Comparison of two common aEEG classifications for the prediction of neurodevelopmental outcome in preterm infants
Neurodevelopmental outcome after prematurity is crucial. The aim was to compare two amplitude-integrated EEG (aEEG) classifications (Hellström-Westas (HW), Burdjalov) for outcome prediction. We recruited 65 infants ≤32 weeks gestational age with aEEG recordings within the first 72 h of life and Bayl...
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description | Neurodevelopmental outcome after prematurity is crucial. The aim was to compare two amplitude-integrated EEG (aEEG) classifications (Hellström-Westas (HW), Burdjalov) for outcome prediction. We recruited 65 infants ≤32 weeks gestational age with aEEG recordings within the first 72 h of life and Bayley testing at 24 months corrected age or death. Statistical analyses were performed for each 24 h section to determine whether very immature/depressed or mature/developed patterns predict survival/neurological outcome and to find predictors for mental development index (MDI) and psychomotor development index (PDI) at 24 months corrected age. On day 2, deceased infants showed no cycling in 80% (HW,
p
= 0.0140) and 100% (Burdjalov,
p
= 0.0041). The Burdjalov total score significantly differed between groups on day 2 (
p
= 0.0284) and the adapted Burdjalov total score on day 2 (
p
= 0.0183) and day 3 (
p
= 0.0472). Cycling on day 3 (HW;
p
= 0.0059) and background on day 3 (HW;
p
= 0.0212) are independent predictors for MDI (
p
= 0.0016) whereas no independent predictor for PDI was found (multiple regression analyses).
Conclusion
: Cycling in both classifications is a valuable tool to assess chance of survival. The classification by HW is also associated with long-term mental outcome.
What is Known:
•
Neurodevelopmental outcome after preterm birth remains one of the major concerns in neonatology.
•aEEG is used to measure brain activity and brain maturation in preterm infants.
What is New:
•
The two common aEEG classifications and scoring systems described by Hellström-Westas and Burdjalov are valuable tools to predict neurodevelopmental outcome when performed within the first 72 h of life.
•Both aEEG classifications are useful to predict chance of survival. The classification by Hellström-Westas can also predict long-term outcome at corrected age of 2 years. |
doi_str_mv | 10.1007/s00431-016-2816-5 |
format | Article |
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p
= 0.0140) and 100% (Burdjalov,
p
= 0.0041). The Burdjalov total score significantly differed between groups on day 2 (
p
= 0.0284) and the adapted Burdjalov total score on day 2 (
p
= 0.0183) and day 3 (
p
= 0.0472). Cycling on day 3 (HW;
p
= 0.0059) and background on day 3 (HW;
p
= 0.0212) are independent predictors for MDI (
p
= 0.0016) whereas no independent predictor for PDI was found (multiple regression analyses).
Conclusion
: Cycling in both classifications is a valuable tool to assess chance of survival. The classification by HW is also associated with long-term mental outcome.
What is Known:
•
Neurodevelopmental outcome after preterm birth remains one of the major concerns in neonatology.
•aEEG is used to measure brain activity and brain maturation in preterm infants.
What is New:
•
The two common aEEG classifications and scoring systems described by Hellström-Westas and Burdjalov are valuable tools to predict neurodevelopmental outcome when performed within the first 72 h of life.
