The oxygen reactivity index indicates disturbed local perfusion regulation after aneurysmal subarachnoid hemorrhage: an observational cohort study

BACKGROUND

Cerebral autoregulation (CA) can be impaired in patients with delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). The Pressure Reactivity Index (PRx, correlation of blood pressure and intracranial pressure) and Oxygen Reactivity Index (ORx, correlation of cerebral perfusion pressure and brain tissue oxygenation, PbtO2) are both believed to estimate CA. We hypothesized that CA could be poorer in hypoperfused territories during DCI and that ORx and PRx may not be equally effective in detecting such local variances.

METHODS

ORx and PRx were compared daily in 76 patients with aSAH with or without DCI until the time of DCI diagnosis. The ICP/PbtO2-probes of DCI patients were retrospectively stratified by being in or outside areas of hypoperfusion via CT perfusion image, resulting in three groups: DCI + /probe + (DCI patients, probe located inside the hypoperfused area), DCI + /probe-  (probe outside the hypoperfused area), DCI-  (no DCI).

RESULTS

PRx and ORx were not correlated (r = - 0.01, p = 0.56). Mean ORx but not PRx was highest when the probe was located in a hypoperfused area (ORx DCI + /probe + 0.28 ± 0.13 vs. DCI + /probe-  0.18 ± 0.15, p < 0.05; PRx DCI + /probe + 0.12 ± 0.17 vs. DCI + /probe-  0.06 ± 0.20, p = 0.35). PRx detected poorer autoregulation during the early phase with relatively higher ICP (days 1-3 after hemorrhage) but did not differentiate the three groups on the following days when ICP was lower on average. ORx was higher in the DCI + /probe + group than in the other two groups from day 3 onward. ORx and PRx did not differ between patients with DCI, whose probe was located elsewhere, and patients without DCI (ORx DCI + /probe-  0.18 ± 0.15 vs. DCI-  0.20 ± 0.14; p = 0.50; PRx DCI + /probe-  0.06 ± 0.20 vs. DCI-  0.08 ± 0.17, p = 0.35).

CONCLUSIONS

PRx and ORx are not interchangeable measures of autoregulation, as they likely measure different homeostatic mechanisms. PRx represents the classical cerebrovascular reactivity and might be better suited to detect disturbed autoregulation during phases with moderately elevated ICP. Autoregulation may be poorer in territories affected by DCI. These local perfusion disturbances leading up to DCI may be more readily detected by ORx than PRx. Further research should investigate their robustness to detect DCI and to serve as a basis for autoregulation-targeted treatment after aSAH.

Optimal Cerebral Perfusion Pressure and Brain Tissue Oxygen in Aneurysmal Subarachnoid Hemorrhage

BACKGROUND

Targeting a cerebral perfusion pressure optimal for cerebral autoregulation (CPPopt) has been gaining more attention to prevent secondary damage after acute neurological injury. Brain tissue oxygenation (PbtO2) can identify insufficient cerebral blood flow and secondary brain injury. Defining the relationship between CPPopt and PbtO2 after aneurysmal subarachnoid hemorrhage may result in (1) mechanistic insights into whether and how CPPopt-based strategies might be beneficial and (2) establishing support for the use of PbtO2 as an adjunctive monitor for adequate or optimal local perfusion.

METHODS

We performed a retrospective analysis of a prospectively collected 2-center dataset of patients with aneurysmal subarachnoid hemorrhage with or without later diagnosis of delayed cerebral ischemia (DCI). CPPopt was calculated as the cerebral perfusion pressure (CPP) value corresponding to the lowest pressure reactivity index (moving correlation coefficient of mean arterial and intracranial pressure). The relationship of (hourly) deltaCPP (CPP-CPPopt) and PbtO2 was investigated using natural spline regression analysis. Data after DCI diagnosis were excluded. Brain tissue hypoxia was defined as PbtO2 <20 mmHg.

RESULTS

One hundred thirty-one patients were included with a median of 44.0 (interquartile range, 20.8-78.3) hourly CPPopt/PbtO2 datapoints. The regression plot revealed a nonlinear relationship between PbtO2 and deltaCPP (P<0.001) with PbtO2 decrease with deltaCPP <0 mmHg and stable PbtO2 with deltaCPP ≥0mmHg, although there was substantial individual variation. Brain tissue hypoxia (34.6% of all measurements) was more frequent with deltaCPP <0 mmHg. These dynamics were similar in patients with or without DCI.

CONCLUSIONS

We found a nonlinear relationship between PbtO2 and deviation of patients' CPP from CPPopt in aneurysmal subarachnoid hemorrhage patients in the pre-DCI period. CPP values below calculated CPPopt were associated with lower PbtO2. Nevertheless, the nature of PbtO2 measurements is complex, and the variability is high. Combined multimodality monitoring with CPP/CPPopt and PbtO2 should be recommended to redefine individual pressure targets (CPP/CPPopt) and retain the option to detect local perfusion deficits during DCI (PbtO2), which cannot be fulfilled by both measurements interchangeably.

A Deep Learning Framework for Deriving Noninvasive Intracranial Pressure Waveforms from Transcranial Doppler

Increased intracranial pressure (ICP) causes disability and mortality in the neurointensive care population. Current methods for monitoring ICP are invasive. We designed a deep learning framework using a domain adversarial neural network to estimate noninvasive ICP, from blood pressure, electrocardiogram, and cerebral blood flow velocity. Our model had a mean of median absolute error of 3.88 ± 3.26 mmHg for the domain adversarial neural network, and 3.94 ± 1.71 mmHg for the domain adversarial transformers. Compared with nonlinear approaches, such as support vector regression, this was 26.7% and 25.7% lower. Our proposed framework provides more accurate noninvasive ICP estimates than currently available. ANN NEUROL 2023;94:196-202.

Non-invasive pulse arrival time is associated with cardiac index in pediatric heart transplant patients with normal ejection fraction

Objective.Cardiac Index (CI) is a key physiologic parameter to ensure end organ perfusion in the pediatric intensive care unit (PICU). Determination of CI requires invasive cardiac measurements and is not routinely done at the PICU bedside. To date, there is no gold standard non-invasive means to determine CI. This study aims to use a novel non-invasive methodology, based on routine continuous physiologic data, called Pulse Arrival Time (PAT) as a surrogate for CI in patients with normal Ejection Fraction (EF).Approach.Electrocardiogram (ECG) and photoplethysmogram (PPG) signals were collected from beside monitors at a sampling frequency of 250 samples per second. Continuous PAT, derived from the ECG and PPG waveforms was averaged per patient. Pearson's correlation coefficient was calculated between PAT and CI, PAT and heart rate (HR), and PAT and EF.Main Results.Twenty patients underwent right heart cardiac catheterization. The mean age of patients was 11.7 ± 5.4 years old, ranging from 11 months old to 19 years old, the median age was 13.4 years old. HR in this cohort was 93.8 ± 17.0 beats per minute. The average EF was 54.4 ± 9.6%. The average CI was 3.51 ± 0.72 l min-1m-2, with ranging from 2.6 to 4.77 l min-1m-2. The average PAT was 0.31 ± 0.12 s. Pearson correlation analysis showed a positive correlation between PAT and CI (0.57,p< 0.01). Pearson correlation between HR and CI, and correlation between EF and CI was 0.22 (p= 0.35) and 0.03 (p= 0.23) respectively. The correlation between PAT, when indexed by HR (i.e. PAT × HR), and CI minimally improved to 0.58 (p< 0.01).Significance.This pilot study demonstrates that PAT may serve as a valuable surrogate marker for CI at the bedside, as a non-invasive and continuous modality in the PICU. The use of PAT in clinical practice remains to be thoroughly investigated.

Suboptimal Cerebral Perfusion is Associated with Ischemia After Intracerebral Hemorrhage

BACKGROUND

Remote ischemic lesions on diffusion-weighted imaging (DWI) occur in one third of patients with intracerebral hemorrhage (ICH) and are associated with worse outcomes. The etiology is unclear and not solely due to blood pressure reduction. We hypothesized that impaired cerebrovascular autoregulation and hypoperfusion below individualized lower limits of autoregulation are associated with the presence of DWI lesions.

METHODS

This was a retrospective, single-center study of all primary ICH with intraparenchymal pressure monitoring within 10 days from onset and subsequent magnetic resonance imaging. Pressure reactivity index was calculated as the correlation coefficient between mean arterial pressure and intracranial pressure. Optimal cerebral perfusion pressure (CPPopt) is the cerebral perfusion pressure (CPP) with the lowest corresponding pressure reactivity index. The difference between CPP and CPPopt, time spent below the lower limit of autoregulation (LLA), and time spent above the upper limit of autoregulation (ULA) were calculated by using mean hourly physiologic data. Univariate associations between physiologic parameters and DWI lesions were analyzed by using binary logistic regression.

RESULTS

A total of 505 h of artifact-free data from seven patients without DWI lesions and 479 h from six patients with DWI lesions were analyzed. Patients with DWI lesions had higher intracranial pressure (17.50 vs. 10.92 mm Hg; odds ratio 1.14, confidence interval 1.01-1.29) but no difference in mean arterial pressure or CPP compared with patients without DWI lesions. The presence of DWI lesions was significantly associated with a greater percentage of time spent below the LLA (49.85% vs. 14.70%, odds ratio 5.77, confidence interval 1.88-17.75). No significant association was demonstrated between CPPopt, the difference between CPP and CPPopt, ULA, LLA, or time spent above the ULA between groups.

CONCLUSIONS

Blood pressure reduction below the LLA is associated with ischemia after acute ICH. Individualized, autoregulation-informed targets for blood pressure reduction may provide a novel paradigm in acute management of ICH and require further study.

Identification of Early Risk Factors for Mortality in Pediatric Veno-Arterial Extra Corporeal Membrane Oxygenation: The Patient Matters

Objective

Pediatric Veno-Arterial Extra Corporeal Membrane Oxygenation (VA ECMO) is a life saving technology associated with high mortality. A successful VA ECMO course requires attention to multiple aspects of patient care, including ECMO and patient parameters. Early, potentially modifiable, risk factors associated with patient mortality should be analyzed and adjusted for when assessing VA ECMO risk profiles.

Method

Retrospective single center experience of pediatric patients requiring VA ECMO from January 2021 to October 2023. Laboratory and ECMO flow parameters were extracted from the patients record and analyzed. Risk factors were analyzed using a Cox proportion hazard regression.

Main Results

There were 45 patients studied. Overall survival was 51%. Upon uncorrected analysis there were no significant differences between the patients who survived and those who died. Utilizing a Cox proportion hazard regression, platelet count, fibrinogen level and creatine level were significant risk factors within the first twenty-four hours of a patient's ECMO course.

Significance

Although we did not find a significant difference among ECMO flow parameters in this study, this work highlights that granular ECMO flow data can be incorporated to risk analysis profiles and potential modeling in pediatric VA ECMO. This study demonstrated, that when controlling for ECMO flow parameters, kidney dysfunction and clotting regulation remain key risk factors for pediatric VA ECMO mortality.

Vector Angle Analysis of Multimodal Neuromonitoring Data for Continuous Prediction of Delayed Cerebral Ischemia

BACKGROUND

Dysfunctional cerebral autoregulation often precedes delayed cerebral ischemia (DCI). Currently, there are no data-driven techniques that leverage this information to predict DCI in real time. Our hypothesis is that information using continuous updated analyses of multimodal neuromonitoring and cerebral autoregulation can be deployed to predict DCI.

