Quantitative multichannel EEG measure predicting the optimal weaning from ventilator in ICU patients with acute respiratory failure
The objective of this study was to develop a novel quantitative multichannel EEG (qEEG) based analysis method, called Global Field Damping Time (GFDT), in order to detect potential EEG changes of patients admitted to the ICU with acute respiratory failure, and correlate them to the patients' re...
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| Veröffentlicht in: | Clinical neurophysiology 2006-04, Vol.117 (4), p.752-770 |
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| creator | Papadelis, Christos Maglaveras, Nikos Kourtidou-Papadeli, Chrysoula Bamidis, Panagiotis Albani, Maria Chatzinikolaou, Kyriazis Pappas, Konstantinos |
| description | The objective of this study was to develop a novel quantitative multichannel EEG (qEEG) based analysis method, called Global Field Damping Time (GFDT), in order to detect potential EEG changes of patients admitted to the ICU with acute respiratory failure, and correlate them to the patients' recovery outcome predicting the optimal time-point to disconnect the patient from mechanical ventilation.
Twenty-nine adult patients with acute respiratory failure out of 98 admitted to the Intensive Care Unit of Saint Paul General Hospital were enrolled, and among them only 15 completed the study. The patients were classified in 3 groups according to their outcome after 3 months follow-up. The patients were intubated with fraction of inspired oxygen (FiO
2) of 100%. Neurological Deficit Scores (NDS) were measured 24
h after intubation to assess patients' neurological condition. Twenty-four hours after patient's intubation, FiO
2 was decreased to 40% (weaning session), followed by a 5
min early recovery session, a 5
min recovery 1 session and a 5
min recovery 2 session. EEG recordings were performed during this experimental procedure. Multichannel EEG segments were processed and fitted into a multivariate autoregressive (mAR) model, and single channel EEG segments into a scalar autoregressive (sAR) model. The mAR and the sAR models of arbitrary order
p were decomposed into
mp and
p oscillators and relaxators, respectively. Damping time of each oscillator and each relaxator, and the Global Field Damping Time (GFDT) as a weighted damping time were estimated for both mAR and sAR models.
A statistically significant increase of mAR model's GFDT during the weaning session was observed in the subjects of all groups. Comparing the 3 patients' groups, statistically significant differences for mAR model's GFDT were observed for the weaning and early recovery session. Linear regression analysis between NDS and mean mAR model's GFDT showed statistical significance during weaning session, early recovery session, and recovery 1 session. There was no statistical significance for SaO
2 in the regression analysis with NDS. The sAR model's GFDT presented worst results in comparison with the mAR modelling GFDT in the identification of hypoxic conditions during weaning session and in the discrimination of patients with acute respiratory failure according to their neurological outcome.
Global Field Damping Time as correlated to the patients' neurological outcome appears to be a simple, com |
| doi_str_mv | 10.1016/j.clinph.2005.12.009 |
| format | Article |
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Twenty-nine adult patients with acute respiratory failure out of 98 admitted to the Intensive Care Unit of Saint Paul General Hospital were enrolled, and among them only 15 completed the study. The patients were classified in 3 groups according to their outcome after 3 months follow-up. The patients were intubated with fraction of inspired oxygen (FiO
2) of 100%. Neurological Deficit Scores (NDS) were measured 24
h after intubation to assess patients' neurological condition. Twenty-four hours after patient's intubation, FiO
2 was decreased to 40% (weaning session), followed by a 5
min early recovery session, a 5
min recovery 1 session and a 5
min recovery 2 session. EEG recordings were performed during this experimental procedure. Multichannel EEG segments were processed and fitted into a multivariate autoregressive (mAR) model, and single channel EEG segments into a scalar autoregressive (sAR) model. The mAR and the sAR models of arbitrary order
p were decomposed into
mp and
p oscillators and relaxators, respectively. Damping time of each oscillator and each relaxator, and the Global Field Damping Time (GFDT) as a weighted damping time were estimated for both mAR and sAR models.
A statistically significant increase of mAR model's GFDT during the weaning session was observed in the subjects of all groups. Comparing the 3 patients' groups, statistically significant differences for mAR model's GFDT were observed for the weaning and early recovery session. Linear regression analysis between NDS and mean mAR model's GFDT showed statistical significance during weaning session, early recovery session, and recovery 1 session. There was no statistical significance for SaO
2 in the regression analysis with NDS. The sAR model's GFDT presented worst results in comparison with the mAR modelling GFDT in the identification of hypoxic conditions during weaning session and in the discrimination of patients with acute respiratory failure according to their neurological outcome.
Global Field Damping Time as correlated to the patients' neurological outcome appears to be a simple, compact, and substantial novel indicator of cerebral hypoxia and a potential predictor of the optimal time-point to disconnect the patient from the ventilator.
Quantitative EEG seems to be an important tool for ICU clinicians assisting them to decide for the patients' optimal time-point to disconnect the patient from the ventilator.</description><identifier>ISSN: 1388-2457</identifier><identifier>EISSN: 1872-8952</identifier><identifier>DOI: 10.1016/j.clinph.2005.12.009</identifier><identifier>PMID: 16495143</identifier><language>eng</language><publisher>Shannon: Elsevier Ireland Ltd</publisher><subject>Acute Disease - rehabilitation ; Acute respiratory failure ; Aged ; Aged, 80 and over ; Biological and medical sciences ; Biological Clocks ; Brain - physiopathology ; Cerebral hypoxia ; Electrodiagnosis. Electric activity recording ; Electroencephalography - methods ; Electroencephalography - standards ; Female ; Humans ; Hypoxia-Ischemia, Brain - diagnosis ; Hypoxia-Ischemia, Brain - physiopathology ; Hypoxia-Ischemia, Brain - prevention & control ; Intensive Care Units - standards ; Intensive Care Units - trends ; Investigative techniques, diagnostic techniques (general aspects) ; Male ; Medical sciences ; Middle Aged ; Models, Neurological ; Models, Statistical ; Monitoring, Physiologic - methods ; Monitoring, Physiologic - standards ; Multivariate AR model ; Nervous system ; Neurological outcome ; Neurology ; Predictive Value of Tests ; Prognosis ; Regression Analysis ; Respiration, Artificial - adverse effects ; Respiratory Insufficiency - complications ; Respiratory Insufficiency - physiopathology ; Respiratory Insufficiency - therapy ; Time Factors ; Vascular diseases and vascular malformations of the nervous system ; Ventilator Weaning - methods ; Ventilator Weaning - standards ; Weaning</subject><ispartof>Clinical neurophysiology, 2006-04, Vol.117 (4), p.752-770</ispartof><rights>2006 International Federation of Clinical Neurophysiology</rights><rights>2006 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c390t-e0d9f56460c279abbef4f4dfa38311202b22d24141236de92d3c29011a208e393</citedby><cites>FETCH-LOGICAL-c390t-e0d9f56460c279abbef4f4dfa38311202b22d24141236de92d3c29011a208e393</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.clinph.2005.12.009$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17640916$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16495143$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Papadelis, Christos</creatorcontrib><creatorcontrib>Maglaveras, Nikos</creatorcontrib><creatorcontrib>Kourtidou-Papadeli, Chrysoula</creatorcontrib><creatorcontrib>Bamidis, Panagiotis</creatorcontrib><creatorcontrib>Albani, Maria</creatorcontrib><creatorcontrib>Chatzinikolaou, Kyriazis</creatorcontrib><creatorcontrib>Pappas, Konstantinos</creatorcontrib><title>Quantitative multichannel EEG measure predicting the optimal weaning from ventilator in ICU patients with acute respiratory failure</title><title>Clinical neurophysiology</title><addtitle>Clin Neurophysiol</addtitle><description>The objective of this study was to develop a novel quantitative multichannel EEG (qEEG) based analysis method, called Global Field Damping Time (GFDT), in order to detect potential EEG changes of patients admitted to the ICU with acute respiratory failure, and correlate them to the patients' recovery outcome predicting the optimal time-point to disconnect the patient from mechanical ventilation.
