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<p>Goal-directed haemodynamic therapy (GDHT) is a comprehensive haemodynamic optimisation strategy rather than simple fluid optimisation. It guides peri-operative haemodynamic management towards predefined physiological targets to reduce post-operative complications in high-risk surgical patients. This review asks whether GDHT retains clinical value in 2026, which patients are most likely to benefit, which targets are most appropriate, and what its relationship to Enhanced Recovery After Surgery (ERAS) programmes should be.</p><p> The physiological basis of GDHT links surgical injury to an inflammatory cascade that impairs vascular integrity and tissue perfusion and draws on Shoemaker’s seminal observation that prompt restoration of oxygen delivery during surgery or in the first post-operative hours predicts survival. RCTs and meta-analyses have reported reductions in post-operative complications, most consistently acute kidney injury, surgical site infections, and gastrointestinal morbidity, with the benefit most consistent in patients at the highest peri-operative risk. Recent evidence has complicated this picture. OPTIMISE II and iPEGASUS, two large pragmatic trials, produced neutral results, and the incorporation of fluid optimisation into ERAS protocols raises a practical question: what does GDHT add when good peri-operative care already includes haemodynamic attention? </p><p>These uncertainties converge on a single practical difficulty: there is no universally accepted definition of “high risk”, which limits both trial comparability and clinical uptake. Artificial intelligence tools, in particular closed-loop haemodynamic management systems and machine learning models for preoperative risk stratification, are appraised here as potential means of supporting more individualised implementation. </p><p>GDHT retains clinical value in 2026, but the benefit appears most evident in patients at the highest peri-operative risk, while in intermediate-risk populations the evidence for added value beyond well-implemented ERAS remains uncertain. The most effective strategy is a multiparameter-based one, combining dynamic preload indices, cardiac output monitoring, and metabolic endpoints rather than fixed single-variable targets. Validated risk stratification is the critical prerequisite for both rational clinical implementation and meaningful future trials.</p>

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Peri-operative goal-directed haemodynamic therapy and high-risk patients: what’s going on? A narrative review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Mariateresa Giglio,
  • Alberto Corriero,
  • Luciano Frassanito,
  • Elena Giovanna Bignami,
  • Filomena Puntillo,
  • Luigi Tritapepe

摘要

Goal-directed haemodynamic therapy (GDHT) is a comprehensive haemodynamic optimisation strategy rather than simple fluid optimisation. It guides peri-operative haemodynamic management towards predefined physiological targets to reduce post-operative complications in high-risk surgical patients. This review asks whether GDHT retains clinical value in 2026, which patients are most likely to benefit, which targets are most appropriate, and what its relationship to Enhanced Recovery After Surgery (ERAS) programmes should be.

The physiological basis of GDHT links surgical injury to an inflammatory cascade that impairs vascular integrity and tissue perfusion and draws on Shoemaker’s seminal observation that prompt restoration of oxygen delivery during surgery or in the first post-operative hours predicts survival. RCTs and meta-analyses have reported reductions in post-operative complications, most consistently acute kidney injury, surgical site infections, and gastrointestinal morbidity, with the benefit most consistent in patients at the highest peri-operative risk. Recent evidence has complicated this picture. OPTIMISE II and iPEGASUS, two large pragmatic trials, produced neutral results, and the incorporation of fluid optimisation into ERAS protocols raises a practical question: what does GDHT add when good peri-operative care already includes haemodynamic attention?

These uncertainties converge on a single practical difficulty: there is no universally accepted definition of “high risk”, which limits both trial comparability and clinical uptake. Artificial intelligence tools, in particular closed-loop haemodynamic management systems and machine learning models for preoperative risk stratification, are appraised here as potential means of supporting more individualised implementation.

GDHT retains clinical value in 2026, but the benefit appears most evident in patients at the highest peri-operative risk, while in intermediate-risk populations the evidence for added value beyond well-implemented ERAS remains uncertain. The most effective strategy is a multiparameter-based one, combining dynamic preload indices, cardiac output monitoring, and metabolic endpoints rather than fixed single-variable targets. Validated risk stratification is the critical prerequisite for both rational clinical implementation and meaningful future trials.