Cerebrovascular pressure reactivity monitoring using wavelet analysis in traumatic brain injury patients: A retrospective study

Abstract

Background: After traumatic brain injury (TBI), the ability of cerebral vessels to appropriately react to changes in arterial blood pressure (pressure reactivity) is impaired, leaving patients vulnerable to cerebral hypo- or hyperperfusion. Although, the traditional pressure reactivity index (PRx) has demonstrated that impaired pressure reactivity is associated with poor patient outcome, PRx is sometimes erratic and may not be reliable in various clinical circumstances. Here, we introduce a more robust transform-based wavelet pressure reactivity index (wPRx) and compare its performance with the widely used traditional PRx across 3 areas: its stability and reliability in time, its ability to give an optimal cerebral perfusion pressure (CPPopt) recommendation, and its relationship with patient outcome.

Methods and findings: Five hundred and fifteen patients with TBI admitted in Addenbrooke's Hospital, United Kingdom (March 23rd, 2003 through December 9th, 2014), with continuous monitoring of arterial blood pressure (ABP) and intracranial pressure (ICP), were retrospectively analyzed to calculate the traditional PRx and a novel wavelet transform-based wPRx. wPRx was calculated by taking the cosine of the wavelet transform phase-shift between ABP and ICP. A time trend of CPPopt was calculated using an automated curve-fitting method that determined the cerebral perfusion pressure (CPP) at which the pressure reactivity (PRx or wPRx) was most efficient (CPPopt_PRx and CPPopt_wPRx, respectively). There was a significantly positive relationship between PRx and wPRx (r = 0.73), and wavelet wPRx was more reliable in time (ratio of between-hour variance to total variance, wPRx 0.957 ± 0.0032 versus PRx and 0.949 ± 0.047 for PRx, p = 0.002). The 2-hour interval standard deviation of wPRx (0.19 ± 0.07) was smaller than that of PRx (0.30 ± 0.13, p < 0.001). wPRx performed better in distinguishing between mortality and survival (the area under the receiver operating characteristic [ROC] curve [AUROC] for wPRx was 0.73 versus 0.66 for PRx, p = 0.003). The mean difference between the patients' CPP and their CPPopt was related to outcome for both calculation methods. There was a good relationship between the 2 CPPopts (r = 0.814, p < 0.001). CPPopt_wPRx was more stable than CPPopt_PRx (within patient standard deviation 7.05 ± 3.78 versus 8.45 ± 2.90; p < 0.001). Key limitations include that this study is a retrospective analysis and only compared wPRx with PRx in the cohort of patients with TBI. Prior prospective validation is required to better assess clinical utility of this approach.

Conclusions: wPRx offers several advantages to the traditional PRx: it is more stable in time, it yields a more consistent CPPopt recommendation, and, importantly, it has a stronger relationship with patient outcome. The clinical utility of wPRx should be explored in prospective studies of critically injured neurological patients.

Fig 1. Example of data analysis using ICM+, showing time trends of arterial blood pressure (ABP), cerebral perfusion pressure (CPP), cerebral autoregulation (CA), and optimal CPP (CPPopt).

(A) Time trends of ABP, CPP, pressure reactivity index (PRx), and CPPopt according to PRx. (B) Time trends of ABP, CPP, wavelet pressure reactivity index (wPRx), and CPPopt according to wPRx. The U-shape curves at the bottom panels were used to determine automated CPPopt with a curve-fitting method. The (w)PRx-CPP plot was created using CPP 5-minute mean value as x-axis, with averaged (w)PRx values in CPP bins spanning 5 mmHg as y-axis.

Fig 2. Relationship and agreement between pressure reactivity index (PRx) and wavelet pressure reactivity index (wPRx).

(A) There was a strong positive, nonlinear relationship between PRx and wPRx. (B) Bland-Altman plot showing disagreement of the 2 parameters, with a tendency for a positive wPRx-PRx difference.

Fig 3. The ability of pressure reactivity index (PRx) and wavelet pressure reactivity index (wPRx) in distinguishing different patient outcome demonstrated by receiver operating characteristic (ROC) curve.

(A) ROC curve of PRx in distinguishing the favorable group and unfavorable group. (B) ROC curve of wPRx in distinguishing the favorable and unfavorable groups. (C) ROC curve of PRx in distinguishing the fatal and nonfatal groups. (D) ROC curve of wPRx in distinguishing the fatal and nonfatal groups. AUROC, area under the ROC curve. N = 515 (the Glasgow Outcome Scale [GOS] scores were missing in 73 patients), with outcome distributed as follows: good recovery, n = 75 (17.0%); moderate disability, n = 117 (26.5%); severe disability, n = 142 (32.1%); persistent vegetative state, n = 11 (2.5%); and death, n = 97 (21.9%).

Fig 4. Average cerebral autoregulation (CA) parameters in cerebral perfusion pressure (CPP) bins and the relationship between different optimal CPPs (CPPopts) and between CPP and CPPopt.

(A) Pressure reactivity index (PRx) versus CPP plot for the whole cohort. (B) Wavelet pressure reactivity index (wPRx) versus CPP plot for all patients. (C) The relationship between CPPopt calculated according to wPRx (CPPopt_wPRx) and CPPopt based on PRx (CPPPopt_PRx). (D) Bland-Altman plot of CPPopt_wPRx and CPPopt_PRx, showing high agreement of the 2 parameters. (E) CPPopt_PRx versus mean CPP. (F) CPPopt_wPRx versus mean CPP. Each data point in Fig 4C–4F represents the average value of each patient across the whole monitoring period (n = 515).

Fig 5. Graphs of the relationship between patient outcome and ΔCPP according to pressure reactivity index (PRx) (white bar) and wavelet pressure reactivity index (wPRx) (striped bar).

(A) A U-shape curve demonstrating that the smallest incidence of unfavorable outcome was associated with median cerebral perfusion pressure (CPP) around optimal CPP (CPPopt). (B) There was an asymmetrical inversed U-shape curve between favorable outcome and ΔCPP. (C) The mortality increases steadily when median CPP is increasingly below CPPopt. (D) Severe disability increased while CPP median is above CPPopt. The denominator represents the total patient number in each bar, and the numerator indicates the patient in each outcome group. ΔCPP, the difference between median CPP and CPPopt.

Fig 6. Distribution of Glasgow Outcome Scale (GOS) Score (%) versus the binned difference between the median cerebral perfusion pressure (CPP) and optimal CPP (CPPopt).

(A) Distribution of GOS Score versus the binned difference between median CPP and CPPopt _PRx; (B) Distribution of GOS Score versus the binned difference between median CPP and CPPopt _wPRx. CPPopt_PRx, optimal cerebral perfusion pressure according to pressure reactivity index (PRx); CPPopt_wPRx, optimal cerebral perfusion pressure according to wavelet PRx.

Fig 7. The relationship between cerebral pressure reactivity parameters, age, and intracranial pressure (ICP).

(A) Cerebral pressure reactivity seems to have deteriorated with the increase of age, shown by increased pressure reactivity index (PRx). (B) Wavelet pressure reactivity index (wPRx) also showed a slight increase while the age was increased. (C) PRx was linearly related with ICP. (D) A linear relationship existed between wPRx and ICP.