Epidemiological characteristics and factors influencing hospitalization burden among trauma patients: a retrospective analysis

Advancing pain management for extremity trauma: the evolution of ultrasound-guided nerve blocks for patients in the supine position in trauma centers

PURPOSE

Trauma, particularly extremity trauma, poses a considerable challenge in healthcare, especially among young adults. Given the severity of patient pain and the risks associated with excessive opioid use, managing acute pain in trauma centers is inherently complex. This study aims to investigate the application and benefits of ultrasound-guided nerve blocks for early pain management in patients with extremity trauma positioned supine.

METHODS

A comprehensive literature review was conducted to assess the effectiveness and advantages of ultrasound-guided peripheral nerve blocks in the acute pain management of extremity trauma patients in the supine position. Special emphasis was placed on evaluating the selection criteria, indications, contraindications, adverse reactions, and potential complications associated with these nerve block techniques.

RESULTS

Ultrasound-guided nerve blocks represent a safer and more precise option for managing pain in extremity trauma patients placed in the supine position. These techniques offer significant advantages in terms of reducing healthcare expenses, diminishing reliance on opioid medications, and mitigating opioid-related complications. Nonetheless, challenges may arise due to the necessity for patient cooperation during specific nerve block procedures.

CONCLUSION

Ultrasound-guided nerve blocks present a promising avenue for early pain management in extremity trauma patients positioned supinely. Their implementation can lead to improved patient outcomes by alleviating pain severity, reducing opioid consumption, and cutting down healthcare costs. Further research and clinical integration of these techniques is imperative to enhance pain management protocols in trauma centers.

Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma

Purpose

Earlier research has identified several potentially predictive features including biomarkers associated with trauma, which can be used to assess the risk for harmful outcomes of polytraumatized patients. These features encompass various aspects such as the nature and severity of the injury, accompanying health conditions, immune and inflammatory markers, and blood parameters linked to organ functioning, however their applicability is limited. Numerous indicators relevant to the patients` outcome are routinely gathered in the intensive care unit (ICU) and recorded in electronic medical records, rendering them suitable predictors for risk assessment of polytraumatized patients.

Methods

317 polytraumatized patients were included, and the influence of 29 clinical and biological features on the complication patterns for systemic inflammatory response syndrome (SIRS), pneumonia and sepsis were analyzed with a machine learning workflow including clustering, classification and explainability using SHapley Additive exPlanations (SHAP) values. The predictive ability of the analyzed features within three days after admission to the hospital were compared based on patient-specific outcomes using receiver-operating characteristics.

Results

A correlation and clustering analysis revealed that distinct patterns of injury and biomarker patterns were observed for the major complication classes. A k-means clustering suggested four different clusters based on the major complications SIRS, pneumonia and sepsis as well as a patient subgroup that developed no complications. For classification of the outcome groups with no complications, pneumonia and sepsis based on boosting ensemble classification, 90% were correctly classified as low-risk group (no complications). For the high-risk groups associated with development of pneumonia and sepsis, 80% of the patients were correctly identified. The explainability analysis with SHAP values identified the top-ranking features that had the largest impact on the development of adverse outcome patterns. For both investigated risk scenarios (infectious complications and long ICU stay) the most important features are SOFA score, Glasgow Coma Scale, lactate, GGT and hemoglobin blood concentration.

Conclusion

The machine learning-based identification of prognostic feature patterns in patients with traumatic injuries may improve tailoring personalized treatment modalities to mitigate the adverse outcomes in high-risk patient clusters.