METHODS

Time series values of intracranial pressure, brain tissue oxygenation, cerebral perfusion pressure (CPP), optimal CPP (CPPOpt), ΔCPP (CPP - CPPOpt), mean arterial pressure, and pressure reactivity index were combined and summarized as vectors. A validated temporal signal angle measurement was modified into a classification algorithm that incorporates hourly data. The time-varying temporal signal angle measurement (TTSAM) algorithm classifies DCI at varying time points by vectorizing and computing the angle between the test and reference time signals. The patient is classified as DCI+ if the error between the time-varying test vector and DCI+ reference vector is smaller than that between the time-varying test vector and DCI- reference vector. Finally, prediction at time point t is calculated as the majority voting over all the available signals. The leave-one-patient-out cross-validation technique was used to train and report the performance of the algorithms. The TTSAM and classifier performance was determined by balanced accuracy, F1 score, true positive, true negative, false positive, and false negative over time.

RESULTS

One hundred thirty-one patients with aneurysmal subarachnoid hemorrhage who underwent multimodal neuromonitoring were identified from two centers (Columbia University: 52 [39.7%], Aachen University: 79 [60.3%]) and included in the analysis. Sixty-four (48.5%) patients had DCI, and DCI was diagnosed 7.2 ± 3.3 days after hemorrhage. The TTSAM algorithm achieved a balanced accuracy of 67.3% and an F1 score of 0.68 at 165 h (6.9 days) from bleed day with a true positive of 0.83, false positive of 0.16, true negative of 0.51, and false negative of 0.49.

CONCLUSIONS

A TTSAM algorithm using multimodal neuromonitoring and cerebral autoregulation calculations shows promise to classify DCI in real time.

Pulse rate variability as a predictor for length of stay for patients with bronchiolitis in the pediatric intensive care unit

Patients admitted to pediatric Intensive Care Unit (PICU) due to bronchiolitis have unpredictable length of stay (LOS). The aim of this study is to observe the difference in the relationship between pulse rate variability (PRV) and heart rate variability (HRV) for patients with bronchiolitis admitted to the PICU and its association with LOS. The first 12 h of physiologic data after PICU admission were used for analysis. Electrocardiogram (ECG) and photoplethysmogram (PPG) were divided into non-overlapping 5-minute segments, and R-peak and PPG-peak were obtained to calculate PRV and HRV. Correlation was calculated between HRV and PRV for both PICU short-stay and long-stay groups. A total of 119 patients were included in this study, where 66 are short-stay and 53 are long-stay group. For both LOS groups, PRV and HRV parameters were significantly higher HRV parameters compared to PRV. SDSD, SDNN, RMSSD, pNN50, SD1, and SD2 were 13.72, 10.24, 13.72, 0.77, 9.7, 10.6, and 0.85 for HRV. For PRV it was 5.88, 4.83, 5.88, 0.75, 4.16, 5.28, and 0.85. However, in the comparison of the correlations between PRV and HRV parameters, the short-stay group had significantly higher correlation compared to the long-stay group. The correlations in the short-stay group are above 0.72-0.82, whereas for the long-stay group the correlation ranged from 0.29 to 0.67. This study demonstrates that the correlation between the PRV and HRV is lower in patients with longer length of stay, suggesting this can be a potential metric for LOS in PICU.

