Neurocritical care
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Pregabalin (PGB) is an effective adjunctive treatment for focal epilepsy and acts by binding to the alpha2-delta subunit of voltage-gated calcium channels to reduce excitatory neurotransmitter release. Limited data exist on its use in the neurocritical care setting, including cyclic seizures-a pattern of recurrent seizures occurring at nearly regular intervals. Although the mechanism underpinning cyclic seizures remains elusive, spreading excitation linked to spreading depolarizations may play a role in seizure recurrence and periodicity. PGB has been shown to increase spreading depolarization threshold; hence, we hypothesized that the magnitude of antiseizure effect from PGB is more pronounced in patients with cyclic versus noncyclic seizures in a critically ill cohort with recurrent seizures. ⋯ PGB was associated with a relative reduction in seizure burden in neurocritically ill patients with recurrent seizures, especially those with cyclic seizures, and may be considered in the therapeutic arsenal for refractory seizures. Whether this effect is mediated via modulation of spreading depolarization requires further study.
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Most trials in critical care have been neutral, in part because between-patient heterogeneity means not all patients respond identically to the same treatment. The Precision Care in Cardiac Arrest: Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (PRECICECAP) study will apply machine learning to high-resolution, multimodality data collected from patients resuscitated from out-of-hospital cardiac arrest. We aim to discover novel biomarker signatures to predict the optimal duration of therapeutic hypothermia and 90-day functional outcomes. In parallel, we are developing a freely available software platform for standardized curation of intensive care unit-acquired data for machine learning applications. ⋯ Cardiac arrest is a heterogeneous disease that causes substantial morbidity and mortality. PRECICECAP will advance the overarching goal of titrating personalized neurocritical care on the basis of robust measures of individual need and treatment responsiveness. The software platform we develop will be broadly applicable to hospital-based research after acute illness or injury.
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Respiratory support is required in 20-30% of patients with Guillain-Barré syndrome (GBS). We investigated clinical and biological risk factors for mechanical ventilation (MV) in northeast China through a retrospective GBS study. The Erasmus GBS Respiratory Insufficiency Score (EGRIS) is a prognostic model for MV in patients with GBS, and its usefulness has been validated in several countries but not in China. Therefore, we intended to validate the EGRIS model in our GBS cohort. ⋯ An elevated neutrophil-to-lymphocyte ratio at admission and a high EGRIS could serve as predictors for MV in our GBS cohort.
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Observational Study
Prognosis Predictions by Families, Physicians, and Nurses of Patients with Severe Acute Brain Injury: Agreement and Accuracy.
Effective shared decision-making relies on some degree of alignment between families and the medical team regarding a patient's likelihood of recovery. Patients with severe acute brain injury (SABI) are often unable to participate in decisions, and therefore family members make decisions on their behalf. The goal of this study was to evaluate agreement between prognostic predictions by families, physicians, and nurses of patients with SABI regarding their likelihood of regaining independence and to measure each group's prediction accuracy. ⋯ For patients with SABI, agreement in predictions between families, physicians, and nurses regarding likelihood of recovery is poor. Accuracy appears higher for physicians and nurses compared with families, with no significant difference between physicians and nurses.
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To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. ⋯ A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest.