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Manrique-Castano D, Bhaskar D, ElAli A. Dissecting glial scar formation by spatial point pattern and topological data analysis. Sci Rep 2024; 14:19035. [PMID: 39152163 PMCID: PMC11329771 DOI: 10.1038/s41598-024-69426-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 08/05/2024] [Indexed: 08/19/2024] Open
Abstract
Glial scar formation represents a fundamental response to central nervous system (CNS) injuries. It is mainly characterized by a well-defined spatial rearrangement of reactive astrocytes and microglia. The mechanisms underlying glial scar formation have been extensively studied, yet quantitative descriptors of the spatial arrangement of reactive glial cells remain limited. Here, we present a novel approach using point pattern analysis (PPA) and topological data analysis (TDA) to quantify spatial patterns of reactive glial cells after experimental ischemic stroke in mice. We provide open and reproducible tools using R and Julia to quantify spatial intensity, cell covariance and conditional distribution, cell-to-cell interactions, and short/long-scale arrangement, which collectively disentangle the arrangement patterns of the glial scar. This approach unravels a substantial divergence in the distribution of GFAP+ and IBA1+ cells after injury that conventional analysis methods cannot fully characterize. PPA and TDA are valuable tools for studying the complex spatial arrangement of reactive glia and other nervous cells following CNS injuries and have potential applications for evaluating glial-targeted restorative therapies.
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Affiliation(s)
- Daniel Manrique-Castano
- Neuroscience Axis, Research Center of CHU de Québec-Université Laval, Quebec City, QC, Canada.
- Department of Psychiatry and Neuroscience, Faculty of Medicine, Université Laval, Quebec City, QC, Canada.
| | | | - Ayman ElAli
- Neuroscience Axis, Research Center of CHU de Québec-Université Laval, Quebec City, QC, Canada.
- Department of Psychiatry and Neuroscience, Faculty of Medicine, Université Laval, Quebec City, QC, Canada.
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2
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Kim YS, Kim J, Park S, Kim KN, Ha Y, Yi S, Shin DA, Kuh SU, Lee CK, Koo BN, Kim SE. Differential effects of sevoflurane and desflurane on frontal intraoperative electroencephalogram dynamics associated with postoperative delirium. J Clin Anesth 2024; 93:111368. [PMID: 38157663 DOI: 10.1016/j.jclinane.2023.111368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 11/23/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024]
Abstract
STUDY OBJECTIVE Intraoperative electroencephalogram (EEG) patterns associated with postoperative delirium (POD) development have been studied, but the differences in EEG recordings between sevoflurane- and desflurane-induced anesthesia have not been clarified. We aimed to distinguish the EEG characteristics of sevoflurane and desflurane in relation to POD development. DESIGN AND PATIENTS We collected frontal four-channel EEG data during the maintenance of anesthesia from 148 elderly patients who received sevoflurane (n = 77) or desflurane (n = 71); 30 patients were diagnosed with delirium postoperatively. The patients were divided into four subgroups based on anesthetics and delirium status: sevoflurane delirium (n = 17), sevoflurane non-delirium (n = 60), desflurane delirium (n = 13), and desflurane non-delirium (n = 58). We compared spectral power, coherence, and pairwise phase consistency (PPC) between sevoflurane and desflurane, and between non-delirium and delirium groups for each anesthetic. MAIN RESULTS In patients without POD, the sevoflurane non-delirium group exhibited higher EEG spectral power across 8.5-35 Hz (99.5% CI bootstrap analysis) and higher PPC from alpha to gamma bands (p < 0.005) compared to the desflurane non-delirium group. Conversely, in patients with POD, no significant EEG differences were observed between the sevoflurane and desflurane delirium groups. For the sevoflurane-induced patients, the sevoflurane delirium group had significantly lower power within 7.5-31.5 Hz (99.5% CI bootstrap analysis), reduced coherence over 8.9-23.8 Hz (99.5% CI bootstrap analysis), and lower PPC values in the alpha band (p < 0.005) compared with the sevoflurane non-delirium group. For the desflurane-induced patients, there were no significant differences in the EEG patterns between delirium and non-delirium groups. CONCLUSIONS In normal patients without POD, sevoflurane demonstrates a higher power spectrum and prefrontal connectivity than desflurane. Furthermore, reduced frontal alpha power, coherence, and connectivity of intraoperative EEG could be associated with an increased risk of POD. These intraoperative EEG characteristics associated with POD are more noticeable in sevoflurane-induced anesthesia than in desflurane-induced anesthesia.
