Supplementary Digital Content 2
In this file we provide six supplementary Figures that explore in more detail some of the findings described in the accompanying research report. The following material includes:
Figure S1. Graphs illustrating the steps in the computational process for generating input-to-output summary plots used in the main text (pages 2-3)
Figure S2. Impact of low frequency changes in mean BP profile over 6 hours on CPPOpt when CA is intact (pages 4-5)
Figure S3. Impact of changes in PaCo2 level or state of impairment in CA (pages 6-7)
Figure S4. To be compared with Figure 3 in the main text (page 8)
Figure S5. To be compared with Figure 4 in the main text (pages 9-10)
Figure S6. Changes in PRx with CA (to be compared with Table S1, SDC 3) (pages 11-12)
Figure S1.
Graphs illustrating the steps in the computational process for generating input-to-output summary plots used in the main text.After using the lumped compartmental model of CA we have the following steps illustrated by graphs:
a, input profiles in BP during the 6-hour simulation;
b, output real-time ICP profile during the simulation;
c, output real-time CPP profile during the simulation;
d, output real-time profile in whole brain CBF.
Panels e and f are derived using ICP-to-BP Fisher-transformed Pearson’s correlation values, PRx:
e, serial plot of individual time-averaged PRx values, during the simulation;
f, plot of CPP by 5 mm Hg “bins” against mean PRx values.
The Figure also shows that there needs to be significant slow wave oscillation in order to generate a suitable candidate for CPPOpt. For example, compare the three input BP profiles in Fig.S1a with the output PRx-plots in Fig.S1f. The more variant sample (i.e., with greater-peak-to-peak oscillation input profile, blue line) has a clearly defined candidate for CPPOpt. See Figure S2 (next page) for further analyses.
Figure S2.
Impact of low frequency change in mean BP profile over 6 hours on CPPOpt when CA is intact.In this composite Figure, first compare the two BP input profiles (parts a, b) with their respective output PRx–plots (compare a-b with c-d). The superimposed blue dotted lines in b and d are the transformations used to calculate CPPOpt. The discrepancy in the PRx-plot with two minima and blue dotted line in b shows that there is more than one candidate for CPPOpt, which causes ambiguity (see text for details).
Next in parts e and f, three different patterns in BP profile inputs with output PRx–plots (parts g and h respectively) are shown. One of the patterns in BP profile used in e (highlighted black, originating at 80 mmHg) shifted either up or down by fixed amount, giving 6 inputs differing in mean BP (g and h, are the same components used in Figure 2 in the main text).
Figure S3.
Impact of change in PaCo2 level or state of impairment in CA.The component graphs in the Figure are grouped in columns, with results in change in PaCo2 level on the left and results in impairment state of CA on the right.
The input BP pattern is the same (see red line originating at 100 mmHg in Fig.
2a in the main text). Data are presented with resulting ICP profile (panels a and d), consequent CBF profile (panels b and e) and output CPP versus PRx–plots (panels c and f, see text for details):
a. Constant change in PaCo2 (32, 36, 40, 44, or 48 mm Hg; baseline 40 mmHg highlighted as red) over the 6-hour period causes little variation in ICP profile;
b. Constant change in PaCo2 has an effect on CBF (compare with Fig. 1b in main trxt);
c. Constant change in PaCo2 has minimal impact on the PRx–plots;
d. Constant impairment in CA (0%, 25%, 50%, 75% or 100%; baseline 0%
highlighted as red) over the 6-hour period causes little variation in ICP profile;
e. Constant impairment in CA has an impact on CBF when perturbed by expected swings in input mean BP profile (compare with Fig. 1c in main text);
f. Constant impairment in CA has minimal impact on the PRx–plots. However, the value of PRx increases from negative to positive as the degree of CA impairment increases.
Figure S4.
To be compared with Figure 3 in main text.Composite Figure with same format as Figure S1:
a, input mean BP profile during the 6-hour simulation (same as Fig.S2c);
b, six constant ICP profile levels during the simulation (same as Fig. 3a in main text);
c, output real-time profile in CPP during the simulation;
d, output real-time profile in whole brain CBF.
e, serial plot of individual time-averaged PRx values, during the simulation;
f, plot of CPP by 5 mm Hg “bins” against mean PRx values.
Figure S5.
To be compared with Figure 4 in main text.Composite Figure with same format as Figure S1:
a, input mean BP profile during the 6-hour simulation (same as Fig.S2c and Fig.S4a);
b, six ICP profiles with serial peaks levels during the simulation (same as Fig.
4a);
c, output real-time profile in CPP during the simulation;
d, output real-time profile in whole brain CBF;
e, serial plot of individual time-averaged PR values, during the simulation;
f, plot of CPP by 5 mm Hg “bins” against mean PRx values.
The graphical sequence shows that at the start and end of each serial peak in ICP, PRx decreases and returns to the baseline value, which is similar to that reported in the clinical literature (see Lang EW, et al. Changes in cerebral partial oxygen pressure and cerebrovascular reactivity during intracranial plateau waves. Neurocrit Care 2015;23:85-91).
Since CA is intact in the simulation, we expect to see a large decrease in PRx
values. In cases where CA is not intact, we would expect to see a smaller decrease in the PRx value at the start and end of each serial peak. Please note that intactness of CA is an intrinsic property of the whole brain model that we have used, and so it does not change by the value of ICP. Changes in ICP only changes the working point, and consequently amount of decrease in PRx value (see SDC 4).
Figure S6.
To be compared with Table S1 (SDC 3).Each component of Figure S6 shows changes in the mean (red line) and standard deviation (dotted borders) of PRx with respect to the changes in the gain of CA under different conditions (for methods, see section D, SDC 1).
In each of the component (a-f) we carried out 126 simulations, but for clarity only show the results of 26 per component:
Blood pressure (a, b): Changes in PRx with respect to the CA gain when average BP is 104 mmHg (a) and 84 mmHg (b), respectively. During these simulations, PaCo2 and ICP were set to constant values of 40 mmHg and 9.5
Change in PaCo2 (c): Changes in PRx with respect to the CA gain when PaCo2
is held at 32 mmHg is shown. During these simulations, average BP and ICP were 104 mmHg and 10 mmHg, ad 32 mmHg, respectively (compare the distribution with a).
Change in ICP patterns (d, e, f): Changes in PRx with respect to the CA gain when there is a constant level in ICP to 25 mmHg (d), or serial peaks in ICP to 25 mmHg (e), or escalation in ICP to 20 mmHg (f) (see Figs. 3-5 for
comparison). During these simulations, average BP and PaCo2 were 104 mmHg and 40 mmHg, respectively (compare the distribution with a).
On inspection of the graphs it is evident that, in general, there is a decrease in PRx when increasing CA gain (a-f). However, PRx alone cannot be used to assess the state of CA as it is also sensitive to the pattern of ICP (a versus d-f) and BP (a versus b), and PaCo2 (a versus b) For example, mean PRx changes between -0.72 and 0.20 at gain of 100% for different conditions. Therefore, in order to correctly assess the state of CA, other parameters should be taken into consideration.
