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. 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 ejection fraction (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/m2, with ranging from 2.6 to 4.77 L/min/m2. The average PAT was 0.31±0.12 seconds. 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.

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 mathematical model of SARS-CoV-2 immunity predicts paxlovid rebound.

Nirmatrelvir/ritonavir (Paxlovid), an oral antiviral medication targeting SARS-CoV-2, remains an important treatment for COVID-19. Initial studies of nirmatrelvir/ritonavir were performed in SARS-CoV-2 unvaccinated patients without prior confirmed SARS-CoV-2 infection; however, most individuals have now either been vaccinated and/or have experienced SARS-CoV-2 infection. After nirmatrelvir/ritonavir became widely available, reports surfaced of "Paxlovid rebound," a phenomenon in which symptoms (and SARS-CoV-2 test positivity) would initially resolve, but after finishing treatment, symptoms and test positivity would return. We used a previously described parsimonious mathematical model of immunity to SARS-CoV-2 infection to model the effect of nirmatrelvir/ritonavir treatment in unvaccinated and vaccinated patients. Model simulations show that viral rebound after treatment occurs only in vaccinated patients, while unvaccinated (SARS-COV-2 naïve) patients treated with nirmatrelvir/ritonavir do not experience any rebound in viral load. This work suggests that an approach combining parsimonious models of the immune system could be used to gain important insights in the context of emerging pathogens.

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.

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.

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.

Classification of Level of Consciousness in a Neurological ICU Using Physiological Data.

BACKGROUND: Impaired consciousness is common in intensive care unit (ICU) patients, and an individual's degree of consciousness is crucial to determining their care and prognosis. However, there are no methods that continuously monitor consciousness and alert clinicians to changes. We investigated the use of physiological signals collected in the ICU to classify levels of consciousness in critically ill patients. METHODS: We studied 61 patients with subarachnoid hemorrhage (SAH) and 178 patients with intracerebral hemorrhage (ICH) from the neurological ICU at Columbia University Medical Center in a retrospective observational study of prospectively collected data. The level of consciousness was determined on the basis of neurological examination and mapped to comatose, vegetative state or unresponsive wakefulness syndrome (VS/UWS), minimally conscious minus state (MCS-), and command following. For each physiological signal, we extracted time-series features and performed classification using extreme gradient boosting on multiple clinically relevant tasks across subsets of physiological signals. We applied this approach independently on both SAH and ICH patient groups for three sets of variables: (1) a minimal set common to most hospital patients (e.g., heart rate), (2) variables available in most ICUs (e.g., body temperature), and (3) an extended set recorded mainly in neurological ICUs (absent for the ICH patient group; e.g., brain temperature). RESULTS: On the commonly performed classification task of VS/UWS versus MCS-, we achieved an area under the receiver operating characteristic curve (AUROC) in the SAH patient group of 0.72 (sensitivity 82%, specificity 57%; 95% confidence interval [CI] 0.63-0.81) using the extended set, 0.69 (sensitivity 83%, specificity 51%; 95% CI 0.59-0.78) on the variable set available in most ICUs, and 0.69 (sensitivity 56%, specificity 78%; 95% CI 0.60-0.78) on the minimal set. In the ICH patient group, AUROC was 0.64 (sensitivity 56%, specificity 65%; 95% CI 0.55-0.74) using the minimal set and 0.61 (sensitivity 50%, specificity 80%; 95% CI 0.51-0.71) using the variables available in most ICUs. CONCLUSIONS: We find that physiological signals can be used to classify states of consciousness for patients in the ICU. Building on this with intraday assessments and increasing sensitivity and specificity may enable alarm systems that alert physicians to changes in consciousness and frequent monitoring of consciousness throughout the day, both of which may improve patient care and outcomes.

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 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.

Heart rate variability and adrenal size provide clues to sudden cardiac death in hospitalized COVID-19 patients.

