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

Ranard, Benjamin L / Megjhani, Murad / Terilli, Kalijah / Yarmohammadi, Hirad / Ausiello, John / Park, Soojin

2022 Volume 71, Page(s) 154114

Abstract: 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/ ...

Abstract	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.
MeSH term(s)	Autopsy ; COVID-19 ; Death, Sudden, Cardiac/epidemiology ; Heart Rate ; Humans ; Risk Factors ; SARS-CoV-2
Language	English
Publishing date	2022-07-18
Publishing country	United States
Document type	Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Journal Article ; Research Support, U.S. Gov't, P.H.S.
ZDB-ID	632818-0
ISSN	1557-8615 ; 0883-9441
ISSN (online)	1557-8615
ISSN	0883-9441
DOI	10.1016/j.jcrc.2022.154114
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Article ; Online: A Deep Learning Framework for Deriving Noninvasive Intracranial Pressure Waveforms from Transcranial Doppler.

Megjhani, Murad / Terilli, Kalijah / Weinerman, Bennett / Nametz, Daniel / Kwon, Soon Bin / Velazquez, Angela / Ghoshal, Shivani / Roh, David J / Agarwal, Sachin / Connolly, E Sander / Claassen, Jan / Park, Soojin

Annals of neurology

2023 Volume 94, Issue 1, Page(s) 196–202

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Humans ; Intracranial Pressure/physiology ; Deep Learning ; Cerebrovascular Circulation/physiology ; Blood Pressure/physiology ; Intracranial Hypertension/etiology ; Ultrasonography, Doppler, Transcranial/adverse effects
Language	English
Publishing date	2023-06-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	80362-5
ISSN	1531-8249 ; 0364-5134
ISSN (online)	1531-8249
ISSN	0364-5134
DOI	10.1002/ana.26682
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Article ; Online: Automatic identification of intracranial pressure waveform during external ventricular drainage clamping: segmentation via wavelet analysis.

Megjhani, Murad / Terilli, Kalijah / Kwon, Soon Bin / Nametz, Daniel / Weinerman, Bennett / Velazquez, Angela / Ghoshal, Shivani / Roh, David / Agarwal, Sachin / Connolly, E Sander / Claassen, Jan / Park, Soojin

Physiological measurement

2023 Volume 44, Issue 6

Abstract: ... ...

Abstract	Objective
MeSH term(s)	Female ; Humans ; Male ; Constriction ; Intracranial Pressure ; Subarachnoid Hemorrhage ; Wavelet Analysis
Language	English
Publishing date	2023-07-04
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1149545-5
ISSN	1361-6579 ; 0967-3334
ISSN (online)	1361-6579
ISSN	0967-3334
DOI	10.1088/1361-6579/acdf3b
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Article: Use of Clustering to Investigate Changes in Intracranial Pressure Waveform Morphology in Patients with Ventriculitis.

Megjhani, Murad / Terilli, Kalijah / Kaplan, Aaron / Wallace, Brendan K / Alkhachroum, Ayham / Hu, Xiao / Park, Soojin

Acta neurochirurgica. Supplement

2021 Volume 131, Page(s) 59–62

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Anti-Bacterial Agents ; Cerebral Ventriculitis/diagnosis ; Cluster Analysis ; Drainage ; Humans ; Intracranial Pressure
Chemical Substances	Anti-Bacterial Agents
Language	English
Publishing date	2021-04-09
Publishing country	Austria
Document type	Journal Article
ISSN	0065-1419
ISSN	0065-1419
DOI	10.1007/978-3-030-59436-7_13
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Article: Identification of Endotypes of Hospitalized COVID-19 Patients.

Ranard, Benjamin L / Megjhani, Murad / Terilli, Kalijah / Doyle, Kevin / Claassen, Jan / Pinsky, Michael R / Clermont, Gilles / Vodovotz, Yoram / Asgari, Shadnaz / Park, Soojin

Frontiers in medicine

2021 Volume 8, Page(s) 770343

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2021-11-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2775999-4
ISSN	2296-858X
ISSN	2296-858X
DOI	10.3389/fmed.2021.770343
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Article ; Online: Predicting Shunt Dependency from the Effect of Cerebrospinal Fluid Drainage on Ventricular Size.

Rubinos, Clio / Kwon, Soon Bin / Megjhani, Murad / Terilli, Kalijah / Wong, Brenda / Cespedes, Lizbeth / Ford, Jenna / Reyes, Renz / Kirsch, Hannah / Alkhachroum, Ayham / Velazquez, Angela / Roh, David / Agarwal, Sachin / Claassen, Jan / Connolly, E Sander / Park, Soojin

Neurocritical care

2022 Volume 37, Issue 3, Page(s) 670–677

Abstract: 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 ... ...

Abstract	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).
MeSH term(s)	Humans ; Retrospective Studies ; Prospective Studies ; Ventriculoperitoneal Shunt ; Hydrocephalus/surgery ; Cerebrospinal Fluid Leak ; Subarachnoid Hemorrhage/surgery ; Drainage/methods ; Cerebrospinal Fluid Shunts
Language	English
Publishing date	2022-06-25
Publishing country	United States
Document type	Observational Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2381896-7
ISSN	1556-0961 ; 1541-6933
ISSN (online)	1556-0961
ISSN	1541-6933
DOI	10.1007/s12028-022-01538-8
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Article ; Online: Optimal Cerebral Perfusion Pressure and Brain Tissue Oxygen in Aneurysmal Subarachnoid Hemorrhage.

