Associated Risk Factors and Impact in Clinical Outcomes of Multiorgan Failure in Patients with TBI.

BACKGROUND: Individual extracerebral organ dysfunction is common after severe traumatic brain injury (TBI) and impacts outcomes. However, multiorgan failure (MOF) has received less attention in patients with isolated TBI. Our objective was to analyze the risk factors associated with the development of MOF and its impact in clinical outcomes in patients with TBI. METHODS: This was an observational, prospective, multicenter study using data from a nationwide registry that currently includes 52 intensive care units (ICUs) in Spain (RETRAUCI). Isolated significant TBI was defined as Abbreviated Injury Scale (AIS) ≥ 3 in the head area with no AIS ≥ 3 in any other anatomical area. Multiorgan failure was defined using the Sequential-related Organ Failure Assessment as the alteration of two or more organs with a score of ≥ 3. We analyzed the contribution of MOF to crude and adjusted mortality (age and AIS head) by using logistic regression analysis. A multiple logistic regression analysis was performed to analyze the risk factors associated with the development of MOF in patients with isolated TBI. RESULTS: A total of 9790 patients with trauma were admitted to the participating ICUs. Of them, 2964 (30.2%) had AIS head ≥ 3 and no AIS ≥ 3 in any other anatomical area, and these patients constituted the study cohort. Mean age was 54.7 (19.5) years, 76% of patients were men, and ground-level falls were the main mechanism of injury (49.1%). In-hospital mortality was 22.2%. Up to 185 patients with TBI (6.2%) developed MOF during their ICU stay. Crude and adjusted (age and AIS head) mortality was higher in patients who developed MOF (odds ratio 6.28 [95% confidence interval 4.58-8.60] and odds ratio 5.20 [95% confidence interval 3.53-7.45]), respectively. The logistic regression analysis showed that age, hemodynamic instability, the need of packed red blood cells concentrates in the initial 24 h, the severity of brain injury, and the need for invasive neuromonitoring were significantly associated with MOF development. CONCLUSIONS: MOF occurred in 6.2% of patients with TBI admitted to the ICU and was associated with increased mortality. MOF was associated with age, hemodynamic instability, the need of packed red blood cells concentrates in the initial 24 h, the severity of brain injury, and the need for invasive neuromonitoring.

Chronic critical illness in polytrauma. Results of the Spanish trauma in ICU registry.

PURPOSE: Chronic critical illness after trauma injury has not been fully evaluated, and there is little evidence in this regard. We aim to describe the prevalence and risk factors of chronic critical illness (CCI) in trauma patients admitted to the intensive care unit. MATERIAL AND METHODS: Retrospective observational multicenter study (Spanish Registry of Trauma in ICU (RETRAUCI)). Period March 2015 to December 2019. Trauma patients admitted to the ICU, who survived the first 48 h, were included. Chronic critical illness (CCI) was considered as the need for mechanical ventilation for a period greater than 14 days and/or placement of a tracheostomy. The main outcomes measures were prevalence and risk factors of CCI after trauma. RESULTS: 1290/9213 (14%) patients developed CCI. These patients were older (51.2 ± 19.4 vs 49 ± 18.9); p < .01) and predominantly male (79.9%). They presented a higher proportion of infectious complications (81.3% vs 12.7%; p < .01) and multiple organ dysfunction syndrome (MODS) (27.02% vs 5.19%; p < .01). CCI patients required longer stays in the ICU and had higher ICU and overall in-hospital mortality. Age, injury severity score, head injury, infectious complications, and development of MODS were independent predictors of CCI. CONCLUSION: CCI in trauma is a prevalent entity in our series. Early identification could facilitate specific interventions to change the trajectory of this process.

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.

Current perspectives on the use of artificial intelligence in critical patient safety.

Intensive Care Units (ICUs) have undergone enhancements in patient safety, and artificial intelligence (AI) emerges as a disruptive technology offering novel opportunities. While the published evidence is limited and presents methodological issues, certain areas show promise, such as decision support systems, detection of adverse events, and prescription error identification. The application of AI in safety may pursue predictive or diagnostic objectives. Implementing AI-based systems necessitates procedures to ensure secure assistance, addressing challenges including trust in such systems, biases, data quality, scalability, and ethical and confidentiality considerations. The development and application of AI demand thorough testing, encompassing retrospective data assessments, real-time validation with prospective cohorts, and efficacy demonstration in clinical trials. Algorithmic transparency and explainability are essential, with active involvement of clinical professionals being crucial in the implementation process.

