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Intraoperative hypotension (IOH) is increasingly recognized as a major contributing factor associated with the development of postoperative complications in terms of renal [1–6], myocardial [6–8] and possibly, cerebral injury [9–11], despite substantial variability in literature regarding its exact definition [12, 13]. As IOH is not only associated with perioperative morbidity but with perioperative mortality [5, 14–17] as well—which is the 3rd greatest global contributor to deaths after ischemic heart disease and stroke [18]—efforts should be made to reduce both the incidence and duration of IOH. Hence, recently a consensus statement by the Perioperative Quality Initiative-3 workgroup [19] advises that for adults undergoing non-cardiac surgery, there is substantial evidence supporting that mean arterial pressure (MAP) should be kept above 60–70 mmHg in order to reduce postoperative myocardial and renal injury, and death. Given that even brief periods of IOH may be harmful—e.g. after induction of anesthesia and before surgical incision [1]—it may be beneficial to change our current practice from a reactive approach (by monitoring the patient`s actual hemodynamic status) [20, 21] to a proactive approach, by predicting vital signs [22], especially since (cumulatively) the longer a patients “spends” in IOH, the more likely it is that this will adversely affect outcome [14]. The current advances in medical technology include the use of machinelearning based algorithms [23, 24] to analyze large datasets in order to provide clinically useful information. Such predictive analytics may help in substantiating such a proactive approach. 1 In a nutshell: machine‐learning algorithm in (bio)medical research

Hypotension Prediction Index: from proof-of-concept to proof-of-feasibility