PURPOSE: To examine the association between a measure of heart rate variability and sudden cardiac death (SCD) in COVID-19 patients. METHODS: Patients with SARS-COV-2 infection admitted to Columbia University Irving Medical Center who died between 4/25/2020 and 7/14/2020 and had an autopsy were examined for root mean square of successive differences (RMSSD), organ weights, and evidence of SCD. RESULTS: Thirty COVID-19 patients were included and 12 had SCD. The RMSSD over 7 days without vs with SCD was median 0.0129 (IQR 0.0074-0.026) versus 0.0098 (IQR 0.0056-0.0197), p < 0.0001. The total adjusted adrenal weight of the non-SCD group was 0.40 g/kg (IQR 0.35-0.55) versus 0.25 g/kg (IQR 0.21-0.31) in the SCD group, p = 0.0007. CONCLUSIONS: Hospitalized patients with COVID-19 who experienced SCD had lower parasympathetic activity (RMSSD) and smaller sized adrenal glands. Further research is required to replicate these findings.

Intraarterial Nimodipine Versus Induced Hypertension for Delayed Cerebral Ischemia: A Modified Treatment Protocol.

BACKGROUND: Rescue treatment for delayed cerebral ischemia (DCI) after subarachnoid hemorrhage can include induced hypertension (iHTN) and, in refractory cases, endovascular approaches, of which selective, continuous intraarterial nimodipine (IAN) is one variant. The combination of iHTN and IAN can dramatically increase vasopressor demand. In case of unsustainable doses, iHTN is often prioritized over IAN. However, evidence in this regard is largely lacking. We investigated the effects of a classical (iHTN+IAN) and modified (IANonly) treatment protocol for refractory DCI in an observational study. METHODS: Rescue treatment for DCI was initiated with iHTN (target >180 mm Hg systolic) and escalated to IAN in refractory cases. Until July 2018, both iHTN and IAN were offered in cases refractory to iHTN alone. After protocol modification, iHTN target was preemptively lowered to >120 mm Hg when IAN was initiated (IANonly). Primary outcome was noradrenaline demand. Secondary outcomes included noradrenaline-associated complications, brain tissue oxygenation, DCI-related infarction and favorable 6-month outcome (Glasgow Outcome Scale 4-5). RESULTS: N=29 and n=20 patients were treated according to the classical and modified protocol, respectively. Protocol modification resulted in a significant reduction of noradrenaline demand (iHTN+IAN 0.70±0.54 µg/kg per minute and IANonly 0.26±0.20 µg/kg per minute, P<0.0001) and minor complications (15.0% versus 48.3%, unadjusted odds ratio, 0.19 [95% CI, 0.05-0.79]; P<0.05) with comparable rates of major complications (20.0% versus 20.7%, odds ratio, 0.96 [0.23-3.95]; P=0.95). Incidence of DCI-related infarction (45.0% versus 41.1%, odds ratio, 1.16 [0.37-3.66]; P=0.80) and favorable clinical outcome (55.6% versus 40.0%, odds ratio, 1.88 [0.55-6.39]; P=0.32) were similar. Brain tissue oxygenation was significantly higher with IANonly (26.6±12.8, 39.6±15.4 mm Hg; P<0.01). CONCLUSIONS: Assuming the potential of iHTN to be exhausted in case of refractory hypoperfusion, additional IAN may serve as a last-resort measure to bridge hypoperfusion in the DCI phase. With close monitoring, preemptive lowering of pressure target after induction of IAN may be a safe alternative to alleviate total noradrenaline load and potentially reduce complication rate.

Predicting Shunt Dependency from the Effect of Cerebrospinal Fluid Drainage on Ventricular Size.

BACKGROUND: Prolonged external ventricular drainage (EVD) in patients with subarachnoid hemorrhage (SAH) leads to morbidity, whereas early removal can have untoward effects related to recurrent hydrocephalus. A metric to help determine the optimal time for EVD removal or ventriculoperitoneal shunt (VPS) placement would be beneficial in preventing the prolonged, unnecessary use of EVD. This study aimed to identify whether dynamics of cerebrospinal fluid (CSF) biometrics can temporally predict VPS dependency after SAH. METHODS: This was a retrospective analysis of a prospective, single-center, observational study of patients with aneurysmal SAH who required EVD placement for hydrocephalus. Patients were divided into VPS-dependent (VPS+) and non-VPS dependent groups. We measured the bicaudate index (BCI) on all available computed tomography scans and calculated the change over time (ΔBCI). We analyzed the relationship of ΔBCI with CSF output by using Pearson's correlation. A k-nearest neighbor model of the relationship between ΔBCI and CSF output was computed to classify VPS. RESULTS: Fifty-eight patients met inclusion criteria. CSF output was significantly higher in the VPS+ group in the 7 days post EVD placement. There was a negative correlation between delta BCI and CSF output in the VPS+ group (negative delta BCI means ventricles become smaller) and a positive correlation in the VPS- group starting from days four to six after EVD placement (p < 0.05). A weighted k-nearest neighbor model for classification had a sensitivity of 0.75, a specificity of 0.70, and an area under the receiver operating characteristic curve of 0.80. CONCLUSIONS: The correlation of ΔBCI and CSF output is a reliable intraindividual biometric for VPS dependency after SAH as early as days four to six after EVD placement. Our machine learning model leverages this relationship between ΔBCI and cumulative CSF output to predict VPS dependency. Early knowledge of VPS dependency could be studied to reduce EVD duration in many centers (intensive care unit length of stay).