Effects of PEEP on intracranial pressure in patients with acute brain injury: An observational, prospective and multicenter study.

OBJECTIVE: To analyze the impact of positive end-expiratory pressure (PEEP) changes on intracranial pressure (ICP) dynamics in patients with acute brain injury (ABI). DESIGN: Observational, prospective and multicenter study (PEEP-PIC study). SETTING: Seventeen intensive care units in Spain. PATIENTS: Neurocritically ill patients who underwent invasive neuromonitorization from November 2017 to June 2018. INTERVENTIONS: Baseline ventilatory, hemodynamic and neuromonitoring variables were collected immediately before PEEP changes and during the following 30 min. MAIN VARIABLES OF INTEREST: PEEP and ICP changes. RESULTS: One-hundred and nine patients were included. Mean age was 52.68 (15.34) years, male 71 (65.13%). Traumatic brain injury was the cause of ABI in 54 (49.54%) patients. Length of mechanical ventilation was 16.52 (9.23) days. In-hospital mortality was 21.1%. PEEP increases (mean 6.24-9.10 cmH2O) resulted in ICP increase from 10.4 to 11.39 mmHg, P < .001, without changes in cerebral perfusion pressure (CPP) (P = .548). PEEP decreases (mean 8.96 to 6.53 cmH2O) resulted in ICP decrease from 10.5 to 9.62 mmHg (P = .052), without changes in CPP (P = .762). Significant correlations were established between the increase of ICP and the delta PEEP (R = 0.28, P < .001), delta driving pressure (R = 0.15, P = .038) and delta compliance (R = -0.14, P = .052). ICP increment was higher in patients with lower baseline ICP. CONCLUSIONS: PEEP changes were not associated with clinically relevant modifications in ICP values in ABI patients. The magnitude of the change in ICP after PEEP increase was correlated with the delta of PEEP, the delta driving pressure and the delta compliance.

External validation of the Glasgow Coma Scale-Pupils in patients with severe head injury. / Validación externa de la Escala de Coma de Glasgow con valoración pupilar en pacientes con traumatismo craneoencefálico grave.

OBJECTIVES: To compare the ability of the Glasgow Coma Scale (GCS) score, the GCS Pupils (GCS-P) score, and the Pupil Reactivity Score (PRS) to predict mortality in patients with severe head injury. MATERIAL AND METHODS: Retrospective analysis of all patients with severe head injury and initial GCS scores of 8 or lower on initial evaluation for whom records included pupil dilation information and clinical course after admission to intensive care units of participating hospitals. We assessed the ability of each of the 3 scores (GCS, GCS-P, and PRS) to predict mortality using discrimination analysis. Discrimination was estimated by calculating the areas under the receiver operating characteristic curves (AUC) and 95% CIs. RESULTS: A total of 1551 patients with severe head injury and pupil dilation records were studied. The mean age was 50 years, 1190 (76.7%) were males, and 592 (38.2%) died. No pupil dilation was observed in 905 patients (58.3%), 362 (23.3%) had unilateral mydriasis, and 284 (18.3%) had bilateral mydriasis. The GCS-P score was significantly better at predicting mortality, with an AUC of 0.77 (95% CI, 0.74-0.79), versus 0.69 (95% CI, 0.67-0.72) for the GCS, and 0.75 (95% CI, 0.72-0.77) for the PRS. As the GCS-P score decreased, mortality increased. CONCLUSION: The GCS-P was more useful than the GCS for predicting death after severe head injury.

