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Severe trauma is one of the leading causes of morbidity, mortality, and disability worldwide. Currently, it is the primary cause of death among individuals under 45 years of age. This disease, considered a "silent pandemic," exhibits heterogeneous physiopathology and unequal geographic distribution in terms of the type of injuries. The prognosis of subjects who have suffered severe trauma is uncertain, especially in patients with traumatic brain injury.
The epidemiology of severe trauma has undergone changes in recent years due to the global aging of society, resulting in different populations with older ages and more associated comorbidities. These factors are frequently linked to the use of chronic treatments such as antiplatelet agents or anticoagulants, which could worsen traumatic hemorrhage-the leading preventable cause of death following severe trauma. Despite efforts for primary prevention, such as road safety campaigns and occupational risk prevention, the annual incidence of severe trauma cases worldwide remains high. Enhancing the management of trauma patients would significantly influence the final clinical outcomes.
Given the aforementioned, it is of vital importance to understand the local epidemiology of severe trauma for the development of clinical research. This constitutes an effective tool to investigate changes in clinical practices, improve prevention strategies, and determine the global burden of the disease.
The hypothesis of the IcuTrauma Project is to create a territorial Registry of adults with severe trauma admitted to the ICU to understand the local epidemiology in Tarragona (Spain). This initiative would facilitate new lines of clinical research aimed at improving outcomes and the quality of care for trauma patients.
Full description
Data will be extracted by twofold:
Hand-driven: extracted data from the medical reports by reviewing the clinical history system performed by the study investigators, who are part of the staff of the ICU involved in the care of trauma patients. This information was collected into a large-scale database and, once introduced, the data was checked and verified by two investigators (and principal investigator).
These manually extracted data included pre-hospital variables (such as the time of the injury, the mechanism of injury, first vital signs, pre-hospital procedures), in-hospital information (demographic's, primary survey of trauma in emergency department-ED, use of Extended Focused Assesment with Sonography in Trauma, complete laboratory results in ED), description of detailed anatomic injuries (Traumatic Brain Injury-TBI, thoracic trauma, abdominal trauma, Pelvic trauma, orthopedic injuries), severity scores (Acute Physiology And Chronic Health Evaluation-APACHE II, Sequential Organ Failure Assessment-SOFA score, Injury Severity Score-ISS, Abbreviated Injury Score-AIS, etc), resources during ICU admission (surgeries, transfusions, organ support, etc), complications (organ failures and nosocomial infections such as ventilator-associated pneumonia, catheter-related infection, etc), and clinical outcomes (ICU mortality, cause of death, in-hospital mortality, one-year survival, mechanical ventilation days, ICU and hospital length of stay, disability at ICU discharge and destination at discharge.
Automatic data: automated data collected with the Electronic Health System (Centricity Critical Care) provide solid and valid extraction of information. Automatic variables were checked and validated by the expert and specialized members of the ICU in data quality assessment and the management of data to ensure that recorded information was reliable. The accuracy, completeness and representativeness of automatic data was double checked when entered into the large-scale database for data quality by the principal investigator and a biotechnologist, who were responsible for the quality assurance plan that addressed data validation, monitoring and auditing (identifying inconsistencies, outliers or incorrect ranges for variables entered into the registry). The automated variables collected (not recorded manually) included demographic data, comorbidities and previous risk factors, dates (hospital and ICU admission, hospital and ICU discharge), laboratory tests during the first week of ICU admission, resources consumption during ICU stay, complications during ICU admission and outcomes.
Statistical plan: descriptive and analytical statistics using binary logistic regression (for association), survival analysis with Kaplan Meier analysis and Cox regression (for clinical outcomes), nonlinear association test such as CHAID decision analysis and restricted cubic splines, and matching learning analysis (for cluster or subgroups identification). These analyzes will be carried out using SPSS v24 and R software. Missing data values of automated variables will be handled using multiple imputation. Hand-driven data had negligible missing values.
Variable definitions (all definitions met the ATLS-Advanced Trauma Life Support criteria):
Intra-abdominal infection: peritonitis, abscess, positive surgical fluid.
Infection of open fractures: positive culture of the wound.
MDR (multidrug resistant) infection: includes any bacteria resistant to at least three antibiotic families.
Limitation of life support: withholding or withdrawal.
Causes of death: hemorrhagic shock, early MODS, late MODS, septic shock, refractory ICH (without response to applied measures), others.
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Data sourced from clinicaltrials.gov
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