Twenty-nine adult patients with acute respiratory failure out of 98 admitted to the Intensive Care Unit of Saint Paul General Hospital were enrolled, and among them only 15 completed the study. The patients were classified in 3 groups according to their outcome after 3 months follow-up. The patients were intubated with fraction of inspired oxygen (FiO
2) of 100%. Neurological Deficit Scores (NDS) were measured 24
h after intubation to assess patients' neurological condition. Twenty-four hours after patient's intubation, FiO
2 was decreased to 40% (weaning session), followed by a 5
min early recovery session, a 5
min recovery 1 session and a 5
min recovery 2 session. EEG recordings were performed during this experimental procedure. Multichannel EEG segments were processed and fitted into a multivariate autoregressive (mAR) model, and single channel EEG segments into a scalar autoregressive (sAR) model. The mAR and the sAR models of arbitrary order
p were decomposed into
mp and
p oscillators and relaxators, respectively. Damping time of each oscillator and each relaxator, and the Global Field Damping Time (GFDT) as a weighted damping time were estimated for both mAR and sAR models.
A statistically significant increase of mAR model's GFDT during the weaning session was observed in the subjects of all groups. Comparing the 3 patients' groups, statistically significant differences for mAR model's GFDT were observed for the weaning and early recovery session. Linear regression analysis between NDS and mean mAR model's GFDT showed statistical significance during weaning session, early recovery session, and recovery 1 session. There was no statistical significance for SaO
2 in the regression analysis with NDS. The sAR model's GFDT presented worst results in comparison with the mAR modelling GFDT in the identification of hypoxic conditions during weaning session and in the discrimination of patients with acute respiratory failure according to their neurological outcome.
Global Field Damping Time as correlated to the patients' neurological outcome appears to be a simple, compact, and substantial novel indicator of cerebral hypoxia and a potential predictor of the optimal time-point to disconnect the patient from the ventilator.
Quantitative EEG seems to be an important tool for ICU clinicians assisting them to decide for the patients' optimal time-point to disconnect the patient from the ventilator.</description><subject>Acute Disease - rehabilitation</subject><subject>Acute respiratory failure</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Biological and medical sciences</subject><subject>Biological Clocks</subject><subject>Brain - physiopathology</subject><subject>Cerebral hypoxia</subject><subject>Electrodiagnosis. Electric activity recording</subject><subject>Electroencephalography - methods</subject><subject>Electroencephalography - standards</subject><subject>Female</subject><subject>Humans</subject><subject>Hypoxia-Ischemia, Brain - diagnosis</subject><subject>Hypoxia-Ischemia, Brain - physiopathology</subject><subject>Hypoxia-Ischemia, Brain - prevention & control</subject><subject>Intensive Care Units - standards</subject><subject>Intensive Care Units - trends</subject><subject>Investigative techniques, diagnostic techniques (general aspects)</subject><subject>Male</subject><subject>Medical sciences</subject><subject>Middle Aged</subject><subject>Models, Neurological</subject><subject>Models, Statistical</subject><subject>Monitoring, Physiologic - methods</subject><subject>Monitoring, Physiologic - standards</subject><subject>Multivariate AR model</subject><subject>Nervous system</subject><subject>Neurological outcome</subject><subject>Neurology</subject><subject>Predictive Value of Tests</subject><subject>Prognosis</subject><subject>Regression Analysis</subject><subject>Respiration, Artificial - adverse effects</subject><subject>Respiratory Insufficiency - complications</subject><subject>Respiratory Insufficiency - physiopathology</subject><subject>Respiratory Insufficiency - therapy</subject><subject>Time Factors</subject><subject>Vascular diseases and vascular malformations of the nervous system</subject><subject>Ventilator Weaning - methods</subject><subject>Ventilator Weaning - standards</subject><subject>Weaning</subject><issn>1388-2457</issn><issn>1872-8952</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kUGL1DAYhoso7rr6D0Ry0VvrlzRNm4sgw7guLIjgnkMm_eJkSNOapLPs2T9uhhnYm6eEj-d9yfekqt5TaChQ8fnQGO_Csm8YQNdQ1gDIF9U1HXpWD7JjL8u9HYaa8a6_qt6kdACAHjh7XV1RwWVHeXtd_f256pBd1tkdkUyrz87sdQjoyXZ7SybUaY1IloijM9mF3yTvkcxLdpP25BF1OM1snCdyxFLkdZ4jcYHcbR7IUlrLMJFHl_dEmzUjiZgWF0_UE7Ha-dL-tnpltU_47nLeVA_ftr823-v7H7d3m6_3tWkl5BphlLYTXIBhvdS7HVpu-Wh1O7SUMmA7xkbGKaesFSNKNraGSaBUMxiwle1N9encu8T5z4opq8klg97rgPOalOh7ASVRQH4GTZxTimjVEsu-8UlRUCf56qDO8tVJvqJMFfkl9uHSv-4mHJ9DF9sF-HgBdDLa26iDcemZ6wUHSUXhvpw5LDaODqNKpog05Q8imqzG2f3_Jf8ANzmmuA</recordid><startdate>20060401</startdate><enddate>20060401</enddate><creator>Papadelis, Christos</creator><creator>Maglaveras, Nikos</creator><creator>Kourtidou-Papadeli, Chrysoula</creator><creator>Bamidis, Panagiotis</creator><creator>Albani, Maria</creator><creator>Chatzinikolaou, Kyriazis</creator><creator>Pappas, Konstantinos</creator><general>Elsevier Ireland Ltd</general><general>Elsevier Science</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20060401</creationdate><title>Quantitative multichannel EEG measure predicting the optimal weaning from ventilator in ICU patients with acute respiratory failure</title><author>Papadelis, Christos ; Maglaveras, Nikos ; Kourtidou-Papadeli, Chrysoula ; Bamidis, Panagiotis ; Albani, Maria ; Chatzinikolaou, Kyriazis ; Pappas, Konstantinos</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c390t-e0d9f56460c279abbef4f4dfa38311202b22d24141236de92d3c29011a208e393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Acute Disease - rehabilitation</topic><topic>Acute respiratory failure</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Biological and medical sciences</topic><topic>Biological Clocks</topic><topic>Brain - physiopathology</topic><topic>Cerebral hypoxia</topic><topic>Electrodiagnosis. Electric activity recording</topic><topic>Electroencephalography - methods</topic><topic>Electroencephalography - standards</topic><topic>Female</topic><topic>Humans</topic><topic>Hypoxia-Ischemia, Brain - diagnosis</topic><topic>Hypoxia-Ischemia, Brain - physiopathology</topic><topic>Hypoxia-Ischemia, Brain - prevention & control</topic><topic>Intensive Care Units - standards</topic><topic>Intensive Care Units - trends</topic><topic>Investigative techniques, diagnostic techniques (general aspects)</topic><topic>Male</topic><topic>Medical sciences</topic><topic>Middle Aged</topic><topic>Models, Neurological</topic><topic>Models, Statistical</topic><topic>Monitoring, Physiologic - methods</topic><topic>Monitoring, Physiologic - standards</topic><topic>Multivariate AR model</topic><topic>Nervous system</topic><topic>Neurological outcome</topic><topic>Neurology</topic><topic>Predictive Value of Tests</topic><topic>Prognosis</topic><topic>Regression Analysis</topic><topic>Respiration, Artificial - adverse effects</topic><topic>Respiratory Insufficiency - complications</topic><topic>Respiratory Insufficiency - physiopathology</topic><topic>Respiratory Insufficiency - therapy</topic><topic>Time Factors</topic><topic>Vascular diseases and vascular malformations of the nervous system</topic><topic>Ventilator Weaning - methods</topic><topic>Ventilator Weaning - standards</topic><topic>Weaning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Papadelis, Christos</creatorcontrib><creatorcontrib>Maglaveras, Nikos</creatorcontrib><creatorcontrib>Kourtidou-Papadeli, Chrysoula</creatorcontrib><creatorcontrib>Bamidis, Panagiotis</creatorcontrib><creatorcontrib>Albani, Maria</creatorcontrib><creatorcontrib>Chatzinikolaou, Kyriazis</creatorcontrib><creatorcontrib>Pappas, Konstantinos</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Clinical neurophysiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Papadelis, Christos</au><au>Maglaveras, Nikos</au><au>Kourtidou-Papadeli, Chrysoula</au><au>Bamidis, Panagiotis</au><au>Albani, Maria</au><au>Chatzinikolaou, Kyriazis</au><au>Pappas, Konstantinos</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantitative multichannel EEG measure predicting the optimal weaning from ventilator in ICU patients with acute respiratory failure</atitle><jtitle>Clinical neurophysiology</jtitle><addtitle>Clin Neurophysiol</addtitle><date>2006-04-01</date><risdate>2006</risdate><volume>117</volume><issue>4</issue><spage>752</spage><epage>770</epage><pages>752-770</pages><issn>1388-2457</issn><eissn>1872-8952</eissn><abstract>The objective of this study was to develop a novel quantitative multichannel EEG (qEEG) based analysis method, called Global Field Damping Time (GFDT), in order to detect potential EEG changes of patients admitted to the ICU with acute respiratory failure, and correlate them to the patients' recovery outcome predicting the optimal time-point to disconnect the patient from mechanical ventilation.