Automatic identification of intracranial pressure waveform during external ventricular drainage clamping: segmentation via wavelet analysis

Objective. The objective of this study is to develop and validate a method for automatically identifying segments of intracranial pressure (ICP) waveform data from external ventricular drainage (EVD) recordings during intermittent drainage and closure.Methods. The proposed method uses time-frequency analysis through wavelets to distinguish periods of ICP waveform in EVD data. By comparing the frequency compositions of the ICP signals (when the EVD system is clamped) and the artifacts (when the system is open), the algorithm can detect short, uninterrupted segments of ICP waveform from the longer periods of non-measurement data. The method involves applying a wavelet transform, calculating the absolute power in a specific range, using Otsu thresholding to automatically identify a threshold, and performing a morphological operation to remove small segments. Two investigators manually graded the same randomly selected one-hour segments of the resulting processed data. Performance metrics were calculated as a percentage.Results. The study analyzed data from 229 patients who had EVD placed following subarachnoid hemorrhage between June 2006 and December 2012. Of these, 155 (67.7%) were female and 62 (27%) developed delayed cerebral ischemia. A total of 45 150 h of data were segmented. 2044 one-hour segments were randomly selected and evaluated by two investigators (MM and DN). Of those, the evaluators agreed on the classification of 1556 one-hour segments. The algorithm was able to correctly identify 86% (1338 h) of ICP waveform data. 8.2% (128 h) of the time the algorithm either partially or fully failed to segment the ICP waveform. 5.4% (84 h) of data, artifacts were mistakenly identified as ICP waveforms (false positives).Conclusion. The proposed algorithm automates the identification of valid ICP waveform segments of waveform in EVD data and thus enables the inclusion in real-time data analysis for decision support. It also standardizes and makes research data management more efficient.

Heart rate and heart rate variability as a prognosticating feature for functional outcome after cardiac arrest: A scoping review

Background

Despite significant progress in cardiopulmonary resuscitation and post-cardiac arrest care, favorable outcome in out-of hospital sudden cardiac arrest patients remains low. One of the main reasons for mortality in these patients is withdrawal of life-sustaining treatment. There is a need for precise and equitable prognostication tools to support families in avoiding premature or inappropriate WLST. Heart rate (HR) and heart rate variability (HRV) have been noted for their association with outcome, and are positioned to be a useful modality for prognostication.

Objectives

The aim of this scoping review is to rigorously explore which electrocardiography features have been shown to predict functional outcome in post-cardiac arrest patients.

Methods

The search was performed in Pubmed, EMBASE, and SCOPUS for studies published from January 1, 2011, to September 29, 2022, including papers in English or Korean.

Results

Seven studies were included with a total of 1359 patients. Four studies evaluated HR, one study evaluated RR inverval, and two studies evaluated HRV. All studies were retrospective, with 3 multi-center and 4 single-center studies. All seven studies were inclusive of patients who underwent targeted temperature management (TTM) after cardiac arrest, and two studies included patients without TTM. Five studies used cerebral performance category to assess functional outcome, two studies used Glasgow outcome score, and one study used modified Rankin scale. Three studies measured outcome at hospital discharge, one study measured outcome at 14 days after return of spontaneous circulation, two studies measured outcome after 3 months, and one after 1 year. In all studies that evaluated HR, lower HR was associated with favorable functional outcome. Two studies found that higher complexity of HRV was associated with favorable functional outcome.

Conclusion

HR and HRV showed clear associations with functional outcome in patients after CA, but cinilcial utility for prognostication is uncertain.

A generalizable physiological model for detection of Delayed Cerebral Ischemia using Federated Learning

Delayed cerebral ischemia (DCI) is a complication seen in patients with subarachnoid hemorrhage stroke. It is a major predictor of poor outcomes and is detected late. Machine learning models are shown to be useful for early detection, however training such models suffers from small sample sizes due to rarity of the condition. Here we propose a Federated Learning approach to train a DCI classifier across three institutions to overcome challenges of sharing data across hospitals. We developed a framework for federated feature selection and built a federated ensemble classifier. We compared the performance of FL model to that obtained by training separate models at each site. FL significantly improved performance at only two sites. We found that this was due to feature distribution differences across sites. FL improves performance in sites with similar feature distributions, however, FL can worsen performance in sites with heterogeneous distributions. The results highlight both the benefit of FL and the need to assess dataset distribution similarity before conducting FL.