Acute hypercapnic cerebral vasodilatation
The influence of changes within the cerebral vasculature and there impact on ICP remain poorly understood in humans. Studies [49] have shown that in head injured patients, the cerebral perfusion pressure is inversely proportional to the amplitude of pulsatile inflow and, consequently, the exponential shape of the pressure-volume relationship is not the only factor influencing the magnitude of ICP pulse wave [50]. This section presents a recent study [14] conducted by our group to test the hypothesis that acute hypercapnic cerebral vasodilatation induces consistent changes in ICP waveform morphology. This hypothesis is tested on a dataset of ICP signals of uninjured patients undergoing a CO 2 inhalation challenge in which hypercapnia induced acute cerebral vasodilatation. For each morphological metrics extracted from the ICP waveforms using MOCAIP, the consistency and rate of change were analyzed.
Materials and methods
The hypercapnic dataset consists of the ICP and ECG recordings of four patients, who were admitted at UCLA medical center for the evaluation of their chronic headaches. During their hospitalization, the patients received continuous ICP monitoring using intraparenchymal microsensors situated in the right frontal lobe. They also underwent a CO 2 challenge test by inhaling a 5% CO 2 mixture for less than three minutes. During the test, ICP and ECG signals were recorded at a sampling rate of 400 Hz at the bedside with a dedicated acquisition system.
To quantify the rate of change of each metric over a specific time segment, a line was first fitted to the segment of interest and then the slope of this line was used to calculate the rate of the metric change. The sign of the obtained hourly rate of change (negative vs. positive) was used to determine the trend of change (decreasing vs. increasing).
Experimental protocol
The average duration of the selected data segment was (5.1 + 0.7 minutes) which included (1.5 + 0.5minutes) of baseline, (2.5 + 0.5 minutes) of CO2 challenge test and (1.1 + 0.2minutes) of post-test data. The slope of the lines fitted to each of the extracted metrics over the rising edge of ICP signal during CO 2 challenge test and the falling edge of ICP signal during the post-test normal breathing, were used to define the hourly rate of change during the test and post-test normal breathing, respectively. For the purpose of comparing the hourly rate of change between different metrics, each metric rate was normalized by the average value of the corresponding metric over either the last ten beats of the baseline or the first ten beats of the stabilized part of the post-test data.
To evaluate the results from baseline, test, and post-test, we report 1) the consistency of changes of individual metrics; 2) differences in rate of metric changes; and 3) which of the peak regions (P1, P2 and P3) has a more dramatic change during the cerebral vasodilation; the specifics of how the region assignments were defined are in the publication [14]. In addition, we calculated the region-weighted relative hourly rate of change averaged over the subset of consistent metrics.
Fig. 6. Illustration of induced hypercapnia for a headache patient. There are three segments (Baseline, CO2 Inhalation, and Post-test) with the linear fits shown by dotted lines.
Results
Fig. 6 depicts the mean ICP value for one of the headache patients during the baseline, the CO2 challenge test, and the post-test normal breathing. When the patient inhales the 5% mixture of CO2, mean ICP increases over time, reaches a saturation level, and then stabilizes. When the patient returns to breathing normal concentrations of CO2, the mean ICP returned to baseline in less than one minute.
Investigating the hourly rate of change for all 128 ICP metrics during the hypercapnic and normal breathing post-test data, reveal that; out of 128 ICP metrics, 72 metrics had consistent changes in association with CO2 changes for all four subjects. We observe that no metrics had the same trend during both the hypercapnic and normal breathing post-test data. This observation is consistent with our expectation that, if a variable has a specific trend of change in one condition, the change would be in the opposite direction as the condition is reversed. We also observe that for all subjects, 50 metrics consistently increased ("+" metrics) during hypercapnia and decreased when patients switched back to room air and 22 metrics consistently decreased ("-" metrics) during CO2 inhalation phase and increased during post-test normal breathing.
The region-weighted (P1, P2, P3) relative hourly rate of change of the 50 ”+” metrics were (0.518, 1.076, and 0.976), respectively. Conversely, for the 22 ”-” metrics, the region-weighted relative hourly rate of change averaged over 22 ”-” metrics were (0.20, 0.32 and 0.27), respectively.
Discussion
Acute vasodilatation caused consistent changes in a total of 72 ICP pulse morphological metrics. In addition, it appears that the P 2 sub-region responded to cerebral vascular changes in the most consistent way with the greatest changes as compared to P 1 and P 3 sub-regions. Information with regard to how ICP pulse morphology responds to vasodilatation and vasoconstriction may allow surrogate, continuous monitoring of the cerebral vasculature.