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Affiliation(s)
- Yeon-Su Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
| | - Jeongmin Kim
- Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; Anesthesia and Pain Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Sujung Park
- Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; Anesthesia and Pain Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Keung Nyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Yoon Ha
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea; POSTECH Biotech Center, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Republic of Korea
| | - Seong Yi
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Dong Ah Shin
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Sung Uk Kuh
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Chang Kyu Lee
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Bon-Nyeo Koo
- Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; Anesthesia and Pain Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
| | - Seong-Eun Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
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3
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Nishizawa Y, Yamanashi T, Saito T, Marra P, Crutchley KJ, Wahba NE, Malicoat J, Shibata K, Nishiguchi T, Lee S, Cho HR, Kanazawa T, Shinozaki G. Bispectral EEG (BSEEG) Algorithm Captures High Mortality Risk Among 1,077 Patients: Its Relationship to Delirium Motor Subtype. Am J Geriatr Psychiatry 2023; 31:704-715. [PMID: 37003894 DOI: 10.1016/j.jagp.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 02/16/2023] [Accepted: 03/03/2023] [Indexed: 04/03/2023]
Abstract
OBJECTIVE Delirium is dangerous and a predictor of poor patient outcomes. We have previously reported the utility of the bispectral EEG (BSEEG) with a novel algorithm for the detection of delirium and prediction of patient outcomes including mortality. The present study employed a normalized BSEEG (nBSEEG) score to integrate the previous cohorts to combine their data to investigate the prediction of patient outcomes. We also aimed to test if the BSEEG method can be applicable regardless of age, and independent of delirium motor subtypes. METHODS We calculated nBSEEG score from raw BSEEG data in each cohort and classified patients into BSEEG-positive and BSEEG-negative groups. We used log-rank test and Cox proportional hazards models to predict 90-day and 1-year outcomes for the BSEEG-positive and -negative groups in all subjects and motor subgroups. RESULTS A total of 1,077 subjects, the BSEEG-positive group showed significantly higher 90-day (hazard ratio 1.33 [95% CI 1.16-1.52] and 1-year (hazard ratio 1.22 [95% CI 1.06-1.40] mortality rates than the negative group after adjustment for covariates such as age, sex, CCI, and delirium status. Among patients with different motor subtypes of delirium, the hypoactive group showed significantly higher 90-day (hazard ratio 1.41 [95% CI 1.12-1.76] and 1-year mortality rates (hazard ratio 1.32 [95% CI 1.05-1.67], which remained significant after adjustment for the same covariates. CONCLUSION We found that the BSEEG method is capable of capturing patients at high mortality risk.
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Affiliation(s)
- Yoshitaka Nishizawa
- Department of Psychiatry and Behavioral Sciences (YN, TY, KS, TN, GS), Stanford University School of Medicine, Palo Alto, CA; Faculty of Medicine (YN, TK), Department of Neuropsychiatry, Osaka Medical and Pharmaceutical University, Takatsuki, Osaka, Japan
| | - Takehiko Yamanashi
- Department of Psychiatry and Behavioral Sciences (YN, TY, KS, TN, GS), Stanford University School of Medicine, Palo Alto, CA; Department of Psychiatry (TY, TS, PM, KC, NW, JM, GS), Iowa City, University of Iowa Carver College of Medicine, IA; Department of Neuropsychiatry (TY, TN), Tottori University Faculty of Medicine, Yonago, Tottori, Japan
| | - Taku Saito
- Department of Psychiatry (TY, TS, PM, KC, NW, JM, GS), Iowa City, University of Iowa Carver College of Medicine, IA; Department of Psychiatry (TS), School of Medicine, National Defense Medical College, Tokorozawa City, Saitama, Japan
| | - Pedro Marra
- Department of Psychiatry (TY, TS, PM, KC, NW, JM, GS), Iowa City, University of Iowa Carver College of Medicine, IA
| | - Kaitlyn J Crutchley
- Department of Psychiatry (TY, TS, PM, KC, NW, JM, GS), Iowa City, University of Iowa Carver College of Medicine, IA; College of Medicine (KC), University of Nebraska Medical Center, Omaha, NE
| | - Nadia E Wahba
- Department of Psychiatry (TY, TS, PM, KC, NW, JM, GS), Iowa City, University of Iowa Carver College of Medicine, IA; Department of Psychiatry (NW), School of Medicine, Oregon Health & Science University, Portland, OR
| | - Johnny Malicoat
- Department of Psychiatry (TY, TS, PM, KC, NW, JM, GS), Iowa City, University of Iowa Carver College of Medicine, IA
| | - Kazuki Shibata
- Department of Psychiatry and Behavioral Sciences (YN, TY, KS, TN, GS), Stanford University School of Medicine, Palo Alto, CA; Sumitomo Pharma Co. Ltd (KS), Osaka, Osaka, Japan
| | - Tsuyoshi Nishiguchi
