25-Year experience with adult polytraumatized patients in a European level 1 trauma center: polytrauma between 1995 and 2019. What has changed? A retrospective cohort study.

PURPOSE: To analyze the changes of the clinical characteristics, injury patterns, and mortality rates of polytraumatized patients within the past 25 years in a European Level I trauma center. METHODS: 953 consecutive polytraumatized patients treated at a single-level 1 trauma center between January 1995 and December 2019 were enrolled retrospectively. Polytrauma was defined as AIS ≥ 3 points in at least two different body regions. Retrospective data analysis on changes of clinical characteristics and mortality rates over time. RESULTS: A significant increase of the average age by 2 years per year of the study could be seen with a significant increase of geriatric patients over time. No changes of the median Injury Severity Score (ISS) could be seen over time, whereas the ISS significantly decreased by patient's year. The rates of concomitant severe traumatic brain injury (TBI) remained constant over time, and did not increase with rising age of the patients. Although, the mortality rate remained constant over time the relative risk of overall in-hospital mortality increased by 1.7% and the relative risk of late-phase mortality increased by 2.2% per patient's year. CONCLUSION: The number of polytraumatized patients remained constant over the 25-year study period. Also, the mortality rates remained stable over time, although a significant increase of the average age of polytraumatized patients could be seen with stable injury severity scores. Severe TBI and age beyond 65 years remained independent prognostic factors on the late-phase survival of polytraumatized patients. TRIAL REGISTRATION: NCT04723992. LEVEL OF EVIDENCE: Prognostic study, level III.

Red blood cell distribution width and Charlson comorbidity index help to identify frail polytraumatized patients : Experiences from a level I trauma center.

INTRODUCTION: Little is known about the potential impact of the red blood cell distribution width (RDW) and pre-existing comorbidities on the late-phase survival of polytraumatized patients. METHODS: A total of 173 polytraumatized patients were included retrospectively in this cohort study in a level I trauma center from January 2012 to December 2015. The Charlson comorbidity index (CCI) scores and RDW values were evaluated. RESULTS: Out of all polytraumatized patients (n = 173), 72.8% (n = 126) were male, the mean ISS was 31.7 points (range 17-75) and the mean age was 45.1 years (range 18-93 years). Significantly higher RDW values (13.90 vs. 13.37; p = 0.006) and higher CCI scores (3.38 vs. 0.49; p < 0.001) were seen in elderly polytraumatized patients (age > 55 years). RDW values > 13.75% (p = 0.033) and CCI scores > 2 points (p = 0.001) were found to have a significant influence on the late-phase survival of polytraumatized patients. Age > 55 years (p = 0.009, HR 0.312; 95% confidence interval (CI) 0.130-0.749) and the presence of severe traumatic brain injury (TBI) (p = 0.007; HR 0.185; 95% CI 0.054-0.635) remained as independent prognostic factors on the late-phase survival after multivariate analysis. CONCLUSION: Even younger elderly polytraumatized patients (> 55 years of age) showed significant higher RDW values and higher CCI scores. In addition to the presence of severe TBI and age > 55 years, RDW value > 13.75% on admission and CCI score > 2 might help to identify the "younger" frail polytraumatized patient at risk.

Impact of Polytrauma and Acute Respiratory Distress Syndrome on Markers of Fibrinolysis: A Prospective Pilot Study.

Acute respiratory distress syndrome (ARDS), which is associated with major morbidity and high mortality, is commonly developed by polytraumatized patients. Its pathogenesis is complex, and its development is difficult to anticipate, as candidate biomarkers for the prediction of ARDS were found not to be reliable for clinical use. In this prospective study, we assessed the serum antigen levels of tissue plasminogen activator (tPA) and plasminogen activator inhibitor type-1 (PAI-1) of 28 survivors of blunt polytrauma (age ≥18 years; injury severity score ≥16) at admission and on days 1, 3, 5, 7, 10, 14, and 21 of hospitalization. Our results show that these patients presented high mean tPA and PAI-1 antigen levels at admission; despite their decline, these parameters remained elevated for 3 weeks. Over this period, the mean tPA antigen level was higher in polytrauma victims suffering from ARDS than in those without ARDS, whereas the mean PAI-1 level was higher in polytrauma victims sustaining pneumonia than in those without pneumonia. Moreover, in each individual developing ARDS, the polytrauma-related elevated tPA antigen level either continued to rise after admission or suffered a second increase up to the onset of ARDS, declining immediately thereafter. Therefore, our findings support the assessment of serum tPA antigen levels after the initial treatment of polytraumatized patients, as this parameter shows potential as a biomarker for the development of ARDS and for the consequent identification of high-risk individuals.

IL-33 and its increased serum levels as an alarmin for imminent pulmonary complications in polytraumatized patients.