OBJETIVO: Analizar la capacidad para predecir la mortalidad hospitalaria de la Escala de Coma de Glasgow con valoración pupilar (GCS-P) comparado con la Escala de Coma de Glasgow (GCS) y con la escala de reactividad pupilar (PRS) en pacientes con traumatismo craneoencefálico (TCE) grave. METODO: Análisis retrospectivo de cohortes de todos los pacientes con TCE, puntuación en la GCS # 8 en la atención inicial, datos de exploración pupilar inicial y del desenlace hospitalario ingresados en las unidades de cuidados intensivos participantes. Se determinó la capacidad predictiva de mortalidad de la GCS, PRS y la GCS-P mediante un análisis de discriminación. La discriminación se analizó empleando curvas operativas del receptor (COR), el área bajo la curva (ABC) y su intervalo de confianza del 95% (IC 95%). RESULTADOS: Se analizaron 1.551 pacientes con TCE grave y datos sobre exploración pupilar. La edad media fue de 50 años, 1.190 (76,7%) eran hombres, y hubo 592 (38,2%) defunciones. Hubo 905 (58,3%) pacientes sin alteraciones pupilares, 362 (23,3%) con midriasis unilateral y 284 (18,3%) pacientes con midriasis bilateral. El análisis del ABCCOR para predecir la mortalidad hospitalaria mostró de forma significativa una mejor capacidad predictiva del GCS-P con ABC = 0,77 (IC 95% 0,74-0,79) respecto al GCS con ABC = 0,69 (IC 95% 0,67-0,72). La reactividad pupilar mostró un ABC = 0,75 (IC 95% 0,72-0,77). Se observó un incremento de mortalidad con la disminución del GCS-P. CONCLUSIONES: La escala GCS-P presentó mejor rendimiento que la GCS para predecir mortalidad en el TCE grave.

Mentoring Programme: Guiding your first steps towards scientific excellence.

SEMICYUC's first Mentoring Programme aims to support the research careers of the Society's youngest members. Added benefits include acquiring new research and/or clinical skills, increasing the ability of critical thought, and fostering the development of the next generation of research leaders. This project would not be possible without the exceptional team of mentors or research experts willing to embark on the journey with the young trainees. This article sets out the foundations of such a programme and proposes future changes for continuous improvement.

Could machine learning algorithms help us predict massive bleeding at prehospital level?

OBJECTIVE: Comparison of the predictive ability of various machine learning algorithms (MLA) versus traditional prediction scales (TPS) for massive hemorrhage (MH) in patients with severe traumatic injury (STI). DESIGN: On a database of a retrospective cohort with prehospital clinical variables and MH outcome, a treatment of the database was performed to be able to apply the different AML, obtaining a total set of 473 patients (80% training, 20% validation). For modeling, proportional imputation and cross validation were performed. The predictive power was evaluated with the ROC metric and the importance of the variables using the Shapley values. SETTING: Out-of-hospital care of patients with STI. PARTICIPANTS: Patients with STI treated out-of-hospital by a out-of-hospital medical service from January 2010 to December 2015 and transferred to a trauma center in Madrid. INTERVENTIONS: None. MAIN VARIABLES OF INTEREST: Obtaining and comparing the "Receiver Operating Characteristic curve" (ROC curve) metric of four MLAs: "random forest" (RF), "vector support machine" (SVM), "gradient boosting machine" (GBM) and "neural network" (NN) with the results obtained with TPS. RESULTS: The different AML reached ROC values higher than 0.85, having medians close to 0.98. We found no significant differences between AMLs. Each AML offers a different set of more important variables with a predominance of hemodynamic, resuscitation variables and neurological impairment. CONCLUSIONS: MLA may be helpful in patients with HM by outperforming TPS.

Elevated monocyte distribution width in trauma: An early cellular biomarker of organ dysfunction.

INTRODUCTION: Traumatic injury elicits an inflammatory response such as the one occurring during systemic infection. Monocyte distribution width (MDW) has been found to distinguish sepsis in a pool of patients with suspected infection. We hypothesized that an elevated MDW in trauma patients would be associated with the development of multiple organ dysfunction syndrome (MODS) and an increased mortality. MATERIALS AND METHODS: Observational study in a dedicated trauma Intensive Care Unit (ICU) in Madrid during 2019-2020. Patients were classified according to their first MDW value on admission, as greater or lesser than 21 U. Clinical data was obtained and univariate and multivariate analysis were realized, as well as a test performance analysis. RESULTS: 354 patients were studied, with a median age of 46 years, 78% male. Half presented with severe trauma ISS > 15, mostly with a blunt mechanism of injury. A MDW ≥ 21 U on admission was found in 17% of cases. These patients were more likely to present with hemodynamic instability and MODS. They had a higher length of stay (3.8 vs 2 days) and higher mortality (21 vs 5%) compared to the low MDW group. These findings remained statistically significant in the multivariate analysis, with an OR 4.6 (IC 95% 1.7-12) for MODS and 3.1 (IC 95% 1.2-8.3) for mortality. CONCLUSIONS: In trauma patients, a MDW ≥ 21 U on admission was independently associated with a greater risk of MODS, a higher mortality and a higher length of stay. This biomarker could be useful in predicting severity in the initial evaluation of trauma patients.

Risk Factors Associated with Mortality in Severe Chest Trauma Patients Admitted to the ICU.

Our objective was to determine outcomes of severe chest trauma admitted to the ICU and the risk factors associated with mortality. An observational, prospective, and multicenter registry of trauma patients admitted to the participating ICUs (March 2015-December 2019) was utilized to collect the patient data that were analyzed. Severe chest trauma was defined as an Abbreviated Injury Scale (AIS) value of ≥3 in the thoracic area. Logistic regression analysis was used to evaluate the contribution of severe chest trauma to crude and adjusted ORs for mortality and to analyze the risk factors associated with mortality. Overall, 3821 patients (39%) presented severe chest trauma. The sample's characteristics were as follows: a mean age of 49.88 (19.21) years, male (77.6%), blunt trauma (93.9%), a mean ISS of 19.9 (11.6). Crude and adjusted (for age and ISS) ORs for mortality in severe chest trauma were 0.78 (0.68-0.89) and 0.43 (0.37-0.50) (p < 0.001), respectively. In-hospital mortality in the severe chest trauma patients without significant traumatic brain injury (TBI) was 5.63% and was 25.71% with associated significant TBI (p < 0.001). Age, the severity of injury (NISS and AIS-head), hemodynamic instability, prehospital intubation, acute kidney injury, and multiorgan failure were risk factors associated with mortality. The contribution of severe chest injury to the mortality of trauma patients admitted to the ICU was very low. Risk factors associated with mortality were identified.

Traumatic Brain Injury and Acute Kidney Injury-Outcomes and Associated Risk Factors.

Our objective was to analyze the contribution of acute kidney injury (AKI) to the mortality of isolated TBI patients and its associated risk factors. Observational, prospective and multicenter registry (RETRAUCI) methods were used, from March 2015 to December 2019. Isolated TBI was defined as abbreviated injury scale (AIS) ≥ 3 head with no additional score ≥ 3. A comparison of groups was conducted using the Wilcoxon test, chi-square test or Fisher's exact test, as appropriate. A multiple logistic regression analysis was conducted to analyze associated risk factors in the development of AKI. For the result, overall, 2964 (30.2%) had AIS head ≥ 3 with no other area with AIS ≥ 3. The mean age was 54.7 (SD 19.5) years, 76% were men, and the ground-level falls was 49.1%. The mean ISS was 18.4 (SD 8). The in-hospital mortality was 22.2%. Up to 310 patients (10.6%) developed AKI, which was associated with increased mortality (39% vs. 17%, adjusted OR 2.2). Associated risk factors (odds ratio (OR) (95% confidence interval)) were age (OR 1.02 (1.01-1.02)), hemodynamic instability (OR 2.87 to OR 5.83 (1.79-13.1)), rhabdomyolysis (OR 2.94 (1.69-5.11)), trauma-associated coagulopathy (OR 1.67 (1.05-2.66)) and transfusion of packed red-blood-cell concentrates (OR 1.76 (1.12-2.76)). In conclusion, AKI occurred in 10.6% of isolated TBI patients and was associated with increased mortality.

Dexamethasone for the treatment of traumatic brain injured patients with brain contusions and pericontusional edema: Study protocol for a prospective, randomized and double blind trial.

