Abigail A Bucklin, Wolfgang Ganglberger, Syed A Quadri, Ryan A Tesh, Noor Adra, Madalena Da Silva Cardoso, Michael J Leone, Parimala V Krishnamurthy, Aashritha Hemmige, Subapriya Rajan, Ezhil Panneerselvam, Luis Paixao, Jasmine Higgins, Muhammad A Ayub, Yu-Ping Shao, Elissa M Ye, Brian Coughlin, Haoqi Sun, Sydney S Cash, Taylor B Thompson, Oluwaseun Akeju, David Kuller, Robert J Thomas, and Brandon M Westover. 8/16/2022. “
High prevalence of sleep-disordered breathing in the intensive care unit—a cross-sectional study.” Sleep and Breathing, Pp. 1-14.
Publisher's VersionAbstract
Purpose
Sleep-disordered breathing may be induced by, exacerbate, or complicate recovery from critical illness. Disordered breathing during sleep, which itself is often fragmented, can go unrecognized in the intensive care unit (ICU). The objective of this study was to investigate the prevalence, severity, and risk factors of sleep-disordered breathing in ICU patients using a single respiratory belt and oxygen saturation signals.
Methods
Patients in three ICUs at Massachusetts General Hospital wore a thoracic respiratory effort belt as part of a clinical trial for up to 7 days and nights. Using a previously developed machine learning algorithm, we processed respiratory and oximetry signals to measure the 3% apnea–hypopnea index (AHI) and estimate AH-specific hypoxic burden and periodic breathing. We trained models to predict AHI categories for 12-h segments from risk factors, including admission variables and bio-signals data, available at the start of these segments.
Results
Of 129 patients, 68% had an AHI ≥ 5; 40% an AHI > 15, and 19% had an AHI > 30 while critically ill. Median [interquartile range] hypoxic burden was 2.8 [0.5, 9.8] at night and 4.2 [1.0, 13.7] %min/h during the day. Of patients with AHI ≥ 5, 26% had periodic breathing. Performance of predicting AHI-categories from risk factors was poor.
Conclusions
Sleep-disordered breathing and sleep apnea events while in the ICU are common and are associated with substantial burden of hypoxia and periodic breathing. Detection is feasible using limited bio-signals, such as respiratory effort and SpO2 signals, while risk factors were insufficient to predict AHI severity.
Andreas Brink-Kjaer, Eileen B Leary, Haoqi Sun, Brandon M Westover, Katie L Stone, Paul E Peppard, Nancy E Lane, Peggy M Cawthon, Susan Redline, Poul Jennum, Helge BD Sorensen, and Emmanuel Mignot. 7/22/2022. “
Age estimation from sleep studies using deep learning predicts life expectancy.” npj Digital Medicine, 5, 1, Pp. 1-10.
Publisher's VersionAbstractSleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with increased all-cause mortality rate of 29% (95% confidence interval: 20–39%). An increase from −10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1–11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.
Wendong Ge, Haitham Alabsi, Aayushee Jain, Elissa Ye, Haoqi Sun, Marta Fernandes, Colin Magdamo, Ryan A Tesh, Sarah I Collens, Amy Newhouse, Lidia MVR Moura, Sahar Zafar, John Hsu, Oluwaseun Akeju, Gregory K Robbins, Shibani S Mukerji, Sudeshna Das, and Brandon M Westover. 6/24/2022. “
Identifying Patients With Delirium Based on Unstructured Clinical Notes: Observational Study.” JMIR Formative Research, 6, 6, Pp. e33834.
Publisher's VersionAbstract
Background: Delirium in hospitalized patients is a syndrome of acute brain dysfunction. Diagnostic (International Classification of Diseases [ICD]) codes are often used in studies using electronic health records (EHRs), but they are inaccurate.
Objective: We sought to develop a more accurate method using natural language processing (NLP) to detect delirium episodes on the basis of unstructured clinical notes.
Methods: We collected 1.5 million notes from >10,000 patients from among 9 hospitals. Seven experts iteratively labeled 200,471 sentences. Using these, we trained three NLP classifiers: Support Vector Machine, Recurrent Neural Networks, and Transformer. Testing was performed using an external data set. We also evaluated associations with delirium billing (ICD) codes, medications, orders for restraints and sitters, direct assessments (Confusion Assessment Method [CAM] scores), and in-hospital mortality. F1 scores, confusion matrices, and areas under the receiver operating characteristic curve (AUCs) were used to compare NLP models. We used the φ coefficient to measure associations with other delirium indicators.
Results: The transformer NLP performed best on the following parameters: micro F1=0.978, macro F1=0.918, positive AUC=0.984, and negative AUC=0.992. NLP detections exhibited higher correlations (φ) than ICD codes with deliriogenic medications (0.194 vs 0.073 for ICD codes), restraints and sitter orders (0.358 vs 0.177), mortality (0.216 vs 0.000), and CAM scores (0.256 vs –0.028).
Conclusions: Clinical notes are an attractive alternative to ICD codes for EHR delirium studies but require automated methods. Our NLP model detects delirium with high accuracy, similar to manual chart review. Our NLP approach can provide more accurate determination of delirium for large-scale EHR-based studies regarding delirium, quality improvement, and clinical trails.