Twenty-nine adult patients with acute respiratory failure out of 98 admitted to the Intensive Care Unit of Saint Paul General Hospital were enrolled, and among them only 15 completed the study. The patients were classified in 3 groups according to their outcome after 3 months follow-up. The patients were intubated with fraction of inspired oxygen (FiO
2) of 100%. Neurological Deficit Scores (NDS) were measured 24
h after intubation to assess patients' neurological condition. Twenty-four hours after patient's intubation, FiO
2 was decreased to 40% (weaning session), followed by a 5
min early recovery session, a 5
min recovery 1 session and a 5
min recovery 2 session. EEG recordings were performed during this experimental procedure. Multichannel EEG segments were processed and fitted into a multivariate autoregressive (mAR) model, and single channel EEG segments into a scalar autoregressive (sAR) model. The mAR and the sAR models of arbitrary order
p were decomposed into
mp and
p oscillators and relaxators, respectively. Damping time of each oscillator and each relaxator, and the Global Field Damping Time (GFDT) as a weighted damping time were estimated for both mAR and sAR models.
A statistically significant increase of mAR model's GFDT during the weaning session was observed in the subjects of all groups. Comparing the 3 patients' groups, statistically significant differences for mAR model's GFDT were observed for the weaning and early recovery session. Linear regression analysis between NDS and mean mAR model's GFDT showed statistical significance during weaning session, early recovery session, and recovery 1 session. There was no statistical significance for SaO
2 in the regression analysis with NDS. The sAR model's GFDT presented worst results in comparison with the mAR modelling GFDT in the identification of hypoxic conditions during weaning session and in the discrimination of patients with acute respiratory failure according to their neurological outcome.
Global Field Damping Time as correlated to the patients' neurological outcome appears to be a simple, compact, and substantial novel indicator of cerebral hypoxia and a potential predictor of the optimal time-point to disconnect the patient from the ventilator.
Quantitative EEG seems to be an important tool for ICU clinicians assisting them to decide for the patients' optimal time-point to disconnect the patient from the ventilator.</abstract><cop>Shannon</cop><pub>Elsevier Ireland Ltd</pub><pmid>16495143</pmid><doi>10.1016/j.clinph.2005.12.009</doi><tpages>19</tpages></addata></record> |
| fulltext | fulltext |
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| ispartof | Clinical neurophysiology, 2006-04, Vol.117 (4), p.752-770 |
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| subjects | Acute Disease - rehabilitation Acute respiratory failure Aged Aged, 80 and over Biological and medical sciences Biological Clocks Brain - physiopathology Cerebral hypoxia Electrodiagnosis. Electric activity recording Electroencephalography - methods Electroencephalography - standards Female Humans Hypoxia-Ischemia, Brain - diagnosis Hypoxia-Ischemia, Brain - physiopathology Hypoxia-Ischemia, Brain - prevention & control Intensive Care Units - standards Intensive Care Units - trends Investigative techniques, diagnostic techniques (general aspects) Male Medical sciences Middle Aged Models, Neurological Models, Statistical Monitoring, Physiologic - methods Monitoring, Physiologic - standards Multivariate AR model Nervous system Neurological outcome Neurology Predictive Value of Tests Prognosis Regression Analysis Respiration, Artificial - adverse effects Respiratory Insufficiency - complications Respiratory Insufficiency - physiopathology Respiratory Insufficiency - therapy Time Factors Vascular diseases and vascular malformations of the nervous system Ventilator Weaning - methods Ventilator Weaning - standards Weaning |
| title | Quantitative multichannel EEG measure predicting the optimal weaning from ventilator in ICU patients with acute respiratory failure |
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