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Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.
Takkavatakarn, Kullaya; Oh, Wonsuk; Chan, Lili; Hofer, Ira; Shawwa, Khaled; Kraft, Monica; Shah, Neomi; Kohli-Seth, Roopa; Nadkarni, Girish N; Sakhuja, Ankit.
Afiliação
  • Takkavatakarn K; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Oh W; Division of Nephrology, Department of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand.
  • Chan L; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Hofer I; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Shawwa K; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Kraft M; Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Shah N; Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Kohli-Seth R; Department of Anesthesiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Nadkarni GN; Division of Nephrology, Department of Medicine, West Virginia University, Morgantown, WV, USA.
  • Sakhuja A; Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Crit Care ; 28(1): 156, 2024 05 10.
Article em En | MEDLINE | ID: mdl-38730421
ABSTRACT

BACKGROUND:

Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear.

METHODS:

This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation.

RESULTS:

Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87-9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69-7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41-2.94). These associations were similar on external validation.

CONCLUSIONS:

These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estado Terminal / Sepse / Creatinina / Injúria Renal Aguda / Aprendizado de Máquina Limite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Crit Care Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estado Terminal / Sepse / Creatinina / Injúria Renal Aguda / Aprendizado de Máquina Limite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Crit Care Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos