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.

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.

¿Podrían ayudarnos los algoritmos de machine learning en la predicción de hemorragia masiva a nivel prehospitalario? / Could machine learning algorithms help us in predicting massive hemorrhage at the prehospital level?

Objetivo: Comparación de la capacidad predictiva de diferentes algoritmos de machine learning (AML) respecto a escalas tradicionales de predicción de hemorragia masiva en pacientes con enfermedad traumática grave (ETG). Diseño: Sobre una base de datos de una cohorte retrospectiva con variables clínicas prehospitalarias y de resultado de hemorragia masiva se realizó un tratamiento de la base de datos para poder aplicar los AML, obteniéndose un conjunto total de 473 pacientes (80% entrenamiento, 20% validación). Para la modelización se realizó imputación proporcional y validación cruzada. El poder predictivo se evaluó con la métrica ROC y la importancia de las variables mediante los valores Shapley. Ámbito: Atención extrahospitalaria del paciente con ETG. Pacientes: Pacientes con ETG atendidos en el medio extrahospitalario por un servicio médico extrahospitalario desde enero de 2010 hasta diciembre de 2015 y trasladados a un centro de trauma en Madrid. Intervenciones: Ninguna. Variables de interés principales: Obtención y comparación de la métrica ROC de 4 AML: random forest, support vector machine, gradient boosting machine y neural network con los resultados obtenidos con escalas tradicionales de predicción. Resultados: Los diferentes AML alcanzaron valores ROC superiores al 0,85, teniendo medianas cercanas a 0,98. No encontramos diferencias significativas entre los AML. Cada AML ofrece un conjunto de variables diferentes, pero con predominancia de las variables hemodinámicas, de reanimación y de deterioro neurológico. Conclusiones: Los AML podrían superar a las escalas tradicionales de predicción en la predicción de hemorragia masiva. (AU)

Objective: Comparison of the predictive ability of various machine learning algorithms (MLA) versus traditional prediction scales for massive hemorrhage in patients with severe traumatic injury (ETG). Design: On a database of a retrospective cohort with prehospital clinical variables and massive hemorrhage outcome, a treatment of the database was performed to be able to apply the different MLA, obtaining a total set of 473 patients (80% training and 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 ETG. Participants: Patients with ETG treated out-of-hospital by a prehospital 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 ROC curve metric of 4 MLAs: random forest, support vector machine, gradient boosting machine and neural network with the results obtained with traditional prediction scales. Results: The different MLA reached ROC values higher than 0.85, having medians close to 0.98. We found no significant differences between MLAs. Each MLA 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 massive hemorrhage by outperforming traditional prediction scales. (AU)

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.

Retos de hoy en Medicina Intensiva: diversidad, equidad e inclusión. Posicionamiento de género de la Sociedad Española de Medicina Intensiva, Crítica y Unidades Coronarias (SEMICYUC) / Challenges in intensive medicine: Diversity, equity and inclusion. Gender statement of the Spanish Society of Intensive and Critical Medicine and Coronary Units (SEMICYUC)

El género es un determinante social que impacta en el ámbito de la salud y genera desigualdades a todos los niveles; repercute en los pacientes y en la patología crítica, en los profesionales y en el desarrollo de la carrera profesional y las sociedades científicas, desde una perspectiva de justicia social. Todas las sociedades científicas internacionales de medicina intensiva comprometidas en aportar una perspectiva de género coinciden en la necesidad de un compromiso formal por parte de las instituciones. La Sociedad de Medicina Intensiva y Unidades Coronarias (SEMICYUC) se compromete a velar por la equidad, la inclusión y la representatividad de sus profesionales y combatir la brecha de género en el ámbito de la medicina intensiva (AU)

Gender is a social determinant that impacts on health and generates inequities at all levels; it has an impact on patients and critical pathology, professionals and professional career development, and scientific societies, from a social justice perspective. All the international scientific societies of Intensive Care Medicine committed to contributing a gender perspective agree on the need for a formal positioning by the institutions. The Society of Intensive Medicine and Coronary Units (SEMICYUC) is committed to ensuring the equity, inclusion and representativeness of its professionals and combating the gender gap in the field of Intensive Medicine (AU)

Challenges in intensive medicine: Diversity, equity and inclusion. Gender statement of the Spanish Society of Intensive and Critical Medicine and Coronary Units (SEMICYUC).

Gender is a social determinant that impacts health and generates inequalities at all levels. It has impacts patients and critical conditions, health professionals and professional career development, and scientific societies from a perspective of social justice. All the International scientific societies of Intensive Care Medicine committed to contributing a gender perspective agree on the institutional need for achieving a formal positioning standpoint. The Spanish Society of Intensive and Critical Medicine and Coronary Units (SEMICYUC) is committed to ensuring the equality, inclusion and representativeness of its health professionals to fight the existing gender gap in the field of Intensive Medicine.

Programa de Mentoría: acompañando tus primeros pasos hacia la excelencia científica / Mentoring Programme: Guiding your first steps towards scientific excellence

El primer Programa de Mentoría de SEMICYUC tiene como objetivo apoyar la carrera investigadora de los miembros más jóvenes de la Sociedad. Como beneficios añadidos está la adquisición de nuevas capacidades de investigación y/o clínicas, incrementar la capacidad de reflexión y fomentar el desarrollo de la próxima generación de líderes en la investigación. Este proyecto no sería posible sin el equipo excepcional de mentores o expertos investigadores dispuestos a emprender el viaje con los jóvenes aprendices. El presente artículo expone las bases de dicho programa, además de proponer futuros cambios en haz de una mejora continua (AU)

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 (AU)

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.

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.

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.

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

Objetivos. 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. Métodos. 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. (AU)

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. 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. (AU)

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.

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.

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.