Dear Editor,
We read with great interest the recent pilot study by Demailly Z et al. (2025), “Multimodal assessment of peripheral perfusion in critically ill patients: a pilot study”, published in Annals of Intensive Care [1]. This study provides important insights into the interplay among bedside perfusion indicators and their prognostic implications in critically ill patients. By simultaneously evaluating six parameters—capillary refill time (CRT), mottling score (MS), peripheral perfusion index (PPI), skin blood flow via laser Doppler flowmetry (SBFBF), temperature gradient (ΔT), and forearm tissue oxygenation (rSO₂)—the authors contribute significantly to the evolving framework of multimodal microcirculatory assessment. This integrative strategy marks a step forward toward precision-guided resuscitation in critical care.
From a clinical and implementation standpoint, three key considerations warrant further discussion.
First, the study’s focus on established ICU patients may limit its relevance for early-phase detection. Peripheral perfusion abnormalities often precede overt organ failure; easily accessible bedside tools such as CRT and PPI may offer rapid triage capabilities in emergency departments, postoperative units, or pre-hospital settings. For instance, the recent ANDROMEDA‑SHOCK‑2 trial demonstrated that a resuscitation protocol targeting CRT in early septic shock yielded a win ratio of 1.16 (95% CI: 1.02–1.33; P = 0.04) compared with usual care, highlighting CRT’s simplicity, reproducibility, and feasibility—especially in resource-limited settings [2]. A tiered approach, using low-cost bedside metrics for initial screening followed by confirmatory high-resolution techniques such as near-infrared spectroscopy (NIRS), may facilitate earlier recognition, timely intervention, and optimized resource allocation.
Second, the study underscores a need for improved data interoperability. In the current trial, the six monitoring tools operated independently and their outputs were recorded separately, highlighting the fragmentation typical of digital silos that hinder clinical integration and decision-making. Future research should adopt interoperable data frameworks (e.g., HL7 FHIR) to enable seamless integration with electronic health records. Such frameworks would facilitate the aggregation of multimodal perfusion data, enabling AI-driven analytics to support real-time, individualized hemodynamic management [3,4].
Third, from a global health perspective, the study was conducted in a high-resource ICU equipped with advanced tools such as laser Doppler flowmetry. To broaden its applicability in low- and middle-income countries (LMICs), a context-sensitive, tiered technology model is needed. In well-resourced settings, comprehensive multimodal monitoring may be feasible, while in LMICs, validated, low-cost tools such as CRT should be prioritized. Embracing adaptable frameworks that deploy sophisticated systems where feasible, and simplified methods where necessary, will promote equitable dissemination of peripheral perfusion monitoring advances across diverse healthcare contexts [5].
In conclusion, Demailly Z et al. lay a solid foundation for future research in bedside perfusion assessment. By expanding the focus to early detection, promoting interoperable data integration, and advocating for scalable technologies, these pilot findings may catalyze globally relevant improvements in critical care. The authors are commended for their innovative and forward-looking contribution to this rapidly evolving field.
CRediT authorship contribution statement
Jie Peng, Xingzhan Zhang, Ling Zhao wrote the main manuscript. All authors reviewed and approved the final manuscript.
Consent for publication
All authors have read and approved the final version of the manuscript and consent to its publication.
Ethics approval and consent to participate
Not applicable. This manuscript is a commentary on a published study and does not involve direct research with human participants or animals.
Funding
None.
Availability of data and materials
No new data were generated or analyzed in this commentary. The discussion is based on the referenced published article.
Declaration of competing interest
The authors declare that they have no competing interests.
Acknowledgements
The authors would like to thank Demailly Z et al. for their important research that stimulated this discussion.
References
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Associated Data
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Data Availability Statement
No new data were generated or analyzed in this commentary. The discussion is based on the referenced published article.