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Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.

Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.

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The use of cardiac devices, including mechanical circulatory support (MCS), cardiac implantable electronic devices (CIEDs), and pacing wires, has increased and significantly improved survival in patients with severe cardiac failure. However, these devices are frequently associated with acute brain injuries (ABIs) including ischemic strokes, intracranial hemorrhages, seizures, and hypoxic-ischemic brain injury which contribute substantially to morbidity and mortality. Computed tomography (CT) and magnetic resonance imaging (MRI), the standard imaging modalities for ABI diagnosis, can pose significant challenges in this patient population due to the risks associated with patient transportation and the incompatibility of ferromagnetic components of certain cardiac devices with high magnetic field of the MRI.

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Objectives: Central nervous system complications of acute pancreatitis (AP) can result in cerebral edema (CE). We assessed the risk of serious outcomes and health care features associated with CE in patients hospitalized with AP.

Methods: We conducted a retrospective cohort study using the National Inpatient Sample database.

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Background: Regionally anticoagulated continuous renal replacement therapy with citrate is the first choice for critically ill patients with acute kidney injury. If citrate that reaches the patient exceeds the metabolic capacity, metabolic alkalosis will follow. Bicarbonate from the treatment fluids will also reach the patient and add to the bicarbonate load.

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Chronic subdural haematoma (cSDH) is a common subtype of traumatic brain injury, typically affecting older people living with frailty and multimorbidity. Until now, no published guideline has existed internationally to guide management, perhaps explaining why the care of the older cSDH patient varies between neurosurgical centres. The Improving Care in Elderly Neurosurgery Initiative guideline is the first guideline dedicated to the care of patients with cSDH across the entire patient pathway, from initial presentation through to rehabilitation and discharge after surgery.

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