โก Quick Summary
This study developed a causal machine learning framework to identify patients with sepsis and acute kidney injury (AKI) who would benefit from restrictive fluid management. The model demonstrated significant improvements in early AKI reversal rates, suggesting a promising direction for personalized treatment strategies. ๐
๐ Key Details
- ๐ Dataset: Retrospective study of patients with sepsis and AKI
- ๐งฉ Features used: IV fluid administration data
- โ๏ธ Technology: Causal machine learning framework
- ๐ Performance: Causal forest model outperformed random forest with AUTOC 0.15 vs. -0.02
๐ Key Takeaways
- ๐ Restrictive fluid management can significantly improve outcomes for certain patients with AKI.
- ๐ก Causal machine learning provides a data-driven approach to identify patients who will benefit from specific treatments.
- ๐ฉโ๐ฌ Study involved 1,931 patients in the external validation cohort.
- ๐ Early AKI reversal was achieved in 53.9% of patients receiving restrictive fluids compared to 33.2% in the control group (p < 0.001).
- ๐ค Sustained AKI reversal rates were also significantly higher in the restrictive fluid group (34.2% vs. 18.0%, p < 0.001).
- ๐ Lower rates of major adverse kidney events (MAKE30) were observed in the restrictive fluid group (17.1% vs. 34.6%, p = 0.003).
- ๐ Model validation was conducted using the Salzburg Intensive Care database.
- ๐ Clinical implications suggest the need for prospective evaluation in clinical trials.
๐ Background
Managing acute kidney injury (AKI) in patients with sepsis is a critical challenge in intensive care. While intravenous (IV) fluids are essential for treatment, they can lead to fluid overload, complicating patient outcomes. A more tailored approach to fluid management is necessary to optimize recovery and minimize adverse effects.
๐๏ธ Study
This retrospective study focused on patients admitted to the ICU with sepsis who developed AKI within 48 hours. The researchers aimed to create a causal machine learning framework to estimate individualized treatment effects of IV fluids, specifically targeting those who would benefit from a restrictive fluid strategy of less than 500 mL within 24 hours post-AKI.
๐ Results
The causal forest model significantly outperformed the random forest model in identifying patients who would benefit from restrictive fluid therapy, achieving an AUTOC of 0.15 in the external validation cohort. Among the 1,931 patients analyzed, the model recommended restrictive fluids for 68.9%, leading to markedly improved outcomes in early and sustained AKI reversal, as well as reduced rates of major adverse kidney events.
๐ Impact and Implications
The findings from this study highlight the potential of personalized fluid management in critically ill patients. By leveraging machine learning techniques, healthcare providers can make more informed decisions regarding fluid therapy, ultimately improving patient outcomes in sepsis-related AKI. This approach could pave the way for more tailored treatment protocols in intensive care settings.
๐ฎ Conclusion
This research underscores the transformative potential of causal machine learning in enhancing fluid management strategies for patients with sepsis and AKI. The ability to identify which patients will benefit from restrictive fluid therapy represents a significant advancement in personalized medicine. Future clinical trials are warranted to further validate these findings and integrate this approach into standard practice.
๐ฌ Your comments
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Personalized Fluid Management in Patients With Sepsis and Acute Kidney Injury: A Casual Machine Learning Approach.
Abstract
IMPORTANCE: IV fluids are the cornerstone for management of acute kidney injury (AKI) after sepsis but can cause fluid overload. A restrictive fluid strategy may benefit some patients; however, identifying them is challenging. Novel causal machine learning (ML) techniques can estimate heterogenous treatment effects (HTEs) of IV fluids among these patients.
OBJECTIVES: To develop and validate a causal-ML framework to identify patients who benefit from restrictive fluids (< 500โmL fluids within 24โhr after AKI).
DESIGN SETTING AND PARTICIPANTS: We conducted a retrospective study among patients with sepsis who developed acute kidney injury (AKI) within 48 hours of ICU admission. We developed a causal-ML approach to estimate individualized treatment effects and guide fluid therapy. We developed the model in Medical Information Mart for Intensive Care IV and externally validated it in Salzburg Intensive Care database.
MAIN OUTCOMES AND MEASURES: Our primary outcome was early AKI reversal at 24 hours. Secondary outcomes included sustained AKI reversal and major adverse kidney events by 30 days (MAKE30). Model performance to identify HTE of restrictive IV fluids was assessed using the area under the targeting operator characteristic curve (AUTOC), which quantifies how well a model captures HTE, and compared with a random forest model.
RESULTS: Causal forest model outperformed random forest in identifying HTE of restrictive IV fluids with AUTOC 0.15 vs. -0.02 in external validation cohort. Among 1931 patients in external validation cohort, the model recommended restrictive fluids for 68.9%. Among these, patients who received restrictive fluids demonstrated significantly higher rates of early AKI reversal (53.9% vs. 33.2%, p < 0.001), sustained AKI reversal (34.2% vs. 18.0%, p < 0.001), and lower rates of MAKE30 (17.1% vs. 34.6%, p = 0.003). Results were consistent in the adjusted analysis.
CONCLUSIONS AND RELEVANCE: 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.
Author: [‘Oh W’, ‘Takkavatakarn K’, ‘Al-Taie Z’, ‘Kittrell H’, ‘Shawwa K’, ‘Gomez H’, ‘Sawant AS’, ‘Tandon P’, ‘Kumar G’, ‘Sterling M’, ‘Hofer I’, ‘Chan L’, ‘Oropello J’, ‘Kohli-Seth R’, ‘Charney AW’, ‘Kraft M’, ‘Kovatch P’, ‘Suรกrez-Fariรฑas M’, ‘Kellum JA’, ‘Nadkarni GN’, ‘Sakhuja A’]
Journal: Crit Care Explor
Citation: Oh W, et al. Personalized Fluid Management in Patients With Sepsis and Acute Kidney Injury: A Casual Machine Learning Approach. Personalized Fluid Management in Patients With Sepsis and Acute Kidney Injury: A Casual Machine Learning Approach. 2025; 7:e1354. doi: 10.1097/CCE.0000000000001354