Personalized Fluid Management in Patients With Sepsis and Acute Kidney Injury: A Causal Machine Learning Approach

Causal-ML framework outperformed random forest model in identifying patients with AKI and sepsis who benefit from restrictive fluid therapy. This provides a data-driven approach for personalized fluid management and merits prospective evaluation in clinical trials.

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Publication Details

Journal
Critical Care Explorations
Published
2025-12-01
DOI
https://doi.org/10.1097/cce.0000000000001354
Primary Topic
Acute Kidney Injury Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Personalized Fluid Management in Patients With Sepsis and Acute Kidney Injury: A Causal Machine Learning Approach

Hernando Gómez, Khaled Shawwa, Kullaya Takkavatakarn, Michael Sterling et al.
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Personalized Fluid Management in Patients With Sepsis and Acute Kidney Injury: A Causal Machine Learning Approach

Hernando Gómez, Khaled Shawwa, Kullaya Takkavatakarn, Michael Sterling, Patricia Kovatch, Ashwin Sawant, John A. Kellum, Zainab Al-Taie, Won-Suk Oh, Girish N. Nadkarni, Pranai Tandon, Hannah Kittrell, Lili Chan, Mayte Suárez‐Fariñas, Ankit Sakhuja, Roopa Kohli‐Seth, Ira S. Hofer, Alexander W. Charney, Monica Kraft, Gagan Kumar, John Oropello
article en

Abstract

Causal-ML framework outperformed random forest model in identifying patients with AKI and sepsis who benefit from restrictive fluid therapy. This provides a data-driven approach for personalized fluid management and merits prospective evaluation in clinical trials.

Critical Care ExplorationsVol. 7(12)
West Virginia University (US), Emory University (US), Chulalongkorn University (TH), University of Pittsburgh (US), King Chulalongkorn Memorial Hospital (TH), West Virginia University Hospitals (US), Mount Sinai Hospital (US), Northeast Georgia Medical Center (US), Pulmonary and Allergy Associates (US), Icahn School of Medicine at Mount Sinai (US)
Openalex Percentile: Top 51%
Acute Kidney Injury Research
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