Hemodynamic phenotyping 4.0.
Clinical Snapshot
PICO Framework
| P — Population | Healthcare professionals and patients requiring hemodynamic assessment in critical care settings |
| I — Intervention | Visual decision support tools for hemodynamic phenotyping |
| C — Comparator | Machine learning algorithms for hemodynamic phenotyping |
| O — Outcomes | Clinical understanding, bedside identification accuracy, patient safety, cost-effectiveness |
Bottom Line
This narrative review discusses the evolution of hemodynamic phenotyping, comparing traditional machine learning approaches with newer visual decision support tools. The authors argue that for 'small data' scenarios typical in bedside hemodynamic assessment, complex machine learning algorithms may be unnecessary and potentially problematic for patient safety. They advocate for visual tools that leverage clinicians' natural pattern recognition abilities as a more practical alternative. However, this paper lacks the rigorous methodology expected of high-quality reviews and provides limited empirical evidence to support its conclusions. While the concepts discussed are relevant to Australian critical care practice, clinicians should await comparative studies with robust clinical outcomes before making significant changes to current hemodynamic assessment practices. The paper serves more as a thought-provoking commentary than definitive guidance for clinical practice.
Key Findings
Effect Size: Not applicable - no quantitative analysis
Primary Outcome: Conceptual comparison of visual versus machine learning approaches to hemodynamic phenotyping
Nnt Or Sensitivity: Not applicable - conceptual paper
Confidence Interval: Not provided
Clinical Application
High feasibility for visual tools implementation; variable for machine learning depending on institutional resources Relevant to Australian ICUs and anaesthetic practice; aligns with ANZICS emphasis on practical, evidence-based monitoring approaches Critical care physicians, anaesthetists, and intensive care nurses involved in hemodynamic monitoring
Abstract
The concept of integrating hemodynamic variables to define specific profiles or phenotypes has been established for decades. Describing hemodynamic phenotypes plays a key role in educating healthcare professionals about cardiovascular physiology, enhancing the understanding of shock mechanisms, and informing treatment strategies. Recently, two notable innovations have emerged to support bedside identification of hemodynamic phenotypes: machine learning (ML) algorithms and visual decision support tools. When it comes to "small data," such as a limited set of hemodynamic variables, ML algorithms may not be essential for data integration or interpretation. In addition, the hemodynamic phenotypes identified by ML techniques often mirror traditional textbook profiles, though occasionally with inconsistencies that may impact patient safety. This raises valid questions about the need to integrate complex and proprietary ML algorithms for bedside hemodynamic assessment. By contrast, visual tools leverage clinicians' innate ability to process graphical information rapidly, improving the understanding of cardiovascular physiology and enabling recognition of hemodynamic profiles at a glance. As such, they may offer a practical, accessible, and cost-effective alternative to ML-based solutions. Future studies comparing the clinical impact of visual versus ML-driven phenotyping are now needed to guide further development and implementation.
References
- 1.Michard, F., & Abou-Arab, O. (2026). Hemodynamic phenotyping 4.0. Anaesthesia, Critical Care & Pain Medicine, 45(2), 101647. https://doi.org/10.1016/j.accpm.2025.101647
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