- Department of Psychiatry and Behavioral Sciences (YN, TY, KS, TN, GS), Stanford University School of Medicine, Palo Alto, CA; Department of Neuropsychiatry (TY, TN), Tottori University Faculty of Medicine, Yonago, Tottori, Japan
| | - Sangil Lee
- Department of Emergency Medicine (SL), University of Iowa Carver College of Medicine, Iowa City, IA
| | - Hyunkeun R Cho
- Department of Biostatistics (HC), University of Iowa College of Public Health, Iowa City, IA
| | - Tetsufumi Kanazawa
- Faculty of Medicine (YN, TK), Department of Neuropsychiatry, Osaka Medical and Pharmaceutical University, Takatsuki, Osaka, Japan
| | - Gen Shinozaki
- Department of Psychiatry and Behavioral Sciences (YN, TY, KS, TN, GS), Stanford University School of Medicine, Palo Alto, CA; Department of Psychiatry (TY, TS, PM, KC, NW, JM, GS), Iowa City, University of Iowa Carver College of Medicine, IA.
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Mulkey MA, Huang H, Albanese T, Kim S, Yang B. Supervised deep learning with vision transformer predicts delirium using limited lead EEG. Sci Rep 2023; 13:7890. [PMID: 37193736 DOI: 10.1038/s41598-023-35004-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 05/11/2023] [Indexed: 05/18/2023] Open
Abstract
As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians detect less than 40% of delirium when using a validated screening tool. EEG is the criterion standard but is resource intensive thus not feasible for widespread delirium monitoring. This study evaluated the use of limited-lead rapid-response EEG and supervised deep learning methods with vision transformer to predict delirium. This proof-of-concept study used a prospective design to evaluate use of supervised deep learning with vision transformer and a rapid-response EEG device for predicting delirium in mechanically ventilated critically ill older adults. Fifteen different models were analyzed. Using all available data, the vision transformer models provided 99.9%+ training and 97% testing accuracy across models. Vision transformer with rapid-response EEG is capable of predicting delirium. Such monitoring is feasible in critically ill older adults. Therefore, this method has strong potential for improving the accuracy of delirium detection, providing greater opportunity for individualized interventions. Such an approach may shorten hospital length of stay, increase discharge to home, decrease mortality, and reduce the financial burden associated with delirium.
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Affiliation(s)
- Malissa A Mulkey
- College of Nursing, University of South Carolina, Columbia, SC, USA.
| | - Huyunting Huang
- Department of Computer and Information Technology, Purdue University, Lafayette, IN, USA
| | - Thomas Albanese
- Department of Engineering, University of East Carolina, Greenville, NC, USA
| | - Sunghan Kim
- Department of Engineering, University of East Carolina, Greenville, NC, USA
| | - Baijian Yang
- Department of Computer and Information Technology, Purdue University, Lafayette, IN, USA
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Yamanashi T, Sullivan EJ, Comp KR, Nishizawa Y, Akers CC, Chang G, Modukuri M, Tran T, Anderson ZEEM, Marra PS, Crutchley KJ, Wahba NE, Iwata M, Karam MD, Noiseux NO, Cho HR, Shinozaki G. Anti-inflammatory medication use associated with reduced delirium risk and all-cause mortality: A retrospective cohort study. J Psychosom Res 2023; 168:111212. [PMID: 36963165 DOI: 10.1016/j.jpsychores.2023.111212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 03/26/2023]
Abstract
OBJECTIVE To investigate the relationship between history of anti-inflammatory medication use and delirium risk, as well as long-term mortality. METHODS In this retrospective cohort study, subjects recruited between January 2016 and March 2020 were analyzed. Information about anti-inflammatory medication use history including aspirin, NSAIDs, glucosamine, and other anti-inflammatory drugs, was collected. Logistic regression analysis investigated the relationship between anti-inflammatory medications and delirium. Log-rank analysis and cox proportional hazards model investigated the relationship between anti-inflammatory medications and one-year mortality. RESULTS The data from 1274 subjects were analyzed. The prevalence of delirium was significantly lower in subjects with NSAIDs usage (23.0%) than in those without NSAIDs usage (35.0%) (p < 0.001). Logistic regression analysis controlling for age, sex, dementia status, and hospitalization department showed that the risk of delirium tended to be reduced by a history of NSAIDs use (OR, 0.76 [95% CI, 0.55 to 1.03]). The one-year mortality in the subjects with NSAIDs (survival rate, 0.879 [95% CI, 0.845 to 0.906]) was significantly higher than in the subjects without NSAIDs (survival rate, 0.776 [95% CI, 0.746 to 0.803]) (p < 0.001). A history of NSAIDs use associated with the decreased risk of one-year mortality even after adjustment for age, sex, Charlson Comorbidity Index, delirium status, and hospitalization department (HR, 0.70 [95% CI, 0.51 to 0.96]). CONCLUSION This study suggested that NSAIDs usage was associated with decreased delirium prevalence and lower one-year mortality. The potential benefit of NSAIDs on delirium risk and mortality were shown.