In summary, the present work provides positive preliminary results related to the hypothesis that the dilation/constriction of the cerebral vasculature results in detectable consistent changes in ICP morphological metrics. Acute vasodilatation caused consistent changes in a total of 72 ICP pulse morphological metrics. In addition, it appears that the P 2 sub-region responded to cerebral vascular changes in the most consistent way with the greatest changes as compared to P 1 and P 3 sub-regions.
Morphological ICP waveform characteristics during slow waves
The diagnosis and management of NPH remain challenging mostly due to a lack of reliable methods of selecting candidates for shunt implantation and third ventriculostomy. There exists positive [51, 52] and negative [53-55] evidence that frequent presence of ICP slow waves predicts a positive outcome after shunt implant. ICP slow waves, also known as Lundberg’s B-waves, are defined as oscillations with a frequency of 2-0.5/minute and large amplitude [56]. There exists indirect evidence that certain characteristics of ICP slow waves may contain useful information for correctly diagnosing NPH and predicting shunt response. It was recently found that increased ICP pulse pressure amplitude has a predictive value for shunt response [9, 57]. In this section, we describe our recent attempt [13] to detect and separate periods of ICP slow waves (BW, "B-wave") from those of flat or nearly flat ICP (NW, "no wave") in an overnight ICP recording. We hypothesized that mean values and variations of ICP pulse morphological metrics extracted by the MOCAIP algorithm can be effectively used as input feature vectors to a classification algorithm to distinguish between periods of flat ICP (NW) and those with slow waves (BW). Such a classifier can then be used to construct an automated ICP slow wave recognition algorithm.
Materials and methods
Pre operative (shunt) overnight ICP recordings performed in 44 patients hospitalized at the UCLA Adult Hydrocephalus Center. Hydrocephalus was diagnosed for all patients. An intraparenchymal ICP sensor was inserted in the right frontal lobe and simultaneous recordings of ICP and ECG signals were performed at a sampling rate of either 400 Hz or 240 Hz. The signal recordings were visually screened by three independent experts to select both BW and NW patterns. Fig. 7 presents an example of ICP overnight monitoring where initially fairly stable ICP recording transforms into clearly distinguishable slow waves with relatively high amplitude and asymmetrical shape. In our study, small ICP slow waves with amplitude less than six mmHg were classified as NW pattern. As both patterns of ICP (BW and NW) might occur multiple times during overnight monitoring in the same patient, we included several selections from the same study. The total number of selected patterns was 276 (NW-131 and BW- 145).
Fig. 7. Illustration of B wave segment
Feature selection and classification
A total of 48 metrics (comprising 24 morphological metrics plus their standard deviation) were used to described the morphology of each ICP pulse.
To optimize the classification performance, three feature selection techniques (differential evolution (DE), discriminant analysis (DA) and analysis of variance based on Anova (V)) were applied to find an optimal set of MOCAIP metrics under different criteria. In addition, we selected three sets of metrics common to those found by combination of two selection methods, to be used as classification features (differential evolution and analysis of variance, discriminant analysis and analysis of variance, and combination of differential evolution and discriminant analysis).
A regularized linear quadratic classifier was used discriminate between BW from NW based on morphological ICP features. We repeated classification experiment seven times: first time for the set of optimal ICP metrics chosen by DE algorithm, second time for the set of ICP metrics selected based on analysis of variance (V), the third time for these metrics picked by step-wise DA approach, the next three runs were repeated for the metrics overlapped two sets: DE+V, DA+V and DE+DA, and finally we used only one parameter — S.D. of mean ICP to confirm possible advantage of using combination of metrics over single metric (SM). To compare the performance of classification for different sets of metrics, we ran the bootstrapping procedure 25 times to calculate average and S.D. of performance metrics including Se, Spe, PPV and Ac.
Results and discussion
Results indicate that the changes in six morphological metrics (S.D. of: dP 2, dP 12 ,dP 13, Curv T, dICP, mICP) were sufficient to distinguish between BW from NW with high specificity 96.2% and acceptable accuracy 88.9%.
Based on our experiments, using DE in conjunction with Anova to derive the final set of metrics leads to the best classification results. The combination of methods: DA+V as well as DE+DA did not improve the classification performance. Combining DE and V methods appeared to be complementary in our study. Since accuracy of classification experiment for Anova+DE is the highest (88.9%) and the number of input metrics is reasonable small (six metrics), we ranked this method as the best for separating BW from NW.
