LAUSR

Machine learning as a construct for artificial intelligence permeates many aspects of our professional lives where it offers the hope to improve health via multiple applications including image recognition, natural or textual language identification, “big data” analysis, and others. While society may not necessarily be completely ready to have their medical care (or automobiles!) usurped by a computer, artificial intelligence has the potential to augment clinical decision-making to enhance safety and improve outcomes. For example, artificial intelligence tools have been created to assist radiologists in reading lung cancer screening computed tomography images and mammograms and have demonstrated significantly better-than-human performance in almost all analyses. In this issue of Anesthesiology, Maheshwari et al. evaluate whether clinician access to alerts from a hypotension prediction algorithm reduces hypotension compared with usual care. This hypotensive predictor is a commercially available machine learning tool previously described. The proprietary algorithm uses analysis of complex features of the arterial waveform not visible with the naked eye. In a validation study, the hypotensive predictor had a sensitivity of 88% (85 to 90%) and specificity of 87% (85 to 90%) to identify a hypotension 15 min in advance (area under the receiver operating characteristic curve, 0.95). That study did not include episodes of hypotension caused by surgical manipulations, and it was not tied to whether a clinical intervention was necessary. The current pilot randomized trial thus has important relevance to this area of research in evaluating the hypotensive predictor under real-time clinical circumstances. The authors specifically ask the question of whether alerting clinicians of impending hypotension (mean arterial pressure [MAP] less than 65 mmHg) reduces its duration and severity compared with usual care. There is divergent opinion on what blood pressure constitutes “hypotension,” and we have argued that its definition is an individual cutoff not accurately derived from population based data. Nonetheless, retrospective investigations have found an association between an intraoperative MAP less than 65 mmHg and acute kidney injury, myocardial infarction, and mortality for adults undergoing noncardiac surgery. Newer data, however, suggests that postoperative hypotension rather than intraoperative hypotension is a more important determinant of myocardial infarction. Regardless, in the current study, 214 adult patients undergoing moderateto high-risk noncardiac surgery were randomized to an intervention group where hypotension prediction alerting was available to clinicians or a group where the alert was withheld. In the intervention arm, clinicians were given a recommended hemodynamic treatment algorithm that included any combination of administration of intravenous fluids, vasopressors, inotropic drugs, or observation. In both study arms, clinicians were advised to minimize the duration of MAP less than 65 mmHg. Eligibility included the need for direct arterial pressure measurement during surgery that was required to last for greater than 2 h. Patients were excluded for various reasons including conditions that are common in surgical patients (e.g., moderate or severe valvular heart disease, atrial fibrillation, or heart failure with low ejection fraction). The main findings were that there was no difference in the primary outcome of the time-weighted average of MAP less than 65 mmHg between groups, nor were there differences for the