Search Results (12)

Background/Objectives: Trauma scoring systems are essential tools for predicting clinical outcomes in patients with multiple injuries. This study aimed to compare the performance of various anatomical and physiological scoring systems in predicting mortality among patients admitted to the emergency department following traffic accidents. Methods: In this prospective observational study, trauma patients presenting with traffic-related injuries were evaluated using seven scoring systems: ISS, NISS, AIS, GCS, RTS, TRISS, and APACHE II. Demographic data, clinical findings, and laboratory values were recorded. The prognostic performance of each score was assessed using ROC curve analysis, and diagnostic metrics including sensitivity, specificity, and likelihood ratios were calculated. Results: Among 554 patients included in the study, the overall mortality rate was 2%. The TRISS and GCS scores demonstrated the highest predictive performance, each with an AUC of 0.98, sensitivity of 100%, and specificity exceeding 93%. APACHE II followed closely with an AUC of 0.97, also achieving 100% sensitivity. NISS (AUC = 0.92) and ISS (AUC = 0.91) were effective anatomical scores, while RTS showed moderate predictive value (AUC = 0.90). Strong correlations were noted between ISS, NISS, and AIS (Rho > 0.85), while RTS was negatively correlated with these anatomical scores. All scoring systems showed statistically significant associations with mortality. Conclusions: TRISS, GCS, and APACHE II were the most effective trauma scoring systems in predicting mortality among emergency department patients. While complex models offer higher accuracy, simpler scores such as RTS and GCS remain valuable for rapid triage. The integration of both anatomical and physiological parameters may enhance early risk stratification and support timely decision-making in trauma care. Full article

Objectives: True trifurcation disease of the left main coronary artery is a rare situation encountered in clinical practice. To date, there is no evidence for a standardized strategy of percutaneous coronary intervention in this type of lesion. Methods: This article describes a novel three-stent strategy using a combination of Triple-Kissing Balloon Crush in both of the side branches. This technique is based on a well-established bifurcation stenting technique, namely, the Double-Kissing Crush technique. Results: This strategy was implemented successfully, demonstrating technical feasibility and optimal stent apposition in the trifurcation lesion, ensuring the preservation of all three branches. Conclusions: Although more data and clinical trials are needed to develop proper evidence-based guidelines, three-stent implantation with Double-Trissing Crush should be taken into consideration as a viable strategy for LM trifurcation lesions in the proper set of patients. Full article

Background: Scientific studies on severely injured patients commonly utilize the Abbreviated Injury Scale (AIS) and the Injury Severity Score (ISS) for injury assessment and to characterize trauma cohorts. However, due to potential deterioration (e.g., in the case of an increasing hemorrhage) during the clinical course, the assessment of injury severity in traumatic brain injury (TBI) can be challenging. Therefore, the aim of this study was to investigate whether and to what extent the worsening of TBI affects the AIS and ISS. Methods: We retrospectively evaluated 80 polytrauma patients admitted to the trauma room of our level I trauma center with computed-tomography-confirmed TBI. The initial AIS, ISS, and Trauma and Injury Severity Score (TRISS) values were reevaluated after follow-up imaging. Results: A total of 37.5% of the patients showed a significant increase in AIS_head (3.7 vs. 4.1; p = 0.002) and the ISS (22.9 vs. 26.7, p = 0.0497). These changes resulted in an eight percent reduction in their TRISS-predicted survival probability (74.82% vs. 66.25%, p = 0.1835). Conclusions: The dynamic nature of intracranial hemorrhage complicates accurate injury severity assessment using the AIS and ISS, necessitating consideration in clinical studies and registries to prevent systematic bias in patient selection and subsequent data analysis. Full article

