Dear Editor,Fluid balance management in critically ill heart failure (HF) patients remains a formidable clinical challenge. While clinicians typically aim for net negative fluid balance to alleviate symptoms, recent studies employing fixed strategies have yielded inconsistent results. 1,2 The 2024 Heart Failure Association guidelines of the European Society of Cardiology emphasized the importance of individualized fluid balance strategies, particularly for critically ill patients. 3 Our study introduces a novel approach using unsupervised learning to identify four distinct phenotypes of critically ill HF patients, each with unique clinical characteristics and fluid balance requirements. To facilitate clinical application, we have developed a user-friendly interface that enables rapid phenotype identification and customized fluid management.We utilized two non-overlapping databases: III-CareVue subset and IV versions of the Intensive Care Medical Information Marketplace (MIMIC) 4 for training cohorts and the eICU Collaborative Research Database (eICU) 5 for external validation (Method S1). The MIMIC cohort comprised 5998 patients, while the eICU cohort included 2549 patients (Figure S1). We initially extracted 56 variables from the first day of ICU admission. After eliminating variables with more than 30% missing data, 47 variables remained, encompassing demographics, comorbidities, laboratory values, vital signs, interventions, and severity scores. To ensure a balanced contribution of characteristics, all data underwent cleaning and normalization (Method S2, Figure S2). In-hospital mortality served as our primary outcome, with ICU length of stay and total hospital length of stay as secondary outcomes.Uniform Manifold Approximation and Projection (UMAP) was used to determine that there were no differences in clinical characteristics between the two training databases (Figure S3). To classify patients, we applied the K-prototypes clustering algorithm, which effectivelyThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.