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Intradialytic hypotension and hemodynamic phenotypes in children following continuous renal replacement therapy
Sameer Thadani1,2, Christin Silos3, Christopher Horvat4
1Department of Pediatrics, Division of Critical Care Medicine, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA. Sameer.thadani@bcm.edu.
Insights
Intradialytic hypotension (IDH) shortly after continuous renal replacement therapy (CRRT) connection in children is linked to worse outcomes. Unsupervised learning identified distinct hemodynamic patterns associated with these adverse events.
Area of Science:
- Pediatric Critical Care Medicine
- Nephrology
- Data Science in Healthcare
Background:
- Intradialytic hypotension (IDH) frequently complicates continuous renal replacement therapy (CRRT) in children, potentially impairing organ perfusion.
- Understanding the relationship between hemodynamic changes during CRRT initiation and patient outcomes is crucial for improving care.
- Unsupervised learning offers a novel approach to identify patient subgroups based on clinical trajectories.
Purpose of the Study:
- To investigate the association between intradialytic hypotension (IDH) during CRRT connection and patient outcomes in a pediatric population.
- To identify distinct hemodynamic trajectory-based phenotypes using unsupervised machine learning.
- To explore the utility of machine learning in analyzing CRRT data in critically ill children.
Main Methods:
- Retrospective observational study of pediatric patients (<18 years) undergoing CRRT.
- IDH defined as a sustained >20% decrease in mean arterial pressure (MAP) from baseline.
- K-means clustering applied to identify MAP trajectory phenotypes; major adverse kidney events at 30 days (MAKE30) as primary outcome.
Main Results:
- Higher IDH burden during CRRT connection was significantly associated with increased MAKE30.
- Two distinct MAP trajectory phenotypes were identified, showing significant differences in MAKE30 incidence.
- The study included 59 pediatric patients and analyzed 232 CRRT connections.
Conclusions:
- Early IDH within the first hour of CRRT connection in children is associated with poor clinical outcomes.
- Time-series clustering is a feasible method for identifying hemodynamic phenotypes in pediatric CRRT.
- Unsupervised machine learning can provide valuable insights into the impact of CRRT in critically ill children.
Background:
Intradialytic hypotension (IDH) leads to inadequate organ perfusion and occurs frequently after continuous renal replacement therapy (CRRT) connection. Unsupervised learning can enhance our understanding of how clinical trajectories impact outcomes. We aim to investigate the association between IDH during CRRT connection and outcomes, while also identifying hemodynamic trajectory-based phenotypes.
Methods:
A single center retrospective observational study of children (<18 years) undergoing CRRT from 9/2016 to 10/2018. IDH was defined as a sustained >20% decrease in mean arterial pressure (MAP) from baseline for ≥2 consecutive minutes. IDH burden was calculated by dividing connections with IDH by total observed connections. The primary outcome was major adverse kidney events at 30 days (MAKE30). K-means clustering was used to identify MAP trajectory-based phenotypes.
Results:
59 patients, 232 connections, and 13,920 minutes were included. Median age was 59 months (IQR 8-152). In multivariable analysis, higher IDH burden [β 4.35 (CI: 0.01-8.70)] was associated with MAKE30. Two distinct MAP trajectories phenotypes were identified, with differing incidence of MAKE30 [21 (100%) vs. 29 (76%), p < 0.01].
Conclusions:
IDH within the first hour of CRRT connection is associated with poor outcomes, and time-series clustering is feasible and could improve our understanding of the impact of CRRT in children.
Impact:
Repeated episodes of intradialytic hypotension within the first hour of continuous renal replacement therapy connection are associated with increased morbidity and mortality. Our findings suggest that intradialytic hypotension in the hour following CRRT connection in children is associated with poor outcomes. Unsupervised machine learning, an underutilized approach in pediatric research, identified two significantly different mean arterial pressure trajectory-based phenotypes with differing anthropometric features and outcomes. Leveraging unsupervised machine learning, we can identify trajectory-based subgroups that can provide insights into the impact of continuous renal replacement therapy in critically ill children.
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