BACKGROUND: Traumatic brain injury (TBI) constitutes a leading cause of death and disability. Patients with TBI and cerebral contusions developing pericontusional edema are occasionally given dexamethasone on the belief that this edema is similar to that of tumors, in which the beneficial effect of dexamethasone has been demonstrated. METHODS: The DEXCON TBI trial is a multicenter, pragmatic, randomized, triple-blind, placebo controlled trial to quantify the effects of dexamethasone on the prognosis of TBI patients with brain contusions and pericontusional edema. Adult patients who fulfill the elegibility criteria will be randomized to dexamethasone/placebo in a short and descending course: 4 mg/6 h (2 days); 4 mg/8 hours (2 days); 2 mg/6 hours (2 days); 2 mg/8 hours (2 days); 1 mg/8 hours (2 days); 1 mg/12 hours (2 days). The primary outcome is the Glasgow Scale Outcome Extended (GOSE) performed 1 month and 6 months after TBI. Secondary outcomes are: number of episodes of neurological deterioration; symptoms associated with TBI; adverse events; volume of pericontusional edema before and after 12 days of treatment; results of the neuropsychological tests one month and 6 months after TBI. The main analysis will be on an "intention-to-treat" basis. Logistic regression will estimate the effect of dexamethasone/placebo on GOSE at one month and at 6 months, dichotomized in unfavorable outcome (GOSE 1-6) and favorable outcome (GOSE 7-8). Efficacy will also be analyzed using the 'sliding dichotomy'. An interim and safety analysis will be performed including patients recruited during the first year to calculate the conditional power. A study with 600 patients would have 80% power (2 sided alpha = 5%) to detect a 12% absolute increase (from 50% to 62%) in good recovery. DISCUSSION: This is a confirmative trial to elucidate the therapeutic efficacy of dexamethasone in a very specific group of TBI patients: patients with brain contusions and pericontusional edema. This trial could become an important milestone for TBI patients as nowadays there is no effective treatment in this type of patients. TRIAL REGISTRATION: eudraCT: 2019-004038-41; Clinical Trials.gov: NCT04303065.

Associated risk factors and outcomes of acute kidney injury in severe trauma: Results from the Spanish trauma ICU registry (RETRAUCI).

INTRODUCTION: Acute kidney injury (AKI) constitutes a common complication after severe trauma. Our objective was to analyse the associated risk factors and outcomes of AKI in a large, multicentre sample of trauma ICU patients. MATERIALS AND METHODS: Observational, prospective and multicentre nationwide registry (RETRAUCI). We included all patients admitted to the participating ICUs from November 2013 to May 2017. We analysed the impact of AKI evaluated by the Risk, Injury, Failure, Loss of kidney function and End-stage kidney disease (RIFLE) definition. Comparison of groups was performed using Wilcoxon test, Chi-Square Test or Fisher's exact test as appropriate. A multiple logistic regression analysis was performed to analyse associated factors to the development of AKI. Logistic regression was used to calculate AKI-related mortality. A P value<0.05 was considered significant. RESULTS: During the study period, 5882 trauma patients were admitted. Complete data were available for 5740 patients. Among them, 871 had AKI (15.17%), distributed by RIFLE R 458 (7.98%), RIFLE I 234 (4.08%) and RIFLE F 179 (3.12%). Associated risk factors were: age (OR 3.05), haemodynamic instability (OR 2.90 to OR 8.34 depending on the severity of hypotension), coagulopathy (OR 1.82), rhabdomyolysis (OR 4.67) and AIS abdomen (OR 1.54). AKI was associated with mortality (crude OR 1.93 (1.59-2.36)), even after adjusting by potential confounders (adjusted OR 1.40 (1.13-1.73)). CONCLUSION: In our large sample of trauma ICU patients we found an incidence of AKI of 15%, which was associated with an increased mortality.

Neuromonitoring in the severe traumatic brain injury. Spanish Trauma ICU Registry (RETRAUCI). / Neuromonitorización en el traumatismo craneoencefálico grave. Datos del Registro español de Trauma en UCI (RETRAUCI).

