Predictive medicine, machine learning, and anesthesia

Rabanal Llevot, J.M.
doi:10.1016/j.redare.2020.03.004
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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleDisplayedQuote" id="dsq0005"><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">&#8220;<span class="elsevierStyleItalic">Whoever looks at the past&#44; sees the future</span>&#8221;&#46; Lope de Vega</p></span></p><p id="par0010" class="elsevierStylePara elsevierViewall">It is easy to assume that arterial pressure &#40;AP&#41; contributes to perioperative morbidity and mortality&#46; Our older colleagues will remember the need to measure AP <span class="elsevierStyleItalic">&#34;</span>as you go<span class="elsevierStyleItalic">&#34;</span> using sphingonometry &#40;Korotov sounds&#41; or simply detecting the radial pulse after deflating the cuff to obtain a systolic AP value&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">In the 1990s&#44; the introduction and widespread use of automated oscillometric AP measurement methods lightened the workload of the surgical team and allowed more frequent readings&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">The anaesthesia monitoring standards published by the American Society of Anesthesiology &#40;ASA&#41; recommends measuring AP at least every 5&#8239;min&#46;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> This recommendation is echoed by the World Health Organization &#40;WHO&#41; in its 2009 &#34;Safe Surgery Guidelines&#34;&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">It is evident that current anaesthesia practice involves measuring AP&#44; among other parameters&#44; since it is essential for good transport of oxygen and nutrients&#44; cardiac output &#40;flow&#41;&#44; and also adequate perfusion pressure&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">The definition of hypotension is more controversial&#44; and a multitude of approaches are found in the literature&#44; depending not only on the threshold of absolute systolic&#44; diastolic or mean AP &#40;MAP&#41;&#44; but also on the percentage decrease in blood pressure over baseline&#46; Hence&#44; according to the study by Bijker et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> the frequency of intraoperative hypotension &#40;IOH&#41; ranges from 5&#37; to 99&#37;&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">From a pathophysiological point of view&#44; MAP is the determining parameter for defining hypotension&#44; since it represents the real pressure of blood flow&#46; Although the threshold IOH value for the onset of tissue ischaemia will vary among individuals &#40;age&#44; hypertension&#44; arteriopathy&#44; alteration of static or dynamic autoregulation&#44; etc&#46;&#41;&#44; the literature establishes a value of 60&#8722;65&#8239;mmHg&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">The impact of IOH on postoperative morbidity and mortality is well defined in the literature&#46; Salmasi et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> in a cohort of 57&#44;315 patients undergoing non-cardiac surgery&#44; showed that the risk of acute kidney injury and myocardial ischaemia begins when MAP falls below 65&#8239;mmHg or below 20&#37; of baseline&#46; Another large retrospective study of 33&#44;330 noncardiac surgery patients<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> found similar MAP thresholds of 55&#8239;mmHg for the risk of renal failure and myocardial ischaemia&#46; In their prospective study&#44; Sun et al&#46; reported identical MAP values of 55&#8239;mmHg lasting more than 20&#8239;min for renal failure&#44; and 60&#8239;mmHg lasting 10&#8239;min for myocardial ischaemia&#46;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a></p><p id="par0045" class="elsevierStylePara elsevierViewall">The literature not only associates IOH with kidney damage and postoperative myocardial damage&#44;<a class="elsevierStyleCrossRefs" href="#bib0020"><span class="elsevierStyleSup">4&#8211;11</span></a> but also establishes its association with ischaemic stroke&#44;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> postoperative delirium<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12&#8211;14</span></a> and death&#46;<a class="elsevierStyleCrossRefs" href="#bib0050"><span class="elsevierStyleSup">10&#44;11&#44;15</span></a> At this point&#44; given the incidence and consequences of IOH&#44; we might well ask ourselves whether it can indeed be predicted&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">Machine learning is a subset of artificial intelligence that involves designing systems capable of learn automatically&#46; The system actually consists of an algorithm in which millions of input variables or <span class="elsevierStyleItalic">features</span> are associated with other output variables&#44; or <span class="elsevierStyleItalic">labels</span>&#46; The computer uses this model to predict future behaviours&#46; Machine learning is therefore used to predict a certain behaviour&#44; and to do this it needs is to identify patterns that are undetectable to the human brain&#46; Using thousands or millions of features and identifying their past behaviour&#44; the computer is able to develop a predictive algorithm for the future&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">Hatib et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> applied machine learning to hundreds of thousands of AP waveform recordings in surgical patients or critical care units to construct a hypotension prediction index &#40;HPI&#41; ranging from 1 to 100&#46; The score indicates the likelihood that a patient will have a MAP of less than 65&#8239;mmHg in the next 5&#8722;15&#8239;min&#44; and that this MAP will last at least 1&#8239;min&#46; The scale had a sensitivity and specificity for predicting IOH of 92&#37; and 95&#37; at 5&#8239;min&#44; 89&#37; and 90&#37; at 10&#8239;min&#44; and 87&#37; and 88&#37; at 15&#8239;min before the hypotensive event occurs&#46; In the subsequent external validation in patients under anaesthesia&#44; the sensitivity and specificity were 87&#37;&#47;89&#37;&#44; 84&#37;&#47;84&#37; and 84&#37;&#47;83&#37; at 5&#44; 10 and 15&#8239;min&#44; respectively&#46; The prediction system has been commercialized&#44; and the IOH alarm is activated when the HPI reaches a likelihood equal to or greater than 85&#37;&#46; This value is accompanied on the screen by other haemodynamic variables that can guide therapeutic decision according to the possible origin of IOH &#40;contractility&#44; preload&#44; arterial load&#44; vascular resistance&#41;&#46;</p><p id="par0060" class="elsevierStylePara elsevierViewall">In this issue&#44; Solares<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> has published a case report in which the HPI was used in a patient with dilated cardiomyopathy who underwent liver resection&#46; The system predicted hypotension&#44; and allowed the surgical team to take prompt action to treat and prevent these events and identify the underlying cause&#46;</p><p id="par0065" class="elsevierStylePara elsevierViewall">The system has also been validated by other authors&#46; For example&#44; Davies et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> in a retrospective analysis of 255 patients&#44; found that the HPI system predicted hypotension with a sensitivity and specificity of 85&#46;8&#37; &#40;95&#37; CI&#44; 85&#46;8&#37;&#8211;85&#46;9&#37;&#41; and 85&#46;8&#37; &#40;95&#37; CI&#44; 85&#46;8&#37;&#8211;85&#46;9&#37;&#41; 5&#8239;min before the event occurred&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> In another prospective&#44; blinded&#44; randomized study in 99 patients undergoing hip arthroplasty&#44; Schneck et al&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> found that HPI monitoring allowed them to significantly reduce both the absolute number of hypotensive events and their duration compared to controls&#46;</p><p id="par0070" class="elsevierStylePara elsevierViewall">Given that the patterns identified in the AP waveform during development of the predictive algorithm are based on the start-up of compensatory mechanisms prior to hypotension&#44; it is logical to assume that increases in the HPI&#44; regardless of whether or not they reach 85&#37;&#44; are indicative of the implementation of these mechanisms&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">Although HPI is undoubtedly a promising monitoring tool&#44; some issues still remain unresolved&#44; for example&#58; the extent to which morbidity and mortality are reduced by using the HPI to predict the number of hypotensive events&#59; whether clinicians will take a more proactive attitude to treating IOH&#59; or the potential consequences of false positives&#59; and based on all of the above&#44; the cost-effectiveness of the HPI system&#46;</p><p id="par0080" class="elsevierStylePara elsevierViewall">What is undeniable is that machine learning applied to both the clinical and technological aspects of medicine is here to stay&#46; Liu et al&#46;&#44; performed a meta-analysis of studies comparing the diagnostic accuracy of health-care professionals and machine learning models&#44; and found no significant differences between the two&#44; although they observed a tendency towards superiority in the latter&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a> An investigation into the number of commercially available artificial intelligence and machine learning systems is beyond the scope of this editorial&#44; but it is estimated that between 2 and 3 are developed each month in fields such as diagnostic imaging&#44; oncology screening&#44; histological diagnosis&#44; dermatology diagnosis&#44; etc&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a></p><p id="par0085" class="elsevierStylePara elsevierViewall">Recently&#44; Attia et al&#46; used an artificial intelligence-enabled electrocardiogram to predict patients with left ventricular ejection fraction values of less 35&#37; with a sensitivity and specificity of 86&#46;3&#37; and 85&#46;7&#37;&#44; respectively&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a> The same author used the same technique to identify patients with atrial fibrillation during sinus rhythm&#44; also with high sensitivity and specificity&#46;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">23</span></a></p><p id="par0090" class="elsevierStylePara elsevierViewall">The appearance of artificial intelligence and machine learning predictive systems in the field of anaesthesia and critical medicine is a key factor in optimising patient care&#44; treatment and diagnosis both now and in the future&#46;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">24</span></a> Data from millions of electronic medical records will provide new models of care&#44; rendering the multiple risk or prognostic scales designed so far obsolete&#46; And an algorithm will establish the indication for surgery based on the patient&#8217;s history and risk assessment&#44; with sensitivities close to 100&#37;&#46; The future is now&#46;</p></span>"
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