Intensive care medicine
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Intensive care medicine · Jan 2024
The predictive value of highly malignant EEG patterns after cardiac arrest: evaluation of the ERC-ESICM recommendations.
The 2021 guidelines endorsed by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) recommend using highly malignant electroencephalogram (EEG) patterns (HMEP; suppression or burst-suppression) at > 24 h after cardiac arrest (CA) in combination with at least one other concordant predictor to prognosticate poor neurological outcome. We evaluated the prognostic accuracy of HMEP in a large multicentre cohort and investigated the added value of absent EEG reactivity. ⋯ The specificity of the ERC-ESICM-recommended EEG patterns for predicting poor outcome after CA exceeds 90% but is lower than in previous studies, suggesting that large-scale implementation may reduce their accuracy. Combining HMEP with an unreactive EEG background significantly improved specificity. As in other prognostication studies, a self-fulfilling prophecy bias may have contributed to observed results.
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Intensive care medicine · Jan 2024
Randomized Controlled TrialPhase-3 trial of recombinant human alkaline phosphatase for patients with sepsis-associated acute kidney injury (REVIVAL).
Ilofotase alfa is a human recombinant alkaline phosphatase with reno-protective effects that showed improved survival and reduced Major Adverse Kidney Events by 90 days (MAKE90) in sepsis-associated acute kidney injury (SA-AKI) patients. REVIVAL, was a phase-3 trial conducted to confirm its efficacy and safety. ⋯ Among critically ill patients with SA-AKI, ilofotase alfa did not improve day 28 survival. There may, however, be reduced MAKE90 events. No safety concerns were identified.
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Intensive care medicine · Jan 2024
Prediction of post-traumatic stress disorder in family members of ICU patients: a machine learning approach.
Post-traumatic stress disorder (PTSD) can affect family members of patients admitted to the intensive care unit (ICU). Easily accessible patient's and relative's information may help develop accurate risk stratification tools to direct relatives at higher risk of PTSD toward appropriate management. ⋯ We propose a machine learning-based approach to predict PTSD in relatives of ICU patients at an individual level. In this model, PTSD is mostly influenced by non-modifiable factors.