Retrospective study that aims to develop and validate a mortality prognostic model with variables included in the RETRAUCI project. RETRAUCI is an observational, prospective, and multicentre nationwide registry that currently includes 52 ICUs in Spain. It has the endorsement of the Neurointensive Care and Trauma Working Group of the Spanish Society of Intensive Care Medicine (SEMICYUC) and currently operates in a web-based electronic format (www.retrauci.org). The records for the years 2015–2019 were used. To achieve internal validation, the total records were divided into two sets: derivation set (2015–2017) and validation set (2018–2019). It is a study with complete-case analysis. The development of the models was carried out following the recommendations established in the Transparency Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) initiative (10).
Ethics Committee approval for the registry was obtained (Hospital Universitario 12 de Octubre, Madrid: 12/209). Due to the retrospective analysis of de-identified collected data, informed consent was not obtained for this study.
Patients And Variables Included
Patients were managed according to the Advanced Trauma Life Support principles. Data on epidemiology, acute management in the pre-hospital and in-hospital settings, type and severity of injury, resources utilisation, complications and outcomes were recorded. We only excluded patients with missing data about in-hospital mortality. For the development of a specific trauma ICU score, we used as candidate variables to be associated with mortality those available in RETRAUCI that could be collected in the first 24 hours after admission to the ICU. The variables entered were then analysed according to different categories:
Patient-related variables included sex, age and prior antiplatelet/anticoagulant treatment. Sex was treated as a dichotomic variable (male/female), age was distributed in four categories (less than 50 years, 50 to 65 years, 66 to 75 years and older than 75 years) and if the patient was on chronic treatment with antiplatelets or anticoagulants he/she was considered to have prior coagulation alteration (PRECOAG) (11).
Pre-hospital care variables included pre-hospital medical attention, pre-hospital intubation and mechanism of trauma, which differentiates penetrating vs. non-penetrating types. Additionally, we coded as a high-risk mechanism trauma those mechanisms with associated mortality higher than 20%. This category included gunshot wounds, pedestrian falls, accidental falls, suicidal precipitation, and those considered as unknown mechanism.
Physiological variables: pupillary size and reactivity (normal, unilateral mydriasis and bilateral mydriasis), and score of the Glasgow Coma Scale (absolute score and percentage of patients with ≤ 8 points).
Anatomical variables describing the severity of injuries according to the Abbreviated Injury Scale (AIS) were considered. The AIS ranges from 0 to 6, with 0 indicating no involvement and 6 indicating maximum involvement (12). A major organic involvement was considered with a score of 3 or higher (MAIS) in any of the following 6 anatomical areas: head (MAIS-Head), thorax (MAIS-Thorax), abdomen (MAIS-Abdomen), upper extremity (MAIS-Ext Upper), lower extremity (MAIS-Ext Lower) and external and thermal injuries (MAIS-External).
Organ failure-related variables were also considered: haemodynamic failure indicated by systolic blood pressure lower than 90 mmHg requiring the administration of volume, blood products, and vasoactive support; respiratory failure, indicated by pO2/FiO2 below 200; and coagulopathy, indicated by the prolongation of prothrombin and activated partial thromboplastin times in > 1.5 times the control or by levels of fibrinogen < 150 mg/dL or thrombocytopenia < 100,000/µL in the determination of the first 24 hours (13).
Treatment variables included the need of mechanical ventilation and the activation of the massive transfusion protocol because of a massive haemorrhage (14).
Outcome Definition
The outcome variable was defined as 30-day mortality after trauma. Patients who were discharged from the hospital alive before 30 days after trauma were assumed to have survived for at least 30 days.
The ICU length of stay (LOS) was also collected to compare the derivation and validation sets. The probability of death (1-probability of survival) according to the TRISS score was used as a comparison model (15).
Statistical analysis
The sample size calculation helped us to verify that there were enough records for the development and validation of the model. For each possible factor 10 deaths are needed. With a mortality of 12% and 20 variables as potential risk factors, at least (20 x 10 / 0.12) = 1666 records are needed for the derivation and validation sets (16).
Categorical variables were described as percentage and continuous variables as median (interquartile range), as they did not follow a normal distribution (Kolmogorov-Smirnov test). For the comparison between the groups Derivation-Validation and Survivors-Non-survivors, the Mann-Whitney test was used for continuous variables and the chi-square test for categorical variables. A p < 0.05 was considered statistically significant.
In the derivation set, a multivariable logistic regression model was used to determine predictors for 30-day mortality. We use the LASSO (least absolute shrinkage and selection operator) logistic regression algorithm in order to obtain a subset of predictor variables from the 20 candidate variables. This subset of predictor variables was used to carry out the logistic regression model and the Odds ratios with their 95% confidence intervals and the β-coefficients of each factor were calculated.
Internal validity and adjustment for overfitting of the model was performed with a bootstrapping procedure. One thousand bootstrap samples were drawn from the derivation set. A shrinkage factor that multiplied the β-coefficients of the predictive factors and made them adjusted was calculated. These β-coefficients were used to calculate the individual probability of death in the derivation and validation sets.
A simple score (RETRASCORE) was developed based on predictors that were associated with 30-day mortality in the multivariate analysis. Score points were defined multiplying the regressions β-coefficients by 2 and rounded them to the nearest integer. The sum of the points is the value of the final score.
The performance of the models, Logistic Regression (LR), TRISS and RETRASCORE were determined in the derivation and validation sets. Global measure was used with the Brier score, discrimination measures using the area under the ROC curve (AUROC) with 95% CI, and calibration with calibration plots (the mean of the predicted probabilities was computed for each risk decile) and the calculation of the values of the fitted lines with intercept and slope with 95% CI (17).
The calculations were performed using STATA software, version 15.0 (Stata Corporation, College Station, Texas, USA) and R statistics 4.0.3 with the “glmnet” package (R Foundation for Statistical Computing, Vienna, Austria) (18).