OBJECTIVES: To analyze the use of intracranial pressure (ICP) and cerebral oximetry monitoring in patients with severe traumatic brain injury (TBI) according to the Spanish Trauma ICU Registry (RETRAUCI). METHODS: We included TBI patients with Glasgow Coma Scale score ≤ 8. Hypotheses were tested using the Student-T or Wilcoxon tests (quantitative variables) and the Chi-square test (categorical variables). Multivariate analysis using logistic regression was performed to analyze the variables associated with the use of ICP monitoring. RESULTS: We analyzed 1463 patients. Age 49.1 years. Males 1130 (77.3%). Mechanism of injury: falls in 350 cases (23.9%). Injury Severity Score 27.9. Uni- or bilateral mydriasis was present in 39.3% of the patients. Neurosurgical intervention within 24hours was performed in 331 patients (22.7%). ICP was monitored in 635 patients (45.1%), pbtO2 in 122 patients (8.6%), SjVO2 in 19 patients (1.34%) and NIRS was used in 25 cases (1.77%). In the multivariate analysis, age, bilateral mydriasis at admission and previous use of antiplatelets or anticoagulants was inversely related with ICP monitoring. Severity of injury and the need of neurosurgical intervention increased the probability of ICP monitoring. CONCLUSIONS: Our study shows a picture of ICP monitoring in severe TBI patients in our environment. Use of cerebral oximetry techniques is very limited.

Predicting survival in geriatric trauma patients: A comparison between the TRISS methodology and the Geriatric Trauma Outcome Score. / Predicción de la supervivencia en pacientes traumáticos ancianos: comparación entre la metodología TRISS y el Geriatric Trauma Outcome Score.

INTRODUCTION: We compared the Geriatric Trauma Outcome Score (GTOS) with the probability of survival using the TRISS methodology (PS-TRISS) in geriatric severe trauma patients admitted to Intensive Care Units (ICU) participating in the Spanish trauma ICU registry (RETRAUCI). METHODS: Retrospective analysis from the RETRAUCI. Quantitative data were reported as median (Interquartile Range (IQR)), and categorical data as number (percentage). We analyzed the validity of the GTOS and PS-TRISS to predict survival. Discrimination was analyzed using receiver operating characteristics curves. Calibration was analyzed using the Hosmer-Lemeshow goodness-of-fit test. A P value <.05 was considered statistically significant. RESULTS: The cohort included 1417 patients aged ≥ 65 years. Median age was 75.5 (70.5-80.5), 1003 patients were male (68.2%) and median Injury Severity Score was 18 (13-25). Mechanical ventilation was required in 61%. Falls were the mechanism of injury in 659 patients (44.8%). In-hospital mortality rate was 18.2%. The areas under the curve were: PS-TRISS 0.69 (95%CI 0.66-0.73), and GTOS 0.66 (95%CI 0.62-0.70); P<.05. Both scores overestimated mortality in the upper range of predicted mortality. CONCLUSIONS: In our sample of geriatric severe trauma patients, the accuracy of GTOS was lower than the accuracy of the PS-TRISS to predict in-hospital survival. The calibration of both scores for the geriatric population was deficient.

Short- and long-term prognosis of critically-ill patients referred to the ICU from the Emergency Department of a tertiary hospital. / Pronóstico a corto y largo plazo de los pacientes críticos ingresados en la Unidad de Cuidados Intensivos desde el Servicio de Urgencias de un hospital terciario.

BACKGROUND AND OBJECTIVE: A frequent source of critically-ill patients admitted to the ICU is the Emergency Department. It is essential to analyse the short-term prognosis of these patients, but also their evolution after their discharge from the hospital, since this is one of the major concerns of these patients. The aim of this study is to describe the epidemiological characteristics of patients admitted to the ICU from the Emergency Department and to analyse their outcome. PATIENTS AND METHOD: This consisted of an observational prospective cohorts study which included 269 Emergency Department patients consecutively admitted to the ICU over an 18-month period. Factors associated with hospital mortality were presented as an odds ratio (OR) and factors associated with long-term mortality were presented as a hazard ratio (HR). A P-value lower than .05 was accepted as significant. The overall survival was analysed on the basis of the Kaplan-Meier curves. RESULTS: Hospital mortality was 15%, ICU complications where the variables with the greatest impact on short-term mortality: acute renal failure (OR 22.7) and respiratory distress syndrome (OR 51.2). After hospital discharge, the cumulative mortality at 12, 24 and 36 months was 6, 11 and 15%, respectively. The degree of functional dependence (HR 3.7), cancer (HR 3.4) and arrhythmias (HR 2.4) were factors related to long-term mortality. CONCLUSIONS: The short-term outcome of ICU patients is related to age and comorbidity, but more significantly to the characteristics of the acute illness. However, the long-term outcome is more closely associated with the patients' characteristics.