Yingxia Liang, David Y. W. Lee, Sherri Zhen, Haoqi Sun, Biyue Zhu, Jing Liu, Dan Lei, Chih-Chung Jerry Lin, Siyi Zhang, Nicholas A. Jacques, Luisa Quinti, Chongzhao Ran, Changning Wang, Ana Griciuc, Se Hoon Choi, Rong Hua Dai, Thomas Efferth, Rudolph E. Tanzi, and Can Zhang. 5/13/2022. “
Natural Medicine HLXL Targets Multiple Pathways of Amyloid-mediated Neuroinflammation and Immune Response in Treating Alzheimer's Disease.” Phytomedicine, 154158.
Publisher's VersionAbstract
Based on the complex pathology of AD, a single chemical approach may not be sufficient to deal simultaneously with multiple pathways of amyloid-tau neuroinflammation. A polydrug approach which contains multiple bioactive components targeting multiple pathways in AD would be more appropriate. Here we focused on a Chinese medicine (HLXL), which contains 56 bioactive natural products identified in 11 medicinal plants and displays potent anti-inflammatory and immuno-modulatory activity.
We investigated the neuroimmune and neuroinflammation mechanisms by which HLXL may attenuate AD neuropathology. Specifically, we investigated the effects of HLXL on the neuropathology of AD using both transgenic mouse models as well as microglial cell-based models. The 5XFAD transgenic animals and microglial cell models were respectively treated with HLXL and Aβ42, and/or lipopolysaccharide (LPS), and then analyzed focusing on microglia mediated Aβ uptake and clearance, as well as pathway changes. We showed that HLXL significantly reduced amyloid neuropathology by upregulation of microglia-mediated phagocytosis of Aβ both in vivo and in vitro. HLXL displayed multi-modal mechanisms regulating pathways of phagocytosis and energy metabolism.
Our results may not only open a new avenue to support pharmacologic modulation of neuroinflammation and the neuroimmune system for AD intervention, but also identify HLXL as a promising natural medicine for AD. It is conceivable that the traditional wisdom of natural medicine in combination with modern science and technology would be the best strategy in developing effective therapeutics for AD.
Nalini M. Singh, Jordan B. Harrod, Sandya Subramanian, Mitchell Robinson, Ken Chang, Suheyla Cetin-Karayumak, Adrian Vasile Dalca, Simon Eickhoff, Michael Fox, Loraine Franke, Polina Golland, Daniel Haehn, Juan Eugenio Iglesias, Lauren J. O’Donnell, Yangming Ou, Yogesh Rathi, Shan H. Siddiqi, Haoqi Sun, M. Brandon Westover, Susan Whitfield-Gabrieli, and Randy L. Gollub. 3/28/2022. “
How Machine Learning is Powering Neuroimaging to Improve Brain Health.” Neuroinformatics.
Publisher's VersionAbstractThis report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, “Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application”, co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.
Noor Adra, Haoqi Sun, Wolfgang Ganglberger, Elissa Ye, Lisa Dummer, Ryan Tesh, Mike Westmeijer, Madalena Da Silva Cardoso, Erin Kitchener, An Ouyang, Joel Salinas, Jonathan Rosand, Sydney S. Cash, Robert J. Thomas, and M. Brandon Westover. 1/4/2022. “
Optimal Spindle Detection Parameters for Predicting Cognitive Performance.” Sleep, zsac001.
Publisher's VersionAbstract
Study Objectives
Alterations in sleep spindles have been linked to cognitive impairment. This finding has contributed to a growing interest in identifying sleep-based biomarkers of cognition and neurodegeneration, including sleep spindles. However, flexibility surrounding spindle definitions and algorithm parameter settings present a methodological challenge. The aim of this study was to characterize how spindle detection parameter settings influence the association between spindle features and cognition and to identify parameters with the strongest association with cognition.
Methods
Adult patients (n=167, 49 ± 18 years) completed the NIH Toolbox Cognition Battery after undergoing overnight diagnostic polysomnography recordings for suspected sleep disorders. We explored 1000 combinations across seven parameters in Luna, an open-source spindle detector, and used four features of detected spindles (amplitude, density, duration, and peak frequency) to fit linear multiple regression models to predict cognitive scores.
Results
Spindle features (amplitude, density, duration, and mean frequency) were associated with the ability to predict raw fluid cognition scores (r=0.503) and age-adjusted fluid cognition scores (r=0.315) with the best spindle parameters. Fast spindle features generally showed better performance relative to slow spindle features. Spindle features weakly predicted total cognition and poorly predicted crystallized cognition regardless of parameter settings.
Conclusion
Our exploration of spindle detection parameters identified optimal parameters for studies of fluid cognition and revealed the role of parameter interactions for both slow and fast spindles. Our findings support sleep spindles as a sleep-based biomarker of fluid cognition.