To date, there is no clinically useful prediction model that is suitable for Japanese pediatric trauma patients. Herein, this study aimed to developed a model for predicting the survival of Japanese pediatric patients with blunt trauma and compare its validity with that of the conventional TRISS model. Patients registered in the Japan Trauma Data Bank were grouped into a derivation cohort (2009–2013) and validation cohort (2014–2018). Logistic regression analysis was performed using the derivation dataset to establish prediction models using age, injury severity, and physiology. The validity of the modified model was evaluated by the area under the receiver operating characteristic curve (AUC). Among 11 predictor models, Model 1 and Model 11 had the best performance (AUC = 0.980). The AUC of all models was lower in patients with survival probability Ps < 0.5 than in patients with Ps ≥ 0.5. The AUC of all models was lower in neonates/infants than in other age categories. Model 11 also had the best performance (AUC = 0.762 and 0.909, respectively) in patients with Ps < 0.5 and neonates/infants. The predictive ability of the newly modified models was not superior to that of the current TRISS model. Our results may be useful to develop a highly accurate prediction model based on the new predictive variables and cutoff values associated with the survival mortality of injured Japanese pediatric patients who are younger and more severely injured by using a nationwide dataset with fewer missing data and added valuables, which can be used to evaluate the age-related physiological and anatomical severity of injured patients. Full article

Background: Most patients with blunt aortic injuries, who arrive alive in a clinic, suffer from traumatic pseudoaneurysms. Due to modern treatments, the perioperative mortality has significantly decreased. Therefore, it is unclear how exact the prediction of commonly used scoring systems of the outcome is. Methods: We analyzed data on 65 polytraumatized patients with blunt aortic injuries. The following scores were calculated: injury severity score (ISS), new injury severity score (NISS), trauma and injury severity score (TRISS), revised trauma score coded (RTSc) and acute physiology and chronic health evaluation II (APACHE II). Subsequently, their predictive value was evaluated using Spearman´s and Kendall´s correlation analysis, logistic regression and receiver operating characteristics (ROC) curves. Results: A proportion of 83% of the patients suffered from a thoracic aortic rupture or rupture with concomitant aortic wall dissection (54/65). The overall mortality was 24.6% (16/65). The sensitivity and specificity were calculated as the area under the receiver operating curves (AUC): NISS 0.812, ISS 0.791, APACHE II 0.884, RTSc 0.679 and TRISS 0.761. Logistic regression showed a slightly higher specificity to anatomical scoring systems (ISS 0.959, NISS 0.980, TRISS 0.957, APACHE II 0.938). The sensitivity was highest in the APACHE II with 0.545. Sensitivity and specificity for the RTSc were not significant. Conclusion: The predictive abilities of all scoring systems were very limited. All scoring systems, except the RTSc, had a high specificity but a low sensitivity. In our study population, the RTSc was not applicable. The APACHE II was the most sensitive score for mortality. Anatomical scoring systems showed a positive correlation with the amount of transfused blood products. Full article

Introduction: Big data-based artificial intelligence (AI) has become increasingly important in medicine and may be helpful in the future to predict diseases and outcomes. For severely injured patients, a new analytics tool has recently been developed (WATSON Trauma Pathway Explorer) to assess individual risk profiles early after trauma. We performed a validation of this tool and a comparison with the Trauma and Injury Severity Score (TRISS), an established trauma survival estimation score. Methods: Prospective data collection, level I trauma centre, 1 January 2018–31 December 2019. Inclusion criteria: Primary admission for trauma, injury severity score (ISS) ≥ 16, age ≥ 16. Parameters: Age, ISS, temperature, presence of head injury by the Glasgow Coma Scale (GCS). Outcomes: SIRS and sepsis within 21 days and early death within 72 h after hospitalisation. Statistics: Area under the receiver operating characteristic (ROC) curve for predictive quality, calibration plots for graphical goodness of fit, Brier score for overall performance of WATSON and TRISS. Results: Between 2018 and 2019, 107 patients were included (33 female, 74 male; mean age 48.3 ± 19.7; mean temperature 35.9 ± 1.3; median ISS 30, IQR 23–36). The area under the curve (AUC) is 0.77 (95% CI 0.68–0.85) for SIRS and 0.71 (95% CI 0.58–0.83) for sepsis. WATSON and TRISS showed similar AUCs to predict early death (AUC 0.90, 95% CI 0.79–0.99 vs. AUC 0.88, 95% CI 0.77–0.97; p = 0.75). The goodness of fit of WATSON (X² = 8.19, Hosmer–Lemeshow p = 0.42) was superior to that of TRISS (X² = 31.93, Hosmer–Lemeshow p < 0.05), as was the overall performance based on Brier score (0.06 vs. 0.11 points). Discussion: The validation supports previous reports in terms of feasibility of the WATSON Trauma Pathway Explorer and emphasises its relevance to predict SIRS, sepsis, and early death when compared with the TRISS method. Full article