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.

Optimal Cerebral Perfusion Pressure During Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage.

OBJECTIVES: The recommendation of induced hypertension for delayed cerebral ischemia treatment after aneurysmal subarachnoid hemorrhage has been challenged recently and ideal pressure targets are missing. A new concept advocates an individual cerebral perfusion pressure where cerebral autoregulation functions best to ensure optimal global perfusion. We characterized optimal cerebral perfusion pressure at time of delayed cerebral ischemia and tested the conformity of induced hypertension with this target value. DESIGN: Retrospective analysis of prospectively collected data. SETTING: University hospital neurocritical care unit. PATIENTS: Thirty-nine aneurysmal subarachnoid hemorrhage patients with invasive neuromonitoring (20 with delayed cerebral ischemia, 19 without delayed cerebral ischemia). INTERVENTIONS: Induced hypertension greater than 180 mm Hg systolic blood pressure. MEASUREMENTS AND MAIN RESULTS: Changepoint analysis was used to calculate significant changes in cerebral perfusion pressure, optimal cerebral perfusion pressure, and the difference of cerebral perfusion pressure and optimal cerebral perfusion pressure 48 hours before delayed cerebral ischemia diagnosis. Optimal cerebral perfusion pressure increased 30 hours before the onset of delayed cerebral ischemia from 82.8 ± 12.5 to 86.3 ± 11.4 mm Hg (p < 0.05). Three hours before delayed cerebral ischemia, a changepoint was also found in the difference of cerebral perfusion pressure and optimal cerebral perfusion pressure (decrease from -0.2 ± 11.2 to -7.7 ± 7.6 mm Hg; p < 0.05) with a corresponding increase in pressure reactivity index (0.09 ± 0.33 to 0.19 ± 0.37; p < 0.05). Cerebral perfusion pressure at time of delayed cerebral ischemia was lower than in patients without delayed cerebral ischemia in a comparable time frame (cerebral perfusion pressure delayed cerebral ischemia 81.4 ± 8.3 mm Hg, no delayed cerebral ischemia 90.4 ± 10.5 mm Hg; p < 0.05). Inducing hypertension resulted in a cerebral perfusion pressure above optimal cerebral perfusion pressure (+12.4 ± 8.3 mm Hg; p < 0.0001). Treatment response (improvement of delayed cerebral ischemia: induced hypertension+ [n = 15] or progression of delayed cerebral ischemia: induced hypertension- [n = 5]) did not correlate to either absolute values of cerebral perfusion pressure or optimal cerebral perfusion pressure, nor the resulting difference (cerebral perfusion pressure [p = 0.69]; optimal cerebral perfusion pressure [p = 0.97]; and the difference of cerebral perfusion pressure and optimal cerebral perfusion pressure [p = 0.51]). CONCLUSIONS: At the time of delayed cerebral ischemia occurrence, there is a significant discrepancy between cerebral perfusion pressure and optimal cerebral perfusion pressure with worsening of autoregulation, implying inadequate but identifiable individual perfusion. Standardized induction of hypertension resulted in cerebral perfusion pressures that exceeded individual optimal cerebral perfusion pressure in delayed cerebral ischemia patients. The potential benefit of individual blood pressure management guided by autoregulation-based optimal cerebral perfusion pressure should be explored in future intervention studies.

Dynamic Intracranial Pressure Waveform Morphology Predicts Ventriculitis.