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Affiliation(s)
- Takehiko Yamanashi
- Stanford University School of Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, United States of America; University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America; Tottori University Faculty of Medicine, Department of Neuropsychiatry, Yonago, Tottori, Japan
| | - Eleanor J Sullivan
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America
| | - Katie R Comp
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America
| | - Yoshitaka Nishizawa
- Stanford University School of Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, United States of America; Osaka Medical and Pharmaceutical University Faculty of Medicine, Department of Neuropsychiatry, Takatsuki, Osaka, Japan
| | - Cade C Akers
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America
| | - Gloria Chang
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America
| | - Manisha Modukuri
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America
| | - Tammy Tran
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America
| | - Zoe-Ella E M Anderson
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America
| | - Pedro S Marra
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America
| | - Kaitlyn J Crutchley
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America
| | - Nadia E Wahba
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America
| | - Masaaki Iwata
- Tottori University Faculty of Medicine, Department of Neuropsychiatry, Yonago, Tottori, Japan
| | - Matthew D Karam
- University of Iowa Carver College of Medicine, Department of Orthopedic Surgery, Iowa City, IA, United States of America
| | - Nicolas O Noiseux
- University of Iowa Carver College of Medicine, Department of Orthopedic Surgery, Iowa City, IA, United States of America
| | - Hyunkeun R Cho
- University of Iowa College of Public Health, Department of Biostatistics, Iowa City, IA, United States of America
| | - Gen Shinozaki
- Stanford University School of Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, United States of America; University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States of America.
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Guay CS, Kafashan M, Huels ER, Jiang Y, Beyoglu B, Spencer JW, Geczi K, Apakama G, Ju YES, Wildes TS, Avidan MS, Palanca BJA. Postoperative Delirium Severity and Recovery Correlate With Electroencephalogram Spectral Features. Anesth Analg 2023; 136:140-151. [PMID: 36130079 PMCID: PMC9653519 DOI: 10.1213/ane.0000000000006075] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Delirium is an acute syndrome characterized by inattention, disorganized thinking, and an altered level of consciousness. A reliable biomarker for tracking delirium does not exist, but oscillations in the electroencephalogram (EEG) could address this need. We evaluated whether the frequencies of EEG oscillations are associated with delirium onset, severity, and recovery in the postoperative period. METHODS Twenty-six adults enrolled in the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES; ClinicalTrials.gov NCT02241655) study underwent major surgery requiring general anesthesia, and provided longitudinal postoperative EEG recordings for this prespecified substudy. The presence and severity of delirium were evaluated with the confusion assessment method (CAM) or the CAM-intensive care unit. EEG data obtained during awake eyes-open and eyes-closed states yielded relative power in the delta (1-4 Hz), theta (4-8 Hz), and alpha (8-13 Hz) bands. Discriminability for delirium presence was evaluated with c-statistics. To account for correlation among repeated measures within patients, mixed-effects models were generated to assess relationships between: (1) delirium severity and EEG relative power (ordinal), and (2) EEG relative power and time (linear). Slopes of ordinal and linear mixed-effects models are reported as the change in delirium severity score/change in EEG relative power, and the change in EEG relative power/time (days), respectively. Bonferroni correction was applied to confidence intervals (CIs) to account for multiple comparisons. RESULTS Occipital alpha relative power during eyes-closed states offered moderate discriminability (c-statistic, 0.75; 98% CI, 0.58-0.87), varying