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Background: Extremity fracture is frequently seen in multiple traumatized patients. Local post-traumatic inflammatory reactions as well as local and systemic interactions have been described in previous studies. However, trauma severity and its impact on the local immunologic reaction remains unclear. Therefore, fracture-associated local inflammation was investigated in a porcine model of isolated and combined trauma to gain information about the early inflammatory stages.
Material and Methods: Polytrauma (PT) consisted of lung contusion, liver laceration, femur fracture, and controlled hemorrhage. Monotrauma (MT) consisted of femur fracture only. The fracture was operatively stabilized and animals were monitored under ICU-standard for 72 h. Blood, fracture hematoma (FH) as well as muscle samples were collected throughout the experimental period. Levels of local and systemic pro- and anti-inflammatory as well as angiogenetic cytokines were measured by ELISA.
Results: Both groups showed a significant decrease in pro-inflammatory IL-6 in FH over time. However, concentrations in MT were significantly higher than in PT. The IL-8 concentrations initially decreased in FH, but recovered by the end of the observation period. These dynamics were only statistically significant in MT. Furthermore, concentrations measured in muscle tissue showed inverse kinetics compared to those in FH. The IL-10 did not present statistical resilient dynamics over time, although a slight increase in FH was seen by the end of the observation time in the MT group.
Conclusions: Time-dependent dynamics of the local inflammatory response were observed. Trauma severity showed a significant impact, with lower values in pro- as well as angiogenetic mediators. Fracture repair could be altered by these trauma-related changes of the local immunologic milieu, which might serve as a possible explanation for the higher rates of delayed or non-union bone repair in polytraumatised patients.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Background: Surgical complications are associated with a significant burden to patients and hospitals and are increasingly discussed in recent literature. This cohort study reviewed surgery-related complications in a Level I trauma center. The effect of a complication avoidance care bundle on the rate of surgical complications was analyzed. Methods: All complications (surgical and nonsurgical) that occur in our trauma department are prospectively captured using a standardized documentation form and are discussed and analyzed in a weekly trauma Morbidity and Mortality (M&M) conference. Surgical complication rates are calculated using the annual surgical procedure numbers. Based on discussions in the M&M conference, a complication avoidance care bundle consisting of five measures was established: (1) Improving team situational awareness; (2) reducing operating room traffic by staff members and limiting door-opening events; (3) preoperative screening for infectious foci; (4) adapted preoperative antibiotic prophylaxis in anatomic regions with a high risk of infectious complications; and (5) use of iodine-impregnated adhesive drape. Results: The number of surgical procedures steadily increased over the study years, from 3587 in 2015 to 3962 in 2019 (an increase of 10.5%). Within this 5-year study period, the overall rate of surgical complications was 0.8%. Surgical site infections were the most frequently found complications (n = 40, 24.8% of all surgical complications), followed by screw malposition (n = 20, 12.4%), postoperative dislocations of arthroplasties (n = 18, 11.2%), and suboptimal fracture reduction (n = 18, 11.2%). Following implementation of the complication avoidance care bundle, the overall rate of surgical complications significantly decreased, from 1.14% in the year 2016 to 0.56% in the study year 2019, which represents a reduction of 51% within a 3-year time period. Conclusions: A multimodal strategy targeted at reducing the surgical complication rate can be successfully established based on a transparent discussion of adverse surgical outcomes. The combination of the different preventive measures was associated with reducing the overall complication rate by half within a 3-year time period.