Neurocritical care
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Review
Decompressive Craniectomy with or Without Dural Closure: Systematic Review and Meta-analysis.
Decompressive craniectomy is used to alleviate intracranial pressure in cases of traumatic brain injury and stroke by removing part of the skull to allow brain expansion. Traditionally, this procedure is followed by a watertight dural suture, although evidence supporting this method is not strong. This meta-analysis examines the feasibility of the open-dura (OD) approach versus the traditional closed-dura (CD) technique with watertight suturing. ⋯ Assessment of operation duration, however, demonstrated a significant difference between techniques, with a mean reduction of 52.50 min favoring the OD approach (mean difference - 52.50 [95% CI - 92.13 to - 12.87]; I2 = 96%). This study supports the viability of decompressive craniectomy without the conventional time-spending watertight duraplasty closure, exhibiting no differences in the rate of infections or CSF leaks. Furthermore, this approach has been associated with improved rates of complications and faster surgery, which are important aspects of this technique, particularly in its potential to reduce both costs and procedure length.
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Characteristics, Complications, and Outcomes of Critical Illness in Patients with Parkinson Disease.
Adults with Parkinson disease (PD) are hospitalized at higher rates than age-matched controls, and these hospitalizations are associated with significant morbidity. However, little is known about the consequences of critical illness requiring intensive care unit (ICU)-level care in patients with PD. The aim of this study was to define the characteristics and outcomes of adults with PD admitted to the ICU. ⋯ During critical illness, patients with PD are at increased risk for longer hospital lengths of stay and complications and require a higher level of care at discharge than matched controls. These findings reveal targets for interventions to improve outcomes for patients with PD and may inform discussions about goals of care in this population.
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The objective of this study was to define clinically meaningful phenotypes of intracerebral hemorrhage (ICH) using machine learning. ⋯ Machine learning identified three phenotypes of ICH that are clinically significant, associated with patient complications, and associated with functional outcomes. Cerebellar hematomas are an additional phenotype underrepresented in our data sources.
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Clinical prediction models serve as valuable instruments for assessing the risk of crucial outcomes and facilitating decision-making in clinical settings. Constructing these models requires nuanced analytical decisions and expertise informed by the current statistical literature. Access and thorough understanding of such literature may be limited for neurocritical care physicians, which may hinder the interpretation of existing predictive models. ⋯ Discussion encompasses critical elements such as model flexibility, hyperparameter selection, data imbalance, cross-validation, model assessment (discrimination and calibration), prediction instability, and probability thresholds. The intricate interplay among these components, the data set, and the clincal context of neurocritical care is elaborated. Leveraging this comprehensive exploration of statistical learning can enhance comprehension of articles encompassing model generation, tailored clinical care, and, ultimately, better interpretation and clinical applicability of predictive models.