inversely with delirium severity (slope, -0.67; 98% CI, -1.36 to -0.01; P = .01) and with severity of inattention (slope, -1.44; 98% CI, -2.30 to -0.58; P = .002). Occipital theta relative power during eyes-open states correlated directly with severity of delirium (slope, 1.28; 98% CI, 0.12-2.44; P = .007), inattention (slope, 2.00; 98% CI, 0.48-3.54; P = .01), and disorganized thinking (slope, 3.15; 98% CI, 0.66-5.65; P = .01). Corresponding frontal EEG measures recapitulated these relationships to varying degrees. Severity of altered level of consciousness correlated with frontal theta relative power during eyes-open states (slope, 11.52; 98% CI, 6.33-16.71; P < .001). Frontal theta relative power during eyes-open states correlated inversely with time (slope, -0.05; 98% CI, -0.12 to -0.04; P = .002). CONCLUSIONS Presence, severity, and core features of postoperative delirium covary with spectral features of the EEG. The cost and accessibility of EEG facilitate the translation of these findings to future mechanistic and interventional trials.
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Affiliation(s)
- Christian S Guay
- From the Department of Anesthesiology
- Center on Biological Rhythms and Sleep, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - MohammadMehdi Kafashan
- From the Department of Anesthesiology
- Center on Biological Rhythms and Sleep, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Emma R Huels
- Neuroscience Graduate Program
- Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan
| | | | - Bora Beyoglu
- Baylor Scott and White Research Institute, Plano, Texas
| | | | - Kristin Geczi
- From the Department of Anesthesiology
- University of Michigan Medical School, Ann Arbor, Michigan
| | | | - Yo-El S Ju
- From the Department of Anesthesiology
- Center on Biological Rhythms and Sleep, Washington University School of Medicine in St Louis, St Louis, Missouri
- Department of Neurology
- Hope Center for Neurological Disorders
| | | | - Michael S Avidan
- From the Department of Anesthesiology
- Center on Biological Rhythms and Sleep, Washington University School of Medicine in St Louis, St Louis, Missouri
- Department of Psychiatry
| | - Ben Julian A Palanca
- Department of Psychiatry
- Division of Biology and Biomedical Sciences
- Department of Biomedical Engineering; Washington University School of Medicine in St Louis, St Louis, Missouri
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7
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Yamanashi T, Anderson ZEEM, Modukuri M, Chang G, Tran T, Marra PS, Wahba NE, Crutchley KJ, Sullivan EJ, Jellison SS, Comp KR, Akers CC, Meyer AA, Lee S, Iwata M, Cho HR, Shinozaki E, Shinozaki G. The potential benefit of metformin to reduce delirium risk and mortality: a retrospective cohort study. Aging (Albany NY) 2022; 14:8927-8943. [PMID: 36399107 PMCID: PMC9740381 DOI: 10.18632/aging.204393] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/27/2022] [Indexed: 12/04/2022]
Abstract
PURPOSE Metformin has been reported to improve age-related disorders, including dementia, and to lower mortality. This study was conducted to investigate whether metformin use lower delirium risk, as well as long-term mortality. METHODS In this retrospective cohort study, previously recruited 1,404 subjects were analyzed. The relationship between metformin use and delirium, and the relationship between metformin use and 3-year mortality were investigated. MAIN FINDINGS 242 subjects were categorized into a type 2 diabetes mellitus (DM)-without-metformin group, and 264 subjects were categorized into a DM-with-metformin group. Prevalence of delirium was 36.0% in the DM-without-metformin group, and 29.2% in the DM-with-metformin group. A history of metformin use reduced the risk of delirium in patients with DM (OR, 0.50 [95% CI, 0.32 to 0.79]) after controlling for confounding factors. The 3-year mortality in the DM-without-metformin group (survival rate, 0.595 [95% CI, 0.512 to 0.669]) was higher than in the DM-with-metformin group (survival rate, 0.695 [95% CI, 0.604 to 0.770]) (p=0.035). A history of metformin use decreased the risk of 3-year mortality after adjustment for confounding factors (HR, 0.69 [95% CI, 0.48 to 0.98]). CONCLUSIONS Metformin use may lower the risk of delirium and mortality in DM patients.
