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Treatment of congestion is one of the main goals in patients hospitalized for acute heart failure. Nevertheless, current evidence shows that decongestion is often not achieved and that residual congestion at discharge is strongly associated with poor outcomes. While this association has been demonstrated, previous studies have primarily focused on single parameters of congestion (physical examination, biomarkers, or imaging features). The aim of the study is to assess residual congestion at discharge using a multiparametric approach and to compare the prognostic value of each evaluation strategy. Additionally, the analysis will be supported by artificial intelligence to develop a multiparametric prognostic algorithm that can provide an improved predictive model compared to standard statistical approaches.
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