The final list of six metrics selected by DE+V (S.D. of: dP2, dP12, dP13, Curv T, dICP, mICP) reflects the most salient changes associated with ICP increase due to slow waves. We found that amplitude of P 2 component notably increases during ICP slow wave occurrence (hence S.D. of dP 2 ), which is also associated with increase in total curvature of the pulse (hence S.D. of CurvT ). Amplitude of Pa, most likely related to systole of arterial pressure [58], shows a moderate increase during BW in comparison with both dP 2 and dP 3 (hence S.D. of dP 12, S.D. of dP 13 ).
Predicting lumbar drain outcome from ICP morphology
Due to the complication rate and malfuction assoicated with existing shunt technologies, differentiating patient with NPH that will benefit from shunt implantation is significant. Extended lumbar drain (LD) for 72 hours has become a popular pre-shunt workup in many neurosurgical centers [59-61]. Its popularity stems from the findings of an excellent study which demonstrated the high specificity and sensitivity of predicting shunt response based on LD test outcome [60, 62]. However, the mechanistic relationship between positive LD outcome and shunt response remains to be elucidated. Therefore, many centers also employ additional tests to assess patients for predicting their shunt response. Overnight ICP monitoring is one of such tests. It has been reported in many studies that overnight ICP monitoring is able to reveal many phenomena that a short-term ICP monitoring or lumbar puncture cannot reveal, e.g., large but slow oscillations of ICP that are termed B-waves. However, the capability of overnight ICP monitoring to predict shunt response remains controversial. [51, 53-55].
Given these existing contradicting results, elucidating the potential prognostic value of overnight ICP monitoring is significant. Overnight ICP monitoring could be an important economic alternative to LD because it requires a shorter hospital stay for patients if its prognostic value can be fully explored. Unfortunately, many existing analysis methods applied to overnight ICP recordings are very subjective and usually extract limited amounts of information from overnight ICP recordings. On the other hand, a few studies utilizing more advanced ICP signal analysis methods [7, 10] have demonstrated that the amplitude of ICP pulse has excellent predictive value for shunt response. In addition to lack of complex analysis methods of overnight ICP, a potential limit to the existing studies that use shunt response as an end-point is that such outcome is also determined by many other post-implantation factors not necessarily associated with overnight ICP characteristics.
Therefore, this work aims to develop an automated method of feature extraction and decision rule construction from overnight ICP recording. This method is used to assess the predictive accuracy of LD outcome instead of shunt outcome.
Specifically, we used the MOCAIP algorithm to analyze the overnight ICP recordings. MOCAIP was applied to consecutive short segments of an ICP recording resulting in 128 metrics per each segment. Then various feature functions were designed to summarize the distribution of the 128 metrics from all the segments per each ICP recording. The predictive value of each feature-metric pair was then assessed using the area under curve [60] of the receiver operator characteristic (ROC) curve. Based on the ROC curve, a simple decision rule involving one metric can be derived for predicting LD outcome. To further improve the performance, we proposed an automated way to combine two such rules.
Materials and methods
The present retrospective study involved 54 patients undergoing pre-shunt workup that included overnight ICP monitoring and extended lumbar drain (three-day) while hospitalized at the UCLA Adult Hydrocephalus Center. An intraparenchymal ICP microsensor (Codman and Schurtleff, Raynaud, MA) was inserted in the right frontal lobe and monitoring started at least one night before the placement of the LD. Continuous waveform data including ECG and ICP was captured using the BedMaster system with a sampling rate of 240 Hz. Patients were assessed both pre-LD and post-LD before discharge from the hospital by ten meter walking exam, and an NPH routine assessment which includes the Mini-mental state examination (MMSE). The LD outcome used in the present work was retrospectively collected as indicated from the clinical report of the follow-up visit after the LD procedure and predominantly focused on the improvement in gait. The average age and standard deviation of the 54 patients is 72.05 ± 9.63 years respectively, with a gender distribution of 35 males and 19 females. Among the patient cohort, 12 patients (seven males and five females) showed no improvement in gait following the procedure.
Feature functions
Once the metrics for each dominant pulse have been computed an additional processing step is used to summarize each of the 128 MOCAIP metrics for one overnight ICP recording via five feature functions. For example, if a patient has ten hours of continuous ICP data there will be approximately 1200 dominant pulses, for each dominant pulse there is 128 MOCAIP metrics, each of the MOCAIP metrics can be summarized with the five feature functions (below). There is no prior knowledge with regard to the best way of summarizing an overnight ICP recording. Therefore, it is necessary to allow for an automated process to identify the best candidates. We have evaluated the following feature functions:
Average feature: This feature function simply calculates the average of each individual metric across one overnight ICP recording.
Standard deviation feature: This function calculates the standard deviation of each individual metric across one overnight ICP recording.
Percentage feature: This function calculates the percentage of time when a metric is greater than a threshold. This threshold is determined by pooling data from all patients and then determined as the average of the corresponding MOCAIP metric.
Percentage of standard deviation feature: This function calculates the percentage of time of the standard deviation of MOCAIP metrics greater than a threshold calculated from a five-minute ICP. The threshold was determined as the standard deviation of pooled MOCAIP metrics from all patients.
Range feature: This function calculates the difference between the 95 percentile and the 5 percentile of each individual metric of an overnight ICP recording.
Optimal single-metric rule
After applying a feature function, an overnight ICP recording is reduced to a vector of 128 metric-feature pairs. We next seek for a single-metric rule to predict LD outcome in the following form: if a metric-feature of an overnight recording is greater (or smaller) than a threshold, then LD outcome will be positive. Note, rules published in several existing studies [10, 63] can be considered as specific instances of the above form. For each metric-feature, we generate a sequence of threshold values by pooling all the data. This sequence of threshold values can be used to generate two ROC curves for each metric-feature, one of which corresponds to the greater than situation and another of which corresponds to the less than situation. Then we retain the ROC that has an AUC greater than 0.5 and discard the other for each metric. Next, for each feature the optimal metric is defined as the one with the greatest AUC. The impact of the optimal metric selection (as a function of AUC) is explained in the results section. Following the optimal metric determination for each feature, one natural step further is to determine if one can combine two such rules to obtain a better performance (a combination of two of the five features described above). Considering the combination of two features (A and B) with the corresponding set of false positive, true positive, false negative, and true negative cases represented (from rule A) as FPA, TPA, FNA, TNA, respectively, the notation is analogous for rule B. Furthermore, we shall consider two possible combination operations: AND and OR. If the OR operator is used for combination, then the resultant sets of false positive, true positive, false negative, and true negative are FPA U FPB, TPA U TPB, FNA Π FNB, and TNA Π TNB. On the other hand, if the AND operator is used for combination, then the corresponding sets are FPA Π FPB, TPA Π TPB, FNA U FNB, and TNA U TNB. Based on this analysis, the accuracy of the combined predictive rules can be calculated using their respective true positive and true negative values along with the total number of patients. Therefore, one can choose the combinations with the maximal accuracy.
Data analysis protocol
In this section, we summarize the data analysis protocol used in the present work to determine the best rule combination and its accuracy. The following steps were taken to analyze the 54 overnight ICP recordings:
1. Each recording is analyzed using the MOCAIP algorithm on consecutive 30-second segments.
2. Resultant MOCAIP metrics are then manually checked for any errors in recognizing legitimate dominant pulse and placement of the landmarks. This step is facilitated by using software developed in house.
3. Generate the threshold sequence for each metric that will be used to generate ROC curve. The i-th value of the sequence of thresholds is calculated for each metric-feature by the following equations: (i-1) where L is the mean plus the ten standard deviation of the metric-feature divided by the number of steps, which is 100 in the present work.
4. Each value of the threshold sequence is used to form two simple rules (greater than and less than), which are then used to assess the 54 cases. The corresponding false positive rate (FPR) and true positive rate (TPR) are obtained. After sweeping through the sequence, two ROC curves are obtained but only the one with AUC greater 0.5 is retained.
5. Determine the optimal metric-feature. This is achieved by selecting one metric-feature that has the largest AUC per each feature function evaluated. In other words, for each feature defined above, the metric (out of the 128 calculated by MOCAIP) with the greatest AUC is defined as the optimal metric-feature (five total, one for each feature).
6. Determine the rule. For the optimal metric-feature, the optimal point on the ROC curve is selected as the operating point, which gives a false positive rate less than 0.35.
