ReviewSensors (Basel, Switzerland)2026
Monitoring Inputs, Control Architectures, and Failure Modes in Closed-Loop Vasopressor Systems: A Comprehensive Review.
Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Closed-loop vasopressor systems integrate real-time blood pressure monitoring with automated decision logic to support hemodynamic stability in perioperative and critical care environments. These technologies sit at the intersection of biomedical sensing, signal processing, and clinician-supervised automation: the quality, latency, and failure behavior of the monitoring input can directly shape controller performance, safety margins, and clinical usability. In this comprehensive review, we synthesize the major closed-loop vasopressor architectures reported in the literature, examine how sensor modality and signal integrity influence algorithm behavior, and summarize recurrent reliability vulnerabilities spanning sensors, control logic, and device integration. We organize the field through an end-to-end information pipeline-monitoring input, signal conditioning and quality assessment, decision and control strategy, actuation via infusion technology, and supervisory safety layers-highlighting common performance metrics used to benchmark control quality. We then discuss clinical validation patterns across settings, emphasizing practical considerations for deployment and the evidence gaps that remain most relevant to high-risk populations. Finally, we propose reporting and validation priorities for future studies, with a focus on sensor robustness, transparency of algorithm design, integration safeguards, and standardized documentation of failures and overrides.
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