Development of a new score for early mortality prediction in trauma ICU patients: RETRASCORE.

BACKGROUND: Severity scores are commonly used for outcome adjustment and benchmarking of trauma care provided. No specific models performed only with critically ill patients are available. Our objective was to develop a new score for early mortality prediction in trauma ICU patients. METHODS: This is a retrospective study using the Spanish Trauma ICU registry (RETRAUCI) 2015-2019. Patients were divided and analysed into the derivation (2015-2017) and validation sets (2018-2019). We used as candidate variables to be associated with mortality those available in RETRAUCI that could be collected in the first 24 h after ICU admission. Using logistic regression methodology, a simple score (RETRASCORE) was created with points assigned to each selected variable. The performance of the model was carried out according to global measures, discrimination and calibration. RESULTS: The analysis included 9465 patients: derivation set 5976 and validation set 3489. Thirty-day mortality was 12.2%. The predicted probability of 30-day mortality was determined by the following equation: 1/(1 + exp (- y)), where y = 0.598 (Age 50-65) + 1.239 (Age 66-75) + 2.198 (Age > 75) + 0.349 (PRECOAG) + 0.336 (Pre-hospital intubation) + 0.662 (High-risk mechanism) + 0.950 (unilateral mydriasis) + 3.217 (bilateral mydriasis) + 0.841 (Glasgow ≤ 8) + 0.495 (MAIS-Head) - 0.271 (MAIS-Thorax) + 1.148 (Haemodynamic failure) + 0.708 (Respiratory failure) + 0.567 (Coagulopathy) + 0.580 (Mechanical ventilation) + 0.452 (Massive haemorrhage) - 5.432. The AUROC was 0.913 (0.903-0.923) in the derivation set and 0.929 (0.918-0.940) in the validation set. CONCLUSIONS: The newly developed RETRASCORE is an early, easy-to-calculate and specific score to predict in-hospital mortality in trauma ICU patients. Although it has achieved adequate internal validation, it must be externally validated.

Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study.

BACKGROUND: Interest in models for calculating the risk of death in traumatic patients admitted to ICUs remains high. These models use variables derived from the deviation of physiological parameters and/or the severity of anatomical lesions with respect to the affected body areas. Our objective is to create different predictive models of the mortality of critically traumatic patients using machine learning techniques. METHODS: We used 9625 records from the RETRAUCI database (National Trauma Registry of 52 Spanish ICUs in the period of 2015-2019). Hospital mortality was 12.6%. Data on demographic variables, affected anatomical areas and physiological repercussions were used. The Weka Platform was used, along with a ten-fold cross-validation for the construction of nine supervised algorithms: logistic regression binary (LR), neural network (NN), sequential minimal optimization (SMO), classification rules (JRip), classification trees (CT), Bayesian networks (BN), adaptive boosting (ADABOOST), bootstrap aggregating (BAGGING) and random forest (RFOREST). The performance of the models was evaluated by accuracy, specificity, precision, recall, F-measure, and AUC. RESULTS: In all algorithms, the most important factors are those associated with traumatic brain injury (TBI) and organic failures. The LR finds thorax and limb injuries as independent protective factors of mortality. The CT generates 24 decision rules and uses those related to TBI as the first variables (range 2.0-81.6%). The JRip detects the eight rules with the highest risk of mortality (65.0-94.1%). The NN model uses a hidden layer of ten nodes, which requires 200 weights for its interpretation. The BN find the relationships between the different factors that identify different patient profiles. Models with the ensemble methodology (ADABOOST, BAGGING and RandomForest) do not have greater performance. All models obtain high values ​​in accuracy, specificity, and AUC, but obtain lower values ​​in recall. The greatest precision is achieved by the SMO model, and the BN obtains the best recall, F-measure, and AUC. CONCLUSION: Machine learning techniques are useful for creating mortality classification models in critically traumatic patients. With clinical interpretation, the algorithms establish different patient profiles according to the relationship between the variables used, determine groups of patients with different evolutions, and alert clinicians to the presence of rules that indicate the greatest severity.

Dermatological Manifestations in the Intensive Care Unit: A Practical Approach.

Dermatological problems are not usually related to intensive medicine because they are considered to have a low impact on the evolution of critical patients. Despite this, dermatological manifestations (DMs) are relatively frequent in critically ill patients. In rare cases, DMs will be the main diagnosis and will require intensive treatment due to acute skin failure. In contrast, DMs can be a reflection of underlying systemic diseases, and their identification may be key to their diagnosis. On other occasions, DMs are lesions that appear in the evolution of critical patients and are due to factors derived from the stay or intensive treatment. Lastly, DMs can accompany patients and must be taken into account in the comprehensive pathology management. Several factors must be considered when addressing DMs: on the one hand, the moment of appearance, morphology, location, and associated treatment and, on the other hand, aetiopathogenesis and classification of the cutaneous lesion. DMs can be classified into 4 groups: life-threatening DMs (uncommon but compromise the patient's life); DMs associated with systemic diseases where skin lesions accompany the pathology that requires admission to the intensive care unit (ICU); DMs secondary to the management of the critical patient that considers the cutaneous manifestations that appear in the evolution mainly of infectious or allergic origin; and DMs previously present in the patient and unrelated to the critical process. This review provides a characterization of DMs in ICU patients to establish a better identification and classification and to understand their interrelation with critical illnesses.

A prospective pilot study using metabolomics discloses specific fatty acid, catecholamine and tryptophan metabolic pathways as possible predictors for a negative outcome after severe trauma.