BACKGROUND: Intracranial pressure waveform morphology reflects compliance, which can be decreased by ventriculitis. We investigated whether morphologic analysis of intracranial pressure dynamics predicts the onset of ventriculitis. METHODS: Ventriculitis was defined as culture or Gram stain positive cerebrospinal fluid, warranting treatment. We developed a pipeline to automatically isolate segments of intracranial pressure waveforms from extraventricular catheters, extract dominant pulses, and obtain morphologically similar groupings. We used a previously validated clinician-supervised active learning paradigm to identify metaclusters of triphasic, single-peak, or artifactual peaks. Metacluster distributions were concatenated with temperature and routine blood laboratory values to create feature vectors. A L2-regularized logistic regression classifier was trained to distinguish patients with ventriculitis from matched controls, and the discriminative performance using area under receiver operating characteristic curve with bootstrapping cross-validation was reported. RESULTS: Fifty-eight patients were included for analysis. Twenty-seven patients with ventriculitis from two centers were identified. Thirty-one patients with catheters but without ventriculitis were selected as matched controls based on age, sex, and primary diagnosis. There were 1590 h of segmented data, including 396,130 dominant pulses in patients with ventriculitis and 557,435 pulses in patients without ventriculitis. There were significant differences in metacluster distribution comparing before culture-positivity versus during culture-positivity (p < 0.001) and after culture-positivity (p < 0.001). The classifier demonstrated good discrimination with median area under receiver operating characteristic 0.70 (interquartile range 0.55-0.80). There were 1.5 true alerts (ventriculitis detected) for every false alert. CONCLUSIONS: Intracranial pressure waveform morphology analysis can classify ventriculitis without cerebrospinal fluid sampling.

Identification of Endotypes of Hospitalized COVID-19 Patients.

Background: Characterization of coronavirus disease 2019 (COVID-19) endotypes may help explain variable clinical presentations and response to treatments. While risk factors for COVID-19 have been described, COVID-19 endotypes have not been elucidated. Objectives: We sought to identify and describe COVID-19 endotypes of hospitalized patients. Methods: Consensus clustering (using the ensemble method) of patient age and laboratory values during admission identified endotypes. We analyzed data from 528 patients with COVID-19 who were admitted to telemetry capable beds at Columbia University Irving Medical Center and discharged between March 12 to July 15, 2020. Results: Four unique endotypes were identified and described by laboratory values, demographics, outcomes, and treatments. Endotypes 1 and 2 were comprised of low numbers of intubated patients (1 and 6%) and exhibited low mortality (1 and 6%), whereas endotypes 3 and 4 included high numbers of intubated patients (72 and 85%) with elevated mortality (21 and 43%). Endotypes 2 and 4 had the most comorbidities. Endotype 1 patients had low levels of inflammatory markers (ferritin, IL-6, CRP, LDH), low infectious markers (WBC, procalcitonin), and low degree of coagulopathy (PTT, PT), while endotype 4 had higher levels of those markers. Conclusions: Four unique endotypes of hospitalized patients with COVID-19 were identified, which segregated patients based on inflammatory markers, infectious markers, evidence of end-organ dysfunction, comorbidities, and outcomes. High comorbidities did not associate with poor outcome endotypes. Further work is needed to validate these endotypes in other cohorts and to study endotype differences to treatment responses.

Use of Clustering to Investigate Changes in Intracranial Pressure Waveform Morphology in Patients with Ventriculitis.

OBJECTIVE: This study aimed to examine whether changes in intracranial pressure (ICP) waveform morphologies can be used as a biomarker for early detection of ventriculitis. METHODS: Consecutive patients (N = 1653) were prospectively enrolled in a hemorrhage outcomes study from 2006 to 2018. Of these, 435 patients (26%) required external ventricular drains (EVDs) and 76 (17.5% of those with EVDs) had ventriculitis treated with antibiotics. Nineteen patients (25% of those with ventriculitis) showed culture-positive cerebrospinal fluid (CSF) and were included in the present analysis. CSF was routinely cultured three times per week and additionally if infection was suspected. EVDs were left open for drainage, with ICP assessed hourly by clamping. Using wavelet analysis, we extracted uninterrupted segments of ICP waveforms. We extracted dominant pulses from continuous high-resolution data, using morphological clustering analysis of intracranial pressure (MOCAIP). Then we applied k-means clustering, using the dynamic time warping distance to obtain morphologically similar groupings. Finally, metaclusters and further-split clusters (when equipoise existed) were categorized for broad comparison by clinician consensus. RESULTS: We extracted 275,911 dominant pulses from 459.9 h of EVD data. Of these, 112,898 pulses (40.9%) occurred before culture positivity, 41,300 pulses (15.0%) occurred during culture positivity, and 121,713 pulses (44.1%) occurred after it. K-means identified 20 clusters, which were further grouped into metaclusters: tri-/biphasic, single-peak, and artifactual waveforms. Prior to ventriculitis, 61.8% of dominant pulses were tri-/biphasic; this percentage reduced to 22.6% during ventriculitis and 28.4% after it (p < 0.0001). One day before the first positive cultures were collected, the distribution of metaclusters changed to include more single-peak and artifactual ICP waveforms (p < 0.0001). CONCLUSION: The distribution of ICP waveform morphology changes significantly prior to clinical diagnosis of ventriculitis and may be a potential biomarker.