Background: Prediction of mortality outcomes in trauma patients in the intensive care unit (ICU) is important for patient care and quality improvement. We aimed to measure the performance of 11 prognostic scoring systems for predicting mortality outcomes in trauma patients in the ICU. Methods: Prospectively registered data in the Trauma Registry System from 1 January 2016 to 31 December 2018 were used to extract scores from prognostic scoring systems for 1554 trauma patients in the ICU. The following systems were used: the Trauma and Injury Severity Score (TRISS); the Acute Physiology and Chronic Health Evaluation (APACHE II); the Simplified Acute Physiology Score (SAPS II); mortality prediction models (MPM II) at admission, 24, 48, and 72 h; the Multiple Organ Dysfunction Score (MODS); the Sequential Organ Failure Assessment (SOFA); the Logistic Organ Dysfunction Score (LODS); and the Three Days Recalibrated ICU Outcome Score (TRIOS). Predictive performance was determined according to the area under the receiver operator characteristic curve (AUC). Results: MPM II at 24 h had the highest AUC (0.9213), followed by MPM II at 48 h (AUC: 0.9105). MPM II at 24, 48, and 72 h (0.8956) had a significantly higher AUC than the TRISS (AUC: 0.8814), APACHE II (AUC: 0.8923), SAPS II (AUC: 0.9044), MPM II at admission (AUC: 0.9063), MODS (AUC: 0.8179), SOFA (AUC: 0.7073), LODS (AUC: 0.9013), and TRIOS (AUC: 0.8701). There was no significant difference in the predictive performance of MPM II at 24 and 48 h (p = 0.37) or at 72 h (p = 0.10). Conclusions: We compared 11 prognostic scoring systems and demonstrated that MPM II at 24 h had the best predictive performance for 1554 trauma patients in the ICU. Full article

Background: We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). Methods: Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves. Results: In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models. Conclusions: These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity. Full article

The reverse shock index (rSI) multiplied by Glasgow Coma Scale (GCS) score (rSIG), calculated by multiplying the GCS score with systolic blood pressure (SBP)/hear rate (HR), was proposed to be a reliable triage tool for identifying risk of in-hospital mortality in trauma patients. This study was designed to externally validate the accuracy of the rSIG in the prediction of mortality in our cohort of trauma patients, in comparison with those that were predicted by the Revised Trauma Score (RTS), shock index (SI), and Trauma and Injury Severity Score (TRISS). Adult trauma patients aged ≥20 years who were admitted to the hospital from 1 January 2009 to 31 December 2017, were included in this study. The rSIG, RTS, and SI were calculated according to the initial vital signs and GCS scores of patients upon arrival at the emergency department (ED). The end-point of primary outcome is in-hospital mortality. Discriminative power of each score to predict mortality was measured using area under the curve (AUC) by plotting the receiver operating characteristic (ROC) curve for 18,750 adult trauma patients, comprising 2438 patients with isolated head injury (only head Abbreviated Injury Scale (AIS) ≥ 2) and 16,312 without head injury (head AIS ≤ 1). The predictive accuracy of rSIG was significantly lower than that of RTS in all trauma patients (AUC 0.83 vs. AUC 0.85, p = 0.02) and in patients with isolated head injury (AUC 0.82 vs. AUC 0.85, p = 0.02). For patients without head injury, no difference was observed in the predictive accuracy between rSIG and RTS (AUC 0.83 vs. AUC 0.83, p = 0.97). Based on the cutoff value of 14.0, the rSIG can predict the probability of dying in trauma patients without head injury with a sensitivity of 61.5% and specificity of 94.5%. The predictive accuracy of both rSIG and RTS is significantly poorer than that of TRISS, in all trauma patients (AUC 0.93) or in patients with (AUC 0.89) and without head injury (AUC 0.92). In addition, SI had the significantly worse predictive accuracy than all of the other three models in all trauma patients (AUC 0.57), and the patients with (AUC 0.53) or without (AUC 0.63) head injury. This study revealed that rSIG had a significantly higher predictive accuracy of mortality than SI in all of the studied population but a lower predictive accuracy of mortality than RTS in all adult trauma patients and in adult patients with isolated head injury. In addition, in the adult patients without head injury, rSIG had a similar performance as RTS to the predictive risk of mortality of the patients. Full article