Meike van Sleuwen, Haoqi Sun, Christine Eckhardt, Anudeepthi Neelagiri, Ryan A. Tesh, Mike Westmeijer, Luis Paixao, Subapriya Rajan, Parimala Velpula Krishnamurthy, Pooja Sikka, Michael J. Leone, Ezhil Panneerselvam, Syed A. Quadri, Oluwaseun Akeju, Eyal Y. Kimchi, and M. Brandon Westover. 1/1/2022. “
Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).” Critical Care Medicine, 50, 1, Pp. e11-e19.
Publisher's VersionAbstract
Objectives:
Delirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a tool that can help close this diagnostic gap: the Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).
Design:
Retrospective cohort study.
Setting:
Single-center tertiary academic medical center.
Patients:
Three-hundred seventy-three adult patients undergoing electroencephalography to evaluate altered mental status between August 2015 and December 2019.
Interventions:
None.
Measurements and Main Results:
We developed the E-CAM-S based on a learning-to-rank machine learning model of forehead electroencephalography signals. Clinical delirium severity was assessed using the Confusion Assessment Method Severity (CAM-S). We compared associations of E-CAM-S and CAM-S with hospital length of stay and inhospital mortality. E-CAM-S correlated with clinical CAM-S (R = 0.67; p < 0.0001). For the overall cohort, E-CAM-S and CAM-S were similar in their strength of association with hospital length of stay (correlation = 0.31 vs 0.41, respectively; p = 0.082) and inhospital mortality (area under the curve = 0.77 vs 0.81; p = 0.310). Even when restricted to noncomatose patients, E-CAM-S remained statistically similar to CAM-S in its association with length of stay (correlation = 0.37 vs 0.42, respectively; p = 0.188) and inhospital mortality (area under the curve = 0.83 vs 0.74; p = 0.112). In addition to previously appreciated spectral features, the machine learning framework identified variability in multiple measures over time as important features in electroencephalography-based prediction of delirium severity.
Conclusions:
The E-CAM-S is an automated, physiologic measure of delirium severity that predicts clinical outcomes with a level of performance comparable to conventional interview-based clinical assessment.
Ryan A. Tesh, Haoqi Sun, Jin Jing, Mike Westmeijer, Anudeepthi Neelagiri, Subapriya Rajan, Parimala V. Krishnamurthy, Pooja Sikka, Syed A. Quadri, Michael J. Leone, Luis Paixao, Ezhil Panneerselvam, Christine Eckhardt, Aaron F. Struck, Peter W. Kaplan, Oluwaseun Akeju, Daniel Jones, Eyal Y. Kimchi, and M. Brandon Westover. 1/2022. “
VE-CAM-S: Visual EEG-Based Grading of Delirium Severity and Associations With Clinical Outcomes.” Critical Care Explorations, 4, 1, Pp. e0611.
Publisher's VersionAbstract
OBJECTIVES:
To develop a physiologic grading system for the severity of acute encephalopathy manifesting as delirium or coma, based on EEG, and to investigate its association with clinical outcomes.
DESIGN:
This prospective, single-center, observational cohort study was conducted from August 2015 to December 2016 and October 2018 to December 2019.
SETTING:
Academic medical center, all inpatient wards.
PATIENTS/SUBJECTS:
Adult inpatients undergoing a clinical EEG recording; excluded if deaf, severely aphasic, developmentally delayed, non-English speaking (if noncomatose), or if goals of care focused primarily on comfort measures. Four-hundred six subjects were assessed; two were excluded due to technical EEG difficulties.
INTERVENTIONS:
None.
MEASUREMENTS AND MAIN RESULTS:
A machine learning model, with visually coded EEG features as inputs, was developed to produce scores that correlate with behavioral assessments of delirium severity (Confusion Assessment Method-Severity [CAM-S] Long Form [LF] scores) or coma; evaluated using Spearman R correlation; area under the receiver operating characteristic curve (AUC); and calibration curves. Associations of Visual EEG Confusion Assessment Method Severity (VE-CAM-S) were measured for three outcomes: functional status at discharge (via Glasgow Outcome Score [GOS]), inhospital mortality, and 3-month mortality. Four-hundred four subjects were analyzed (mean [sd] age, 59.8 yr [17.6 yr]; 232 [57%] male; 320 [79%] White; 339 [84%] non-Hispanic); 132 (33%) without delirium or coma, 143 (35%) with delirium, and 129 (32%) with coma. VE-CAM-S scores correlated strongly with CAM-S scores (Spearman correlation 0.67 [0.62–0.73]; p < 0.001) and showed excellent discrimination between levels of delirium (CAM-S LF = 0 vs ≥ 4, AUC 0.85 [0.78–0.92], calibration slope of 1.04 [0.87–1.19] for CAM-S LF ≤ 4 vs ≥ 5). VE-CAM-S scores were strongly associated with important clinical outcomes including inhospital mortality (AUC 0.79 [0.72–0.84]), 3-month mortality (AUC 0.78 [0.71–0.83]), and GOS at discharge (0.76 [0.69–0.82]).
CONCLUSIONS:
VE-CAM-S is a physiologic grading scale for the severity of symptoms in the setting of delirium and coma, based on visually assessed electroencephalography features. VE-CAM-S scores are strongly associated with clinical outcomes.