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Abstract Background: Mortality in sepsis-associated liver injury (SALI) remains high, and early risk stratification is difficult. Existing prognostic models commonly focus on a single endpoint, usually 28-day mortality, and rarely undergo independent external validation. Methods: We developed machine-learning models using clinical features recorded during the first 24 h to predict all-cause in-hospital mortality within 7, 14, and 28 days.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
