Neurocritical care
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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.
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Management of assisted ventilation and determining the optimal timing for discontinuation presents a significant clinical obstacle in patients affected by neuromuscular (NM) diseases. This study aimed to evaluate the efficacy of ultrasound in appraising diaphragmatic function for predicting the necessity of intubation and determining the opportune moment to discontinue mechanical ventilation (MV) in patients with NM disorders. ⋯ The presence of a baseline left DE of less than 1 cm, a consecutive decrease in DE measurements within 48 h, and a comparative reduction in right DE of more than 50% within the initial 3 days are indicators associated with the requirement for MV in patients with NM disease. Furthermore, the upward trajectory of DE in mechanically ventilated patients is linked to an increased number of days free from ventilator support, suggesting its potential to forecast earlier weaning.