The aim of collecting patient-reported experiences of responsiveness is to learn whether care processes are responsive to patients' needs and well-being.
The aim with estimating individual responsiveness is to set a target for treatment effect – it may be growth response for which we now have prediction models.
A novel approach to inexpensive responsiveness is to utilize the theory of Selective Evolutionary Generation Systems (SEGS), which sequentially and probabilistically selects a candidate optimizer based on the ratio of the fitness values of two candidates and the level of selectivity.
The best way to perform a PLR maneuver to predict volume responsiveness is to elevate the lower limbs to 45° (automatic bed elevation or wedge pillow) while at the same time placing the patient in the supine from a 45° semirecumbent position (Figure 4).
Obviously, the easiest way to test preload responsiveness is to administer fluid and to observe the resulting effect on cardiac output.
The time-honored method of assessing preload responsiveness is to administer a relatively small intravascular volume bolus rapidly and observe the subsequent hemodynamic response in terms of blood pressure, pulse, cardiac output, SvO2 and related measures.
