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Machine learning derived serum anion gap trajectories in critically ill patients with sepsis: A retrospective study based on MIMIC-IV database

2026-05-18

Abstract excerpt

<title>Abstract</title> <p>This study aims to explore the classification of sepsis patients based on early anion gap (AG) trajectories and its association with prognosis. The study included 30,881 adult ICU patients with first-episode sepsis from the MIMIC-IV database. Latent class mixed modeling (LCMM) was employed to identify distinct AG dynamic evolution patterns. The results revealed that patients could be ca...

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Literature Corpus work
c41a21d4-8b7a-5e92-805c-460f18f4225e
DOI
10.21203/rs.3.rs-8466355/v1
Open publication

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Machine learning derived serum anion gap trajectories in critically ill patients with sepsis: A retrospective study based on MIMIC-IV databaseDOI 10.21203/rs.3.rs-8466355/v1
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