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.

Una cianosis grave con saturación de oxígeno casi normal debe sugerir una intoxicación metahemoglobinizante / Severe cyanosis with almost normal oxygen saturation: suspected poisoning from methemoglobinemia-inducing agents

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Individual outcome prediction models for patients with COVID-19 based on their first day of admission to the intensive care unit.

BACKGROUND: Currently, good prognosis and management of critically ill patients with COVID-19 are crucial for developing disease management guidelines and providing a viable healthcare system. We aimed to propose individual outcome prediction models based on binary logistic regression (BLR) and artificial neural network (ANN) analyses of data collected in the first 24 h of intensive care unit (ICU) admission for patients with COVID-19 infection. We also analysed different variables for ICU patients who survived and those who died. METHODS: Data from 326 critically ill patients with COVID-19 were collected. Data were captured on laboratory variables, demographics, comorbidities, symptoms and hospital stay related information. These data were compared with patient outcomes (survivor and non-survivor patients). BLR was assessed using the Wald Forward Stepwise method, and the ANN model was constructed using multilayer perceptron architecture. RESULTS: The area under the receiver operating characteristic curve of the ANN model was significantly larger than the BLR model (0.917 vs 0.810; p < 0.001) for predicting individual outcomes. In addition, ANN model presented similar negative predictive value than the BLR model (95.9% vs 94.8%). Variables such as age, pH, potassium ion, partial pressure of oxygen, and chloride were present in both models and they were significant predictors of death in COVID-19 patients. CONCLUSIONS: Our study could provide helpful information for other hospitals to develop their own individual outcome prediction models based, mainly, on laboratory variables. Furthermore, it offers valuable information on which variables could predict a fatal outcome for ICU patients with COVID-19.

Balón de contrapulsación intraaórtico en la miocardiopatía por cocaína / Balloon counterpulsation in cocaine induced cardiomyopathy

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