Background: According to recently published findings, we hypothesized that serum interleukin-33 (IL-33) may qualify for predicting pulmonary complications in polytraumatized patients. Methods: One hundred and thirty patients (age ≥ 18 years, ISS ≥ 16) were included in our prospective analysis after primary admission to our level I trauma center during the first post-traumatic hour. Serum samples immediately after admission and on day 2 after trauma were obtained and analyzed. Results: Median initial IL-33 levels (in picograms per milliliter) were higher in polytrauma victims (1) with concomitant thoracic trauma [5.08 vs. 3.52; p = 0.036], (2) sustaining parenchymal lung injury (PLI) [5.37 vs. 3.71; p = 0.027], and (3) developing acute respiratory distress syndrome (ARDS) [6.19 vs. 4.48; p = 0.003], compared to the respective rest of the study group. The median initial IL-33 levels were higher in patients experiencing both PLI and ARDS compared to those sustaining PLI and not developing ARDS [6.99 vs. 4.69; p = 0.029]. ROC statistics provided an AUC of 0.666 (p = 0.003) and a cut-off value of 4.77 (sensitivity, 71.8%; specificity, 75.7%) for predicting ARDS. Moreover, a higher initial median IL-33 level was revealed in the deceased compared to the survivors [12.25 vs. 4.72; p = 0.021]. ROC statistics identified the initial level of IL-33 as a predictor of death with 11.19 as cut-off value (sensitivity, 80.0%; specificity, 80.0%; AUC = 0.805; p = 0.021). Conclusions: Following tissue damage, IL-33 is abundantly released in the serum of polytraumatized patients immediately after their injuries occurred. As initial IL-33 levels were particularly high in individuals experiencing both PLI and ARDS, IL-33 release after trauma seems to be involved in the promotion of ARDS and might serve already at admission as a solid indicator of impending death in polytraumatized patients.

Neither Single nor a Combination of Routine Laboratory Parameters can Discriminate between Gram-positive and Gram-negative Bacteremia.

Adequate early empiric antibiotic therapy is pivotal for the outcome of patients with bloodstream infections. In clinical practice the use of surrogate laboratory parameters is frequently proposed to predict underlying bacterial pathogens; however there is no clear evidence for this assumption. In this study, we investigated the discriminatory capacity of predictive models consisting of routinely available laboratory parameters to predict the presence of Gram-positive or Gram-negative bacteremia. Major machine learning algorithms were screened for their capacity to maximize the area under the receiver operating characteristic curve (ROC-AUC) for discriminating between Gram-positive and Gram-negative cases. Data from 23,765 patients with clinically suspected bacteremia were screened and 1,180 bacteremic patients were included in the study. A relative predominance of Gram-negative bacteremia (54.0%), which was more pronounced in females (59.1%), was observed. The final model achieved 0.675 ROC-AUC resulting in 44.57% sensitivity and 79.75% specificity. Various parameters presented a significant difference between both genders. In gender-specific models, the discriminatory potency was slightly improved. The results of this study do not support the use of surrogate laboratory parameters for predicting classes of causative pathogens. In this patient cohort, gender-specific differences in various laboratory parameters were observed, indicating differences in the host response between genders.

A risk prediction model for screening bacteremic patients: a cross sectional study.

BACKGROUND: Bacteraemia is a frequent and severe condition with a high mortality rate. Despite profound knowledge about the pre-test probability of bacteraemia, blood culture analysis often results in low rates of pathogen detection and therefore increasing diagnostic costs. To improve the cost-effectiveness of blood culture sampling, we computed a risk prediction model based on highly standardizable variables, with the ultimate goal to identify via an automated decision support tool patients with very low risk for bacteraemia. METHODS: In this retrospective hospital-wide cohort study evaluating 15,985 patients with suspected bacteraemia, 51 variables were assessed for their diagnostic potency. A derivation cohort (n = 14.699) was used for feature and model selection as well as for cut-off specification. Models were established using the A2DE classifier, a supervised Bayesian classifier. Two internally validated models were further evaluated by a validation cohort (n = 1,286). RESULTS: The proportion of neutrophile leukocytes in differential blood count was the best individual variable to predict bacteraemia (ROC-AUC: 0.694). Applying the A2DE classifier, two models, model 1 (20 variables) and model 2 (10 variables) were established with an area under the receiver operating characteristic curve (ROC-AUC) of 0.767 and 0.759, respectively. In the validation cohort, ROC-AUCs of 0.800 and 0.786 were achieved. Using predefined cut-off points, 16% and 12% of patients were allocated to the low risk group with a negative predictive value of more than 98.8%. CONCLUSION: Applying the proposed models, more than ten percent of patients with suspected blood stream infection were identified having minimal risk for bacteraemia. Based on these data the application of this model as an automated decision support tool for physicians is conceivable leading to a potential increase in the cost-effectiveness of blood culture sampling. External prospective validation of the model's generalizability is needed for further appreciation of the usefulness of this tool.