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Affiliation(s)
- Takehiko Yamanashi
- Stanford University School of Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA 94305, USA,University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA,Tottori University Faculty of Medicine, Department of Neuropsychiatry, Yonago-Shi, Tottori, Japan
| | - Zoe-Ella EM Anderson
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Manisha Modukuri
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Gloria Chang
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Tammy Tran
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Pedro S. Marra
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Nadia E. Wahba
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Kaitlyn J. Crutchley
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Eleanor J. Sullivan
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Sydney S. Jellison
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Katie R. Comp
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Cade C. Akers
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Alissa A. Meyer
- University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
| | - Sangil Lee
- University of Iowa Carver College of Medicine, Department of Emergency Medicine, Iowa City, IA 52242, USA
| | - Masaaki Iwata
- Tottori University Faculty of Medicine, Department of Neuropsychiatry, Yonago-Shi, Tottori, Japan
| | - Hyunkeun R. Cho
- University of Iowa College of Public Health, Department of Biostatistics, Iowa City, IA 52242, USA
| | - Eri Shinozaki
- University of Iowa Carver College of Medicine, Department of Internal Medicine, Iowa City, IA 52242, USA
| | - Gen Shinozaki
- Stanford University School of Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA 94305, USA,University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA
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8
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Wiegand TLT, Rémi J, Dimitriadis K. Electroencephalography in delirium assessment: a scoping review. BMC Neurol 2022; 22:86. [PMID: 35277128 PMCID: PMC8915483 DOI: 10.1186/s12883-022-02557-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/13/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Delirium is a common disorder affecting around 31% of patients in the intensive care unit (ICU). Delirium assessment scores such as the Confusion Assessment Method (CAM) are time-consuming, they cannot differentiate between different types of delirium and their etiologies, and they may have low sensitivities in the clinical setting. While today, electroencephalography (EEG) is increasingly being applied to delirious patients in the ICU, a lack of clear cut EEG signs, leads to inconsistent assessments. METHODS We therefore conducted a scoping review on EEG findings in delirium. One thousand two hundred thirty-six articles identified through database search on PubMed and Embase were reviewed. Finally, 33 original articles were included in the synthesis. RESULTS EEG seems to offer manifold possibilities in diagnosing delirium. All 33 studies showed a certain degree of qualitative or quantitative EEG alterations in delirium. Thus, normal routine (rEEG) and continuous EEG (cEEG) make presence of delirium very unlikely. All 33 studies used different research protocols to at least some extent. These include differences in time points, duration, conditions, and recording methods of EEG, as well as different patient populations, and diagnostic methods for delirium. Thus, a quantitative synthesis and common recommendations are so far elusive. CONCLUSION Future studies should compare the different methods of EEG recording and evaluation to identify robust parameters for everyday use. Evidence for quantitative bi-electrode delirium detection based on increased relative delta power and decreased beta power is growing and should be further pursued. Additionally, EEG studies on the evolution of a delirium including patient outcomes are needed.
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Affiliation(s)
- Tim L T Wiegand
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Neurology, University Hospital, Ludwig Maximilian University, 15 Marchioninistr, 81377, Munich, Germany
| | - Jan Rémi
- Department of Neurology, University Hospital, Ludwig Maximilian University, 15 Marchioninistr, 81377, Munich, Germany
| | - Konstantinos Dimitriadis
- Department of Neurology, University Hospital, Ludwig Maximilian University, 15 Marchioninistr, 81377, Munich, Germany.
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilians University, Munich, Germany.
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9
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Xu X, Drougard N, Roy RN. Topological Data Analysis as a New Tool for EEG Processing. Front Neurosci 2021; 15:761703. [PMID: 34803594 PMCID: PMC8595401 DOI: 10.3389/fnins.2021.761703] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 10/11/2021] [Indexed: 12/03/2022] Open
Abstract
Electroencephalography (EEG) is a widely used cerebral activity measuring device for both clinical and everyday life applications. In addition to denoising and potential classification, a crucial step in EEG processing is to extract relevant features. Topological data analysis (TDA) as an emerging tool enables to analyse and understand data from a different angle than traditionally used methods. As a higher dimensional analogy of graph analysis, TDA can model rich interactions beyond pairwise relations. It also distinguishes different dynamics of EEG time series. TDA remains largely unknown to the EEG processing community while it fits well the heterogeneous nature of EEG signals. This short review aims to give a quick introduction to TDA and how it can be applied to EEG analysis in various applications including brain-computer interfaces (BCIs). After introducing the objective of the article, the main concepts and ideas of TDA are explained. Next, how to implement it for EEG processing is detailed, and lastly the article discusses the benefits and limitations of the method.
