 rib fracture, a thorax Abbreviated Injury Scale (AIS) between 1 and 5, an AIS ≤1 in all other regions, as well as the absence of pneumothorax, hemothorax, or pulmonary contusion. Based on patients’ OFS, patients were classified as non-frail (OFS 0), pre-frail (OFS 1), or frail (OFS ≥2). The prevalence ratio (PR) of composite complications, in-hospital mortality, failure-to-rescue (FTR), and intensive care unit (ICU) admission between the OFS groups was determined using Poisson regression models to adjust for potential confounding. Results A total of 65 375 patients met the study’s inclusion criteria of whom 60% were non-frail, 29% were pre-frail, and 11% were frail. There was a stepwise increased risk of complications, in-hospital mortality, and FTR from non-frail to pre-frail and frail. Compared with non-frail patients, frail patients exhibited a 87% increased risk of in-hospital mortality [adjusted PR (95% CI): 1.87 (1.52-2.31), p<0.001], a 44% increased risk of complications [adjusted PR (95% CI): 1.44 (1.23-1.67), p<0.001], a doubling in the risk of FTR [adjusted PR (95% CI): 2.08 (1.45-2.98), p<0.001], and a 17% increased risk of ICU admission [adjusted PR (95% CI): 1.17 (1.11-1.23), p<0.001]. Conclusion There is a strong association between frailty, measured using the OFS, and adverse outcomes in geriatric patients managed conservatively for rib fractures.

Background: Traumatic brain injury (TBI) represents a significant global health issue; the traditional tools such as the Glasgow Coma Scale (GCS) and Abbreviated Injury Scale (AIS) which have been used for injury severity grading, struggle to capture outcomes after TBI. Aim and methods: This paper aims to implement extreme gradient boosting (XGBoost), a powerful machine learning algorithm that combines the predictions of multiple weak models to create a strong predictive model with high accuracy and efficiency, in order to develop and validate a predictive model for in-hospital mortality in patients with isolated severe traumatic brain injury and to identify the most influential predictors. In total, 545,388 patients from the 2013–2021 American College of Surgeons Trauma Quality Improvement Program (TQIP) database were included in the current study, with 80% of the patients used for model training and 20% of the patients for the final model test. The primary outcome of the study was in-hospital mortality. Predictors were patients’ demographics, admission status, as well as comorbidities, and clinical characteristics. Penalized Cox regression models were used to investigate the associations between the survival outcomes and the predictors and select the best predictors. An extreme gradient boosting (XGBoost)-powered Cox regression model was then used to predict the survival outcome. The performance of the models was evaluated using the Harrell’s concordance index (C-index). The time-dependent area under the receiver operating characteristic curve (AUC) was used to evaluate the dynamic cumulative performance of the models. The importance of the predictors in the final prediction model was evaluated using the Shapley additive explanations (SHAP) value. Results: On average, the final XGBoost-powered Cox regression model performed at an acceptable level for patients with a length of stay up to 250 days (mean time-dependent AUC = 0.713) in the test dataset. However, for patients with a length of stay between 20 and 213 days, the performance of the model was relatively poor (time-dependent AUC < 0.7). When limited to patients with a length of stay ≤20 days, which accounts for 95.4% of all the patients, the model achieved an excellent performance (mean time-dependent AUC = 0.813). When further limited to patients with a length of stay ≤5 days, which accounts for two-thirds of all the patients, the model achieved an outstanding performance (mean time-dependent AUC = 0.917). Conclusion: The XGBoost-powered Cox regression model can achieve an outstanding predictive ability for in-hospital mortality during the first 5 days, primarily based on the severity of the injury, the GCS on admission, and the patient’s age. These variables continue to demonstrate an excellent predictive ability up to 20 days after admission, a period of care that accounts for over 95% of severe TBI patients. Past 20 days of care, other factors appear to be the primary drivers of in-hospital mortality, indicating a potential window of opportunity for improving outcomes.

Background Coronavirus disease 2019 (COVID-19) is associated with a prothrombotic state; leading to multiple sequelae. We sought to detect whether thromboelastography (TEG) parameters would be able to detect thromboembolic events in patients hospitalized with COVID-19. Methods We performed a retrospective multicenter case–control study of the Collaborative Research to Understand the Sequelae of Harm in COVID (CRUSH COVID) registry of 8 tertiary care level hospitals in the United States (US). This registry contains adult patients with COVID-19 hospitalized between March 2020 and September 2020. Results A total of 277 hospitalized COVID-19 patients were analyzed to determine whether conventional coagulation TEG parameters were associated with venous thromboembolic (VTE) and thrombotic events during hospitalization. A clotting index (CI) >3 was present in 45.8% of the population, consistent with a hypercoagulable state. Eighty-three percent of the patients had clot lysis at 30 min (LY30) = 0, consistent with fibrinolysis shutdown, with a median of 0.1%. We did not find TEG parameters (LY30 area under the receiver operating characteristic [ROC] curve [AUC] = 0.55, 95% CI: 0.44-0.65, P value = .32; alpha angle [α] AUC = 0.58, 95% CI: 0.47-0.69, P value = .17; K time AUC = 0.58, 95% CI: 0.46-0.69, P value = .67; maximum amplitude (MA) AUC = 0.54, 95% CI: 0.44-0.64, P value = .47; reaction time [R time] AUC = 0.53, 95% CI: 0.42-0.65, P value = .70) to be a good discriminator for VTE. We also did not find TEG parameters (LY30 AUC = 0.51, 95% CI: 0.42-0.60, P value = .84; R time AUC = 0.57, 95%CI: 0.48-0.67, P value .07; α AUC = 0.59, 95%CI: 0.51-0.68, P value = .02; K time AUC = 0.62, 95% CI: 0.53-0.70, P value = .07; MA AUC = 0.65, 95% CI: 0.57-0.74, P value < .01) to be a good discriminator for thrombotic events. Conclusions In this retrospective multicenter cohort study, TEG in COVID-19 hospitalized patients may indicate a hypercoagulable state, however, its use in detecting VTE or thrombotic events is limited in this population.