Blood pressure variability: can nonlinear dynamics enhance risk assessment during cardiovascular surgery?

As the population ages, increasing numbers of elderly patients with multiple co-morbid conditions are presenting for high-risk cardiovascular surgical procedures. The commensurate increase in perioperative major adverse events (MAEs) increases mortality by 1.4 to 8-fold,1 with an estimated 1 billion dollars annually spent on managing these complications.2 Current MAE risk prediction indexes3,4 are typically based on static or “snapshot” measures, such as the presence or absence of a co-morbid condition like hypertension. Unfortunately, these indexes have failed to adequately predict which high-risk patients will have MAEs5–7 possibly, at least in part, because they do not take into consideration the complex (nonlinear), time-varying features of physiological hemodynamic signals. Furthermore, a “one-size-fits-all” risk prediction model approach is unlikely to accurately identify patients at high risk5–7 particularly at extremes of age and predicted risk.8–13 A major motivation for the program outlined here is that current risk prediction tools may be improved by incorporating dynamical properties of physiologic signals, thereby enhancing: (a) individual patient risk assessment and counseling, (b) design of timely interventions to prevent disabling or fatal complications (e.g., stroke, renal failure, atrial fibrillation and myocardial infarction), and (c) the accuracy of comparisons of provider and hospital performances. Toward this end, our goal is to develop a real-time blood pressure variability (BP variability) index or set of indexes incorporating a patient's own baseline and evolving pathophysiologic characteristics into current “snapshot” scoring systems.4,5,14 One of the most important physiologic signals obtained in the perioperative period is the continuously recorded systemic BP signal.15 While the optimization of BP is a major perioperative target there is no universally accepted guideline for defining hypotension. Furthermore, hypotensive episodes, are dynamic, not static phenomena and not only vary from patient to patient but also within a patient at different surgical stages. Therefore, measures of BP variability, quantified using different metrics, have been the focus of considerable interest. For example, in one study,16 BP variability was defined as the time spent above or below a target systolic blood pressure range of 95–135 mm of Hg, and an increased BP variability value was associated with higher 30-day mortality. In another study, BP variability was defined as the root mean successive square difference of a moving 5 second time period. In this investigation17, decreased intracranial pressure and BP variability were shown to predict long-term adverse outcome after aneurysmal subarachnoid hemorrhage. An intuitive limitation of traditional measures of variability is the fact that they do not take into consideration the temporal structure of a sequence of measurements. For example, the following two sequences: A = {1 2 3 2 1 2 3 2 1 2 3 2 1} and B ={1111222222333}16, have the same variability, as measured by amplitude of range and standard deviation, but completely different structures. In fact, while sequence A defines a triangular wave, sequence B is a step function. Measures that are sensitive to the temporal organization of a signal have been essential in characterizing and discriminating different dynamical systems. Here we assess BP fluctuation (variability) dynamics via two complementary metrics: 1) traditional standard deviation of BP time series and 2) the degree of complexity of their dynamics. The motivating framework for quantifying the degree of complexity of nonlinear physiologic signals, such as BP, is that complexity reflects the degree of robustness/resilience of the underlying control mechanisms, and it decreases with aging and pathology (http://physionet.org/tutorials/cv/, accessed Oct 21, 2013). The term nonlinear may be unfamiliar to readers of physiologic and clinical journals. Linear systems exhibit two properties: proportionality and superposition. Proportionality, as implied by the term, means that there is a straight-line relationship between input and output. Superposition indicates that one can completely understand the system (e.g., a Rube Goldberg-type device) by breaking it down into multiple sub-components. In contrast, the sub-components of a non-linear system do not “add up” to the whole because of either “constructive” or “destructive” interactions between those sub-components. In these cases, reductionist strategies will fail to provide full understanding of a given system.18,19 Furthermore, in nonlinear systems, unanticipated (“off-target”) effects are likely since small input changes may induce major changes in the output (the “so-called “butterfly effect”). Pilot Study: Overview In this pilot study, we tested the feasibility of: i) acquiring BP waveform data of sufficient length and quality for nonlinear complexity analyses, and ii) converting the data from a proprietary to an open-source format. Our specific hypothesis is that the complexity of the dynamics of systolic arterial (SAP), diastolic arterial (DAP) and pulse pressure (PP) from the post-bypass period is lower for the group of patients with MAEs (cases) than for a control group with comparable risk but no MAEs. We included pulse pressure dynamics in light of evidence that abnormalities in pulse pressure has been independently associated with up to 3-fold increase in MAEs following cardiac surgery.20