Novel representation of physiologic states during critical illness and recovery

In the previous issue of Critical Care, Cohen and colleagues [1] offer a new approach to identifying and describing states of critical illness. The work follows a path, launched by John Siegel and colleagues [2,3] almost two decades ago, toward letting the data themselves define densely populated regions of physiologic state space that collectively represent a clinical condition. Areas of densely and of sparsely populated regions of the state space arise spontaneously from interconnections among various organ systems and their constituent tissues [4].What Cohen and colleagues have added to the analysis are bioinformatic tools developed, applied, and validated in the service of genomic analysis. Heat maps representing relative expression and hierarchical clustering give a sense of similarity of states and their adjacencies in physiologic state space, respectively. But the report has a deeper significance that perhaps can be grasped by inspection of Figure 1.When we clinicians glance up at a bedside physiologic display ('monitor') and look at the heart rate and blood pressure, we obtain the picture seen in Figure 1a. The difficulty is that the present state can be reached from many trajectories, so that the important inverse problem, namely 'what condition led to the particular values of the blood pressure and heart rate', is ill posed in the sense of Hadamard [5,6]. There are essentially an infinite number of trajectories that lead to this point. One approach to clarifying the problem is to generate a mathematical model and then ask what sort of perturbation would offer the most clarification as to the actual condition of the patient [7]. Another approach is to look backwards in time, as in Figure 1b, to see whether there is a clue concerning a trend. Either way, the question/answer that many clinicians think they wish to know is represented in Figure 1c: 'what will the patient's physiology look like at some time in the future, and what is my level of confidence in t