With the advances in the microfabrication of analogue front-end devices, and embedded and signal processing technology, it has now become possible to devise miniaturized health monitoring kits for non-invasive real time monitoring at any location. The current commonly available kits only measure singleton physiological parameters, and a composite analysis that covers all vital signs and trauma scores seems to be missing with these kits. The research aims at using vital signs and other physiological parameters to calculate trauma scores National Early Warning Score (NEWS), Revised Trauma Score (RTS), Trauma Score - Injury Severity Score (TRISS) and Prediction of survival (Ps), and to log the trauma event to electronic health records using standard coding schemes. The signal processing algorithms were implemented in MATLAB and could be ported to TI AM335x using MATLAB/Embedded Coder. Motion artefacts were removed using a level ‘5’ stationary wavelet transform and a ‘sym4’ wavelet, which yielded a signal-to-noise ratio of 27.83 dB. To demonstrate the operation of the device, an existing Physionet, MIMIC II Numerics dataset was used to calculate NEWS and RTS scores, and to generate the correlation and regression models for a clinical class of patients with respiratory failure and admitted to Intensive Care Unit (ICU). Parameters such as age, heart rate, Systolic Blood Pressure (SysBP), respiratory rate, and Oxygen Saturation (SpO2) as predictors to Ps, showed significant positive regressions of 93% at p < 0.001. The NEWS and RTS scores showed no significant correlation (r = 0.25, p < 0.001) amongst themselves; however, the NEWS and RTS together showed significant correlations with Ps (blunt) (r = 0.70, p < 0.001). RTS and Ps (blunt) scores showed some correlations (r = 0.63, p < 0.001), and the NEWS score showed significant correlation (r = 0.79, p < 0.001) with Ps (blunt) scores. Global Positioning System (GPS) system was built into the kit to locate the individual and to calculate the shortest path to the nearest healthcare center using the Quantum Geographical Information System (QGIS) Network Analysis tool. The physiological parameters from the sensors, along with the calculated trauma scores, were encoded according to a standard Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) coding system, and the trauma information was logged to electronic health records using Fast Health Interoperability Resources (FHIR) servers. The FHIR servers provided interoperable web services to log the trauma event information in real time and to prepare for medical emergencies. Full article

Geriatric traumatic injuries in emergency departments are frequent and associated with higher mortality rates and catastrophic functional outcomes. Several prediction scores have been established to manage traumatic patients, including the shock index (SI), revised trauma score (RTS), injury severity score (ISS), trauma injury severity score (TRISS), and new injury severity score (NISS). However, it was necessary to investigate the effectiveness and efficiency of care for the geriatric traumatic population. In addition, image studies such as computed tomography and magnetic resonance imaging play an important role in early diagnosis and timely intervention. However, few studies focus on this aspect. The association between the benefit of carrying out more image studies and clinical outcomes remains unclear. In this study, we included a total of 2688 traumatic patients and analyzed the clinical outcomes and predicting factors in terms of geriatric trauma via pre-hospital and in-hospital analysis. Our evaluation revealed that a shock index ≥1 may be not a strong predictor of geriatric trauma due to the poor physical response in the aging population. This should be modified in geriatric patients. Other systems, like RTS, ISS, TRISS, and NISS, were significant in terms of predicting the clinical outcome. Full article

A series of novel functionalized mono-, bis- and tris-(S)-{[(2S,4R,8R)-8-ethyl-quinuclidin-2-yl](6-methoxyquinolin-4-yl)}methanamines including ferrocene-containing derivatives was obtained by the reaction of the precursor amine with a variety of acylation agents. Their in vitro antitumor activity was investigated against human leukemia (HL-60), human neuroblastoma (SH-SY5Y), human hepatoma (HepG2) and human breast cancer (MCF-7) cells by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT)-assay and the 50% inhibitory concentration (IC₅₀) values were determined. Our data indicate that the precursor amine has no antitumor activity in vitro, but the bis-methanamines with ureido-, thioureido and amide-type linkers display attractive in vitro cytotoxicity and cytostatic effects on HL-60, HepG2, MCF-7 and SH-SY5Y cells. Besides ¹H- and ¹³C-NMR methods the structures of the new model compounds were also studied by DFT calculations. Full article