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Affiliation(s)
- Xiaoqi Xu
- ISAE-SUPAERO, Université de Toulouse, Toulouse, France.,ANITI-Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, Toulouse, France
| | - Nicolas Drougard
- ISAE-SUPAERO, Université de Toulouse, Toulouse, France.,ANITI-Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, Toulouse, France
| | - Raphaëlle N Roy
- ISAE-SUPAERO, Université de Toulouse, Toulouse, France.,ANITI-Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, Toulouse, France
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10
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Yamanashi T, Crutchley KJ, Wahba NE, Sullivan EJ, Comp KR, Kajitani M, Tran T, Modukuri MV, Marra PS, Herrmann FM, Chang G, Anderson ZEM, Iwata M, Kobayashi K, Kaneko K, Umeda Y, Kadooka Y, Lee S, Shinozaki E, Karam MD, Noiseux NO, Shinozaki G. Evaluation of point-of-care thumb-size bispectral electroencephalography device to quantify delirium severity and predict mortality. Br J Psychiatry 2021; 220:1-8. [PMID: 35049468 DOI: 10.1192/bjp.2021.101] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
BACKGROUND We have developed the bispectral electroencephalography (BSEEG) method for detection of delirium and prediction of poor outcomes. AIMS To improve the BSEEG method by introducing a new EEG device. METHOD In a prospective cohort study, EEG data were obtained and BSEEG scores were calculated. BSEEG scores were filtered on the basis of standard deviation (s.d.) values to exclude signals with high noise. Both non-filtered and s.d.-filtered BSEEG scores were analysed. BSEEG scores were compared with the results of three delirium screening scales: the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), the Delirium Rating Scale-Revised-98 (DRS) and the Delirium Observation Screening Scale (DOSS). Additionally, the 365-day mortalities and the length of stay (LOS) in the hospital were analysed. RESULTS We enrolled 279 elderly participants and obtained 620 BSEEG recordings; 142 participants were categorised as BSEEG-positive, reflecting slower EEG activity. BSEEG scores were higher in the CAM-ICU-positive group than in the CAM-ICU-negative group. There were significant correlations between BSEEG scores and scores on the DRS and the DOSS. The mortality rate of the BSEEG-positive group was significantly higher than that of the BSEEG-negative group. The LOS of the BSEEG-positive group was longer compared with that of the BSEEG-negative group. BSEEG scores after s.d. filtering showed stronger correlations with delirium screening scores and more significant prediction of mortality. CONCLUSIONS We confirmed the usefulness of the BSEEG method for detection of delirium and of delirium severity, and prediction of patient outcomes with a new EEG device.
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Affiliation(s)
- Takehiko Yamanashi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, California, USA; and Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA; and Department of Neuropsychiatry, Tottori University Faculty of Medicine, Yonago, Japan
| | - Kaitlyn J Crutchley
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA; and School of Medicine, University of Nebraska Medical Center, Nebraska, USA
| | - Nadia E Wahba
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Eleanor J Sullivan
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Katie R Comp
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | | | - Tammy Tran
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Manisha V Modukuri
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Pedro S Marra
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Felipe M Herrmann
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Gloria Chang
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Zoe-Ella M Anderson
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Masaaki Iwata
- Department of Neuropsychiatry, Tottori University Faculty of Medicine, Yonago, Japan
| | | | - Koichi Kaneko
- Department of Neuropsychiatry, Tottori University Faculty of Medicine, Yonago, Japan
| | | | | | - Sangil Lee
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Eri Shinozaki
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Matthew D Karam
- Department of Orthopedic Surgery, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Nicolas O Noiseux
- Department of Orthopedic Surgery, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Gen Shinozaki
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, California, USA; and Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
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11
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Mortality among patients with sepsis associated with a bispectral electroencephalography (BSEEG) score. Sci Rep 2021; 11:14211. [PMID: 34244577 PMCID: PMC8270989 DOI: 10.1038/s41598-021-93588-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 06/22/2021] [Indexed: 11/08/2022] Open
Abstract
We have previously developed a bispectral electroencephalography (BSEEG) device, which was shown to be effective in detecting delirium and predicting patient outcomes. In this study we aimed to apply the BSEEG approach for a sepsis. This was a retrospective cohort study conducted at a single center. Sepsis-positive cases were identified based on retrospective chart review. EEG raw data and calculated BSEEG scores were obtained in the previous studies. The relationship between BSEEG scores and sepsis was analyzed, as well as the relationship among sepsis, BSEEG score, and mortality. Data were analyzed from 628 patients. The BSEEG score from the first encounter (1st BSEEG) showed a significant difference between patients with and without sepsis (p = 0.0062), although AUC was very small indicating that it is not suitable for detection purpose. Sepsis patients with high BSEEG scores showed the highest mortality, and non-sepsis patients with low BSEEG scores showed the lowest mortality. Mortality of non-sepsis patients with high BSEEG scores was as bad as that of sepsis patients with low BSEEG scores. Even adjusting for age, gender, comorbidity, and sepsis status, BSEEG remained a significant predictor of mortality (p = 0.008). These data are demonstrating its usefulness as a potential tool for identification of patients at high risk and management of sepsis.