Megjhani, Murad / Weiss, Miriam / Ford, Jenna / Terilli, Kalijah / Kastenholz, Nick / Nametz, Daniel / Kwon, Soon Bin / Velazquez, Angela / Agarwal, Sachin / Roh, David J / Conzen-Dilger, Catharina / Albanna, Walid / Veldeman, Michael / Connolly, E Sander / Claassen, Jan / Aries, Marcel / Schubert, Gerrit A / Park, Soojin

Stroke

2022 Volume 54, Issue 1, Page(s) 189–197

Abstract: 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 (PbtO: Methods: We performed a ... ...

Abstract	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 (PbtO 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 PbtO 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 PbtO Conclusions: We found a nonlinear relationship between PbtO
MeSH term(s)	Humans ; Subarachnoid Hemorrhage ; Retrospective Studies ; Oxygen ; Brain/diagnostic imaging ; Brain Ischemia ; Cerebral Infarction ; Intracranial Pressure ; Cerebrovascular Circulation/physiology ; Hypoxia ; Brain Injuries, Traumatic/diagnosis
Chemical Substances	Oxygen (S88TT14065)
Language	English
Publishing date	2022-10-31
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	80381-9
ISSN	1524-4628 ; 0039-2499 ; 0749-7954
ISSN (online)	1524-4628
ISSN	0039-2499 ; 0749-7954
DOI	10.1161/STROKEAHA.122.040339
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Article ; Online: Dynamic Intracranial Pressure Waveform Morphology Predicts Ventriculitis.

Megjhani, Murad / Terilli, Kalijah / Kalasapudi, Lakshman / Chen, Justine / Carlson, John / Miller, Serenity / Badjatia, Neeraj / Hu, Peter / Velazquez, Angela / Roh, David J / Agarwal, Sachin / Claassen, Jan / Connolly, E S / Hu, Xiao / Morris, Nicholas / Park, Soojin

Neurocritical care

2021 Volume 36, Issue 2, Page(s) 404–411

Abstract: 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: ... ...

Abstract	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.
MeSH term(s)	Catheters ; Cerebral Ventriculitis/cerebrospinal fluid ; Cerebral Ventriculitis/diagnosis ; Drainage ; Humans ; Intracranial Pressure ; ROC Curve
Language	English
Publishing date	2021-07-30
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2381896-7
ISSN	1556-0961 ; 1541-6933
ISSN (online)	1556-0961
ISSN	1541-6933
DOI	10.1007/s12028-021-01303-3
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Article ; Online: Dynamic Detection of Delayed Cerebral Ischemia: A Study in 3 Centers.

Megjhani, Murad / Terilli, Kalijah / Weiss, Miriam / Savarraj, Jude / Chen, Li Hui / Alkhachroum, Ayham / Roh, David J / Agarwal, Sachin / Connolly, E Sander / Velazquez, Angela / Boehme, Amelia / Claassen, Jan / Choi, HuiMahn A / Schubert, Gerrit A / Park, Soojin

Stroke

2021 Volume 52, Issue 4, Page(s) 1370–1379

Abstract: 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 ... ...

Abstract	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 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.
MeSH term(s)	Aged ; Brain Ischemia/diagnosis ; Brain Ischemia/etiology ; Female ; Humans ; Machine Learning ; Male ; Middle Aged ; Neurophysiological Monitoring ; Risk Factors ; Subarachnoid Hemorrhage/complications
Language	English
Publishing date	2021-02-18
Publishing country	United States
Document type	Journal Article ; Multicenter Study ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	80381-9
ISSN	1524-4628 ; 0039-2499 ; 0749-7954
ISSN (online)	1524-4628
ISSN	0039-2499 ; 0749-7954
DOI	10.1161/STROKEAHA.120.032546
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Article ; Online: An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms.

Megjhani, Murad / Alkhachroum, Ayham / Terilli, Kalijah / Ford, Jenna / Rubinos, Clio / Kromm, Julie / Wallace, Brendan K / Connolly, E Sander / Roh, David / Agarwal, Sachin / Claassen, Jan / Padmanabhan, Raghav / Hu, Xiao / Park, Soojin

Physiological measurement

2019 Volume 40, Issue 1, Page(s) 15002

Abstract: Objective: Intracranial pressure (ICP) is an important and established clinical measurement that is used in the management of severe acute brain injury. ICP waveforms are usually triphasic and are susceptible to artifact because of transient catheter ... ...

Abstract	Objective: Intracranial pressure (ICP) is an important and established clinical measurement that is used in the management of severe acute brain injury. ICP waveforms are usually triphasic and are susceptible to artifact because of transient catheter malfunction or routine patient care. Existing methods for artifact detection include threshold-based, stability-based, or template matching, and result in higher false positives (when there is variability in the ICP waveforms) or higher false negatives (when the ICP waveforms lack complete triphasic components but are valid). Approach: We hypothesized that artifact labeling of ICP waveforms can be optimized by an active learning approach which includes interactive querying of domain experts to identify a manageable number of informative training examples. Main results: The resulting active learning based framework identified non-artifactual ICP pulses with a superior AUC of 0.96 + 0.012, compared to existing methods: template matching (AUC: 0.71 + 0.04), ICP stability (AUC: 0.51 + 0.036) and threshold-based (AUC: 0.5 + 0.02). Significance: The proposed active learning framework will support real-time ICP-derived analytics by improving precision of artifact-labelling.
MeSH term(s)	Artifacts ; Brain Injuries/diagnosis ; Brain Injuries/physiopathology ; False Positive Reactions ; Female ; Humans ; Intracranial Pressure ; Machine Learning ; Male ; Middle Aged ; Signal Processing, Computer-Assisted
Language	English
Publishing date	2019-01-18
Publishing country	England
Document type	Journal Article
ZDB-ID	1149545-5
ISSN	1361-6579 ; 0967-3334
ISSN (online)	1361-6579
ISSN	0967-3334
DOI	10.1088/1361-6579/aaf979
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