7. Then the accuracy of the ten pairs (two combination rules and five feature) are calculated.
8. The best rule combination is then determined as the one with the greatest accuracy. 7.5 Results
Panel F of Fig. 8 displays ROC curves of each of the 128 metric-features (average feature function is used here as an example). The ROC curve shown in bold represents the metric with the greatest AUC; which was selected as the optimal metric-feature for rule construction. The ROC curves of optimal metric-pair corresponding to each of the five feature functions (A: Average feature function; B: Standard deviation feature function; C: Percentage of average feature function; D: Percentage of five-minute standard deviation feature function; E: Inter 5-95 percentile feature function) are shown in Panels A through E of Fig. 8 where the selected MOCAIP metric is also spelled out. On each curve, the operating point is circled, which corresponds to the particular threshold value selected for rule construction.
In summary, the individual metric-feature rules found in the present work are: 1.If standard deviation of RLv2p2Lp1p2 of overnight ICP recording is > 0.0547, then patient will respond to LD. 2.If average RCurvp3Curvv2 of overnight ICP recording is > 0.9808, then patient will respond to LD. 3.If percentage of RC1 of overnight ICP recording greater than 8.76 is < 41.48%, then patient will respond to LD. 4.If percentage of five minute segments of ICP having a standard deviation of RRC3k2 greater than 57.68 is > 0.49%, then patient will respond to LD drain. 5.If the inter 95-5 quartile range of RCurvp2Curvp3 of overnight ICP recording is < 64.25, then patient will respond to LD. The accuracy for the corresponding features is: 70.4%, 72.2%, 74.1%, 72.2%, and 79.6% respectively. Finally, the OR combination of rules 4 and 5 achieves the best accuracy of 88.9%.
Fig. 8. Illustration of LD ROCs
Discussion
We have demonstrated a systemic way of deriving two-rule combination of simple decision rules from overnight ICP recording to predict the corresponding outcome of three-day LD for patients with NPH clinical triad undergoing pre-shunt evaluation. Using a cohort of 54 patients, we are able to find optimal two-rule combination that reaches an accuracy of 88.9%. This rule can be explained in plain English such that it can be readily communicated to non-technical clinicians and patients. Although further validation with large patient cohort is needed, technically, the rules found in the present work in combination with an automated analysis of overnight ICP using the existing MOCAIP algorithm can readily be built into a decision support system of NPH diagnosis and evaluation that has the potential to reduce the time and cost associated with the existing three-day LD procedure.
A systemic way of discovering predictive rules is needed if a large number of ICP pulse morphological metrics are involved to construct rules. Compared to the existing studies that focus on either mICP or ICP pulse amplitude, the increased number of ICP metrics demands an objective way of discovering rules to guarantee optimality and avoid bias. Indeed, the five individual optimal metrics found in the present work do not include either mean ICP or ICP wave amplitude demonstrating the potential power of adopting a more comprehensive ICP pulse morphology characterization.
The proposed rule discovery framework can be easily extended in several aspects. We have proposed five simple feature functions to summarize an overnight ICP recording. More complex feature function can certainly be implemented in future work. In particular, we believe that feature functions characterizing the relationship among MOCAIP metrics could potentially offer better predictive power as compared to single-metric feature functions used here. Furthermore, we have only studied the combination of two rules, one could readily derive the equations to calculate the accuracy of combining multiple rules and check if the predictive accuracy can be further improved.
One fundamental limitation of the present work is the lack of cases to conduct independent evaluation of the discovered rules. It therefore remains to be demonstrated whether the same level of predictive accuracy can be retained when applying the rules discovered using our dataset to process data from other centers and how the data from multi-centers can be utilized to refine the rules. The present work has clearly demonstrated the technical feasibility of such future studies and hopefully more precise and robust predictive rules can be discovered through multi-center collaboration and the adoption of advanced ICP signal analysis and data mining methods.
Conclusions
Pulse pressure amplitude has been linked to several conditions with promising results in NPH for predicting shunt responsiveness [9, 10]. Work has also been done in chronic headaches; again, showing the usefulness of pulse pressure amplitude. Recent, detailed pulse pressure morphology analysis by our group has shown promise as a possible indicator of low global cerebral blood perfusion [11]. The resistance from many clinicians and researchers is the unknown relationship between these pulse pressure metrics (MOCAIP) and their physiologic meaning. Therefore, the additional information provided by knowing the origins of these pulse pressure features would help bridge the gap between a strictly data mining approach and physiologic meaning. Finally, the importance of this work was confirmed in a recent review article by Wagshul et al.:
"Given the success of invasive pulsatility measurements in clinical prognosis, studies which can provide a link between changes in pulse pressure and changes in non-invasive TCD- or MRI-based measures of pulsatility will be particularly valuable." [64]