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12
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Saito T, Malicoat JR, Leyden LR, Williams JC, Jellison SS, Long H, Hellman MM, Crutchley KJ, Anderson ZEEM, Lo D, Modukuri MV, Schacher CJ, Yoshino A, Toda H, Shinozaki E, Cho HR, Lee S, Shinozaki G. Mortality prediction by bispectral electroencephalography among 502 patients: its role in dementia. Brain Commun 2021; 3:fcab037. [PMID: 34136808 PMCID: PMC8204260 DOI: 10.1093/braincomms/fcab037] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/17/2021] [Accepted: 01/18/2021] [Indexed: 11/12/2022] Open
Abstract
Complications of delirium and dementia increase mortality; however, it is difficult to
diagnose delirium accurately, especially among dementia patients. The bispectral
electroencephalography score can detect delirium and predict mortality in elderly
patients. We aimed to develop an efficient and reliable bispectral electroencephalography
device for high-throughput screening. We also hypothesized that bispectral
electroencephalography score can predict mortality among dementia patients. A prospective
cohort study was conducted between January 2016 and December 2018 to measure bispectral
electroencephalography from elderly patients and correlate with outcomes. A total of 502
elderly (55 years old or older) patients with and without dementia were enrolled. For a
replication of the utility of bispectral electroencephalography, mortalities between
bispectral electroencephalography-positive and bispectral electroencephalography-negative
group were compared. In addition, patients with and without dementia status were added to
examine the utility of bispectral electroencephalography among dementia patients. The
mortality within 180 days in the bispectral electroencephalography-positive group was
higher than that of the bispectral electroencephalography-negative group in both the
replication and the total cohorts. Mortality of those in the bispectral
electroencephalography-positive group showed a dose-dependent increase in both cohorts.
When the dementia patients showed bispectral electroencephalography positive, their
mortality was significantly higher than those with dementia but who were bispectral
electroencephalography-negative. Mortality within 30 days in the bispectral
electroencephalography-positive group was significantly higher than that of the bispectral
electroencephalography-negative group. The utility of the bispectral
electroencephalography to predict mortality among large sample of 502 elderly patients was
shown. The bispectral electroencephalography score can predict mortality among elderly
patients in general, and even among dementia patients, as soon as 30 days.
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Affiliation(s)
- Taku Saito
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA.,Department of Psychiatry, School of Medicine, National Defense Medical College, Tokorozawa, Saitama 359-8513, Japan
| | - Johnny R Malicoat
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Lydia R Leyden
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | | | - Sydney S Jellison
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Hailey Long
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Mandy M Hellman
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | | | | | - Duachee Lo
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | | | | | - Aihide Yoshino
- Department of Psychiatry, School of Medicine, National Defense Medical College, Tokorozawa, Saitama 359-8513, Japan
| | - Hiroyuki Toda
- Department of Psychiatry, School of Medicine, National Defense Medical College, Tokorozawa, Saitama 359-8513, Japan
| | - Eri Shinozaki
- Department of Internal Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Hyunkeun R Cho
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA 52242, USA
| | - Sangil Lee
- Department of Emergency Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Gen Shinozaki
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA.,Department of Neurosurgery, University of Iowa, Iowa City, IA 52242, USA.,Department of Anesthesia, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA.,Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242, USA.,Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, IA 52242, USA
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