Authors: Mukkamala R, Schnetz MP, Khanna AK, et al.
Anesthesia & Analgesia 141(1):61-73, doi:10.1213/ANE.0000000000007216
This narrative review evaluates the current state of intraoperative hypotension (IOH) prediction, exploring its methodologies, clinical controversies, and future research directions. Predictive models aim to preemptively mitigate hypotension-related organ injury through machine learning applied to large patient datasets. However, while current methods like the Hypotension Prediction Index (HPI) demonstrate high sensitivity and specificity, their low positive predictive value (~25-30%) limits their clinical effectiveness. Moreover, the prevailing mean arterial pressure (MAP) dominates predictive accuracy, making advanced algorithms only marginally superior to simple MAP monitoring. The article critiques existing validation methods, emphasizing the need for more robust evaluations, including precision-recall analysis and comparisons directly against MAP thresholds.
The authors propose redefining acute hypotensive events (AHEs) using metrics like area-under-the-MAP curve below individualized thresholds, tailored to patient-specific factors (age, ASA score, etc.). Furthermore, they advocate extending prediction models to forecast not only impending hypotension but also associated reductions in blood flow and treatment response. A preliminary risk-benefit analysis suggests that current predictive models may prevent a small number of posthospital injuries but at the expense of numerous unnecessary interventions.
The outlook for IOH prediction depends on shifting research towards individualized, physiology-driven models that surpass standard MAP monitoring in real-world clinical impact. Future trials must focus on patient-centric outcomes and robust comparative standards to validate predictive utility effectively.
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