Dynamic Detection of Delayed Cerebral Ischemia: A Study in 3 Centers.

BACKGROUND AND PURPOSE: Delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage negatively impacts long-term recovery but is often detected too late to prevent damage. We aim to develop hourly risk scores using routinely collected clinical data to detect DCI. METHODS: A DCI classification model was trained using vital sign measurements (heart rate, blood pressure, respiratory rate, and oxygen saturation) and demographics routinely collected for clinical care. Twenty-two time-varying physiological measures were computed including mean, SD, and cross-correlation of heart rate time series with each of the other vitals. Classification was achieved using an ensemble approach with L2-regularized logistic regression, random forest, and support vector machines models. Classifier performance was determined by area under the receiver operating characteristic curves and confusion matrices. Hourly DCI risk scores were generated as the posterior probability at time t using the Ensemble classifier on cohorts recruited at 2 external institutions (n=38 and 40). RESULTS: Three hundred ten patients were included in the training model (median, 54 years old [interquartile range, 45-65]; 80.2% women, 28.4% Hunt and Hess scale 4-5, 38.7% Modified Fisher Scale 3-4); 101 (33%) developed DCI with a median onset day 6 (interquartile range, 5-8). Classification accuracy before DCI onset was 0.83 (interquartile range, 0.76-0.83) area under the receiver operating characteristic curve. Risk scores applied to external institution datasets correctly predicted 64% and 91% of DCI events as early as 12 hours before clinical detection, with 2.7 and 1.6 true alerts for every false alert. CONCLUSIONS: An hourly risk score for DCI derived from routine vital signs may have the potential to alert clinicians to DCI, which could reduce neurological injury.

Machine Learning to Predict Delayed Cerebral Ischemia and Outcomes in Subarachnoid Hemorrhage.

OBJECTIVE: To determine whether machine learning (ML) algorithms can improve the prediction of delayed cerebral ischemia (DCI) and functional outcomes after subarachnoid hemorrhage (SAH). METHODS: ML models and standard models (SMs) were trained to predict DCI and functional outcomes with data collected within 3 days of admission. Functional outcomes at discharge and at 3 months were quantified using the modified Rankin Scale (mRS) for neurologic disability (dichotomized as good [mRS ≤ 3] vs poor [mRS ≥ 4] outcomes). Concurrently, clinicians prospectively prognosticated 3-month outcomes of patients. The performance of ML, SMs, and clinicians were retrospectively compared. RESULTS: DCI status, discharge, and 3-month outcomes were available for 399, 393, and 240 participants, respectively. Prospective clinician (an attending, a fellow, and a nurse) prognostication of 3-month outcomes was available for 90 participants. ML models yielded predictions with the following area under the receiver operating characteristic curve (AUC) scores: 0.75 ± 0.07 (95% confidence interval [CI] 0.64-0.84) for DCI, 0.85 ± 0.05 (95% CI 0.75-0.92) for discharge outcome, and 0.89 ± 0.03 (95% CI 0.81-0.94) for 3-month outcome. ML outperformed SMs, improving AUC by 0.20 (95% CI -0.02 to 0.4) for DCI, by 0.07 ± 0.03 (95% CI -0.0018 to 0.14) for discharge outcomes, and by 0.14 (95% CI 0.03-0.24) for 3-month outcomes and matched physician's performance in predicting 3-month outcomes. CONCLUSION: ML models significantly outperform SMs in predicting DCI and functional outcomes and has the potential to improve SAH management.