Articles: mortality.
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This study aims to investigate independent factors associated with 30-day mortality in patients with acute spontaneous intracerebral hemorrhage (SICH) before treatment. ⋯ Chronic kidney disease, ischemic heart disease, loss of hypertension follow-up, m, reactive pupils, pontine hemorrhage, and basal cistern persistence were independent variables associated with the 30-day mortality rate in SICH patients before treatment initiation. A m, pupil reaction, and basal cistern persistence serve as predictive tools for assessing mortality in SICH before treatment.
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Eur J Trauma Emerg Surg · Apr 2024
Cardiac risk stratification and adverse outcomes in surgically managed patients with isolated traumatic spine injuries.
As the incidence of traumatic spine injuries has been steadily increasing, especially in the elderly, the ability to categorize patients based on their underlying risk for the adverse outcomes could be of great value in clinical decision making. This study aimed to investigate the association between the Revised Cardiac Risk Index (RCRI) and adverse outcomes in patients who have undergone surgery for traumatic spine injuries. ⋯ The RCRI may be a useful tool for identifying patients with traumatic spine injuries who are at an increased risk of in-hospital mortality, cardiopulmonary complications, and failure-to-rescue after surgery.
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Background and Objectives: Patients presenting with ST Elevation Myocardial Infarction (STEMI) due to occlusive coronary arteries remain at a higher risk of excess morbidity and mortality despite being treated with primary percutaneous coronary intervention (PPCI). Identifying high-risk patients is prudent so that close monitoring and timely interventions can improve outcomes. Materials and Methods: A cohort of 605 STEMI patients [64.2 ± 13.2 years, 432 (71.41%) males] treated with PPCI were recruited. ⋯ This significantly improved the overall accuracy and specificity to more than 76% while maintaining the same level of sensitivity. This resulted in an AUC greater than 0.80 for most models. Conclusions: Machine learning algorithms analyzing hemodynamic traces in STEMI patients identify high-risk patients at risk of mortality.