BACKGROUND: We wanted to define metabolomic patterns in plasma to predict a negative outcome in severe trauma patients. METHODS: A prospective pilot study was designed to evaluate plasma metabolomic patterns, established by liquid chromatography coupled to mass spectrometry, in patients allocated to an intensive care unit (in the University Hospital Arnau de Vilanova, Lleida, Spain) in the first hours after a severe trauma (n = 48). Univariate and multivariate statistics were employed to establish potential predictors of mortality. RESULTS: Plasma of patients non surviving to trauma (n = 5) exhibited a discriminating metabolomic pattern, involving basically metabolites belonging to fatty acid and catecholamine synthesis as well as tryptophan degradation pathways. Thus, concentration of several metabolites exhibited an area under the receiver operating curve (ROC) higher than 0.84, including 3-indolelactic acid, hydroxyisovaleric acid, phenylethanolamine, cortisol, epinephrine and myristic acid. Multivariate binary regression logistic revealed that patients with higher myristic acid concentrations had a non-survival odds ratio of 2.1 (CI 95% 1.1-3.9). CONCLUSIONS: Specific fatty acids, catecholamine synthesis and tryptophan degradation pathways could be implicated in a negative outcome after trauma. The metabolomic study of severe trauma patients could be helpful for biomarker proposal.

Location-dependent effects of trauma on oxidative stress in humans.

Though circulating antioxidant capacity in plasma is homeostatically regulated, it is not known whether acute stressors (i.e. trauma) affecting different anatomical locations could have quantitatively different impacts. For this reason, we evaluated the relationship between the anatomical location of trauma and plasma total antioxidant capacity (TAC) in a prospective study, where the anatomical locations of trauma in polytraumatic patients (n = 66) were categorized as primary affecting the brain -traumatic brain injury (TBI)-, thorax, abdomen and pelvis or extremities. We measured the following: plasma TAC by 2 independent methods, the contribution of selected antioxidant molecules (uric acid, bilirubin and albumin) to these values and changes after 1 week of progression. Surprisingly, TBI lowered TAC (919 ± 335 µM Trolox equivalents (TE)) in comparison with other groups (thoracic trauma 1187 ± 270 µM TE; extremities 1025 ± 276 µM TE; p = 0.004). The latter 2 presented higher hypoxia (PaO2/FiO2 272 ± 87 mmHg) and hemodynamic instability (inotrope use required in 54.5%) as well. Temporal changes in TAC are also dependent on anatomical location, as thoracic and extremity trauma patients' TAC values decreased (1187 ± 270 to 1045 ± 263 µM TE; 1025 ± 276 to 918 ± 331 µM TE) after 1 week (p < 0.01), while in TBI these values increased (919 ± 335 to 961 ± 465 µM TE). Our results show that the response of plasma antioxidant capacity in trauma patients is strongly dependent on time after trauma and location, with TBI failing to induce such a response.

Classification of dermatological disorders in critical care patients: a prospective observational study.

PURPOSE: The objective of this study was to identify dermatological disorders detected in the intensive care unit (ICU), to analyze their specific characteristics, and to define a useful classification for intensive care physicians. MATERIALS AND METHODS: This was a prospective, observational study over a 3-year period (2006-2009) in a mixed ICU. This included all patients presenting with dermatological disorders that were detected at the time of ICU admission or developed along the ICU stay. We recorded the specific characteristics of the disorders and its evolution and treatment, which enabled us to classify the different observed conditions. As general variables, we analyzed demographic factors, the principal diagnosis, ICU procedures, the severity score (Acute Physiology and Chronic Health Evaluation II), length of stay, and mortality. RESULTS: One hundred thirty-three patients showed at least one dermatological disorder (9.3%) and were classified into (1) preexisting dermatological disorders, (2) life-threatening dermatologic disorders, (3) systemic dermatological disorders, (4) infectious dermatological disorders, (5) reactive dermatological disorders, and (6) others. CONCLUSIONS: Dermatological disorders are a frequent problem in the ICU, and their recognition is key to set up an appropriate care plan. We propose a classification and description of the different types of dermatological disorders that are most commonly found in ICUs.

Time spent in the emergency department and mortality rates in severely injured patients admitted to the intensive care unit: An observational study.

PURPOSE: The aim of this study was to identify the determinants of a shorter emergency department time (EDt) in patients with severe trauma (STPs) admitted to the intensive care unit and determine whether EDt influences mortality. PATIENTS AND METHODS: A prospective observational study of STPs (2005-2007) was conducted. With the variables available from the ED, 2 multiple logistic regression models (MLRM) were created: one for the factors associated with EDt less than or equal to median and the other with mortality. RESULTS: A total of 243 patients were included. The mean age was 43 years; 76% were male. The overall mortality rate was 20%. The median EDt was 120 minutes. The independent factors that were associated with the MLRM for an EDt of 120 minutes or less included age less than 60 years, mechanical ventilation, severe traumatic brain injury, and a trauma and injury severity score of 20 or higher. The MLRM for mortality was age greater than 60 years, mechanical ventilation, traumatic brain injury and shock. An EDt of 120 minutes or less was associated with an increased risk of death in the univariate analysis but not in the MLRM. CONCLUSIONS: Patients in the ED with indicators of high trauma severity have a reduced EDt but a higher mortality rate. Advanced age increases both mortality and EDt. With the factors included in the model, EDt was not an independent factor for mortality in STPs.