Social Determinants of Health and Limitation of Life-Sustaining Therapy in Neurocritical Care: A CHoRUS Pilot Project.

Background: Social determinants of health (SDOH) have been linked to neurocritical care outcomes. We sought to examine the extent to which SDOH explain differences in decisions regarding life-sustaining therapy, a key outcome determinant. We specifically investigated the association of a patient's home geography, individual-level SDOH, and neighborhood-level SDOH with subsequent early limitation of life-sustaining therapy (eLLST) and early withdrawal of life-sustaining therapy (eWLST), adjusting for admission severity.

Early COVID-19 respiratory risk stratification using machine learning.

Background: COVID-19 has strained healthcare systems globally. In this and future pandemics, providers with limited critical care experience must distinguish between moderately ill patients and those who will require aggressive care, particularly endotracheal intubation. We sought to develop a machine learning-informed Early COVID-19 Respiratory Risk Stratification (ECoRRS) score to assist in triage, by providing a prediction of intubation within the next 48 hours based on objective clinical parameters.

Ethical, legal, and communication challenges in managing goals-of-care discussions in chronically critically ill patients.

Clinicians should expect controversial goals of care discussions in the surgical intensive care from time to time. Differing opinions about the likelihood of meaningful recovery in patients with chronic critical illness often exist between intensive care unit providers of different disciplines. Outcome predictions presented by health-care providers are often reflections of their own point of view that is influenced by provider experience, profession, and personal values, rather than the consequence of reliable scientific evaluation.

Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients.

Patients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86-0.

Variability in Perioperative Fasting Practices Negatively Impacts Nutritional Support of Critically Ill Intubated Patients.

The aim of the study was to quantify nutritional losses related to pre- and postoperative fasts in critically ill intubated patients and to explore whether shorter fasts are safe and appropriate in this population. A retrospective review of mechanically ventilated adults undergoing surgery more than 24 hours after admission to a Level I trauma center over 15 months was done, which yielded 132 procedures and 81 unique patients. Ninety per cent of preoperative periods and 43 per cent of postoperative periods were affected by nonmedical barriers to feeding.