•Both aEEG classifications are useful to predict chance of survival. The classification by Hellström-Westas can also predict long-term outcome at corrected age of 2 years.</description><identifier>ISSN: 0340-6199</identifier><identifier>EISSN: 1432-1076</identifier><identifier>DOI: 10.1007/s00431-016-2816-5</identifier><identifier>PMID: 27924356</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Analysis of Variance ; Brain - physiology ; Chi-Square Distribution ; Child Development ; Child, Preschool ; Classification ; Convulsions & seizures ; Electroencephalography - classification ; Electroencephalography - methods ; Electroencephalography - statistics & numerical data ; Female ; Gestational Age ; Humans ; Infant ; Infant, Newborn ; Infant, Premature ; Intensive care ; Intensive Care Units, Neonatal - statistics & numerical data ; Male ; Medicine ; Medicine & Public Health ; Neonatal care ; Neurodevelopmental Disorders - diagnosis ; Neurodevelopmental Disorders - mortality ; Newborn babies ; Original ; Original Article ; Pediatrics ; Premature babies ; Premature birth ; Prognosis ; Retrospective Studies ; Sensitivity and Specificity ; Statistics, Nonparametric</subject><ispartof>European journal of pediatrics, 2017-02, Vol.176 (2), p.163-171</ispartof><rights>The Author(s) 2016</rights><rights>European Journal of Pediatrics is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c503t-18b3027eec0232643f751beca832850fe860f6211b1d6e7b6feab9f53b367e7e3</citedby><cites>FETCH-LOGICAL-c503t-18b3027eec0232643f751beca832850fe860f6211b1d6e7b6feab9f53b367e7e3</cites><orcidid>0000-0003-3809-1887</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00431-016-2816-5$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00431-016-2816-5$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27924356$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bruns, Nora</creatorcontrib><creatorcontrib>Dransfeld, Frauke</creatorcontrib><creatorcontrib>Hüning, Britta</creatorcontrib><creatorcontrib>Hobrecht, Julia</creatorcontrib><creatorcontrib>Storbeck, Tobias</creatorcontrib><creatorcontrib>Weiss, Christel</creatorcontrib><creatorcontrib>Felderhoff-Müser, Ursula</creatorcontrib><creatorcontrib>Müller, Hanna</creatorcontrib><title>Comparison of two common aEEG classifications for the prediction of neurodevelopmental outcome in preterm infants</title><title>European journal of pediatrics</title><addtitle>Eur J Pediatr</addtitle><addtitle>Eur J Pediatr</addtitle><description>Neurodevelopmental outcome after prematurity is crucial. The aim was to compare two amplitude-integrated EEG (aEEG) classifications (Hellström-Westas (HW), Burdjalov) for outcome prediction. We recruited 65 infants ≤32 weeks gestational age with aEEG recordings within the first 72 h of life and Bayley testing at 24 months corrected age or death. Statistical analyses were performed for each 24 h section to determine whether very immature/depressed or mature/developed patterns predict survival/neurological outcome and to find predictors for mental development index (MDI) and psychomotor development index (PDI) at 24 months corrected age. On day 2, deceased infants showed no cycling in 80% (HW,
p
= 0.0140) and 100% (Burdjalov,
p
= 0.0041). The Burdjalov total score significantly differed between groups on day 2 (
p
= 0.0284) and the adapted Burdjalov total score on day 2 (
p
= 0.0183) and day 3 (
p
= 0.0472). Cycling on day 3 (HW;
p
= 0.0059) and background on day 3 (HW;
p
= 0.0212) are independent predictors for MDI (
p
= 0.0016) whereas no independent predictor for PDI was found (multiple regression analyses).
Conclusion
: Cycling in both classifications is a valuable tool to assess chance of survival. The classification by HW is also associated with long-term mental outcome.
What is Known:
•
Neurodevelopmental outcome after preterm birth remains one of the major concerns in neonatology.
•aEEG is used to measure brain activity and brain maturation in preterm infants.
What is New:
•
The two common aEEG classifications and scoring systems described by Hellström-Westas and Burdjalov are valuable tools to predict neurodevelopmental outcome when performed within the first 72 h of life.
•Both aEEG classifications are useful to predict chance of survival. The classification by Hellström-Westas can also predict long-term outcome at corrected age of 2 years.</description><subject>Analysis of Variance</subject><subject>Brain - physiology</subject><subject>Chi-Square Distribution</subject><subject>Child Development</subject><subject>Child, Preschool</subject><subject>Classification</subject><subject>Convulsions & seizures</subject><subject>Electroencephalography - classification</subject><subject>Electroencephalography - methods</subject><subject>Electroencephalography - statistics & numerical data</subject><subject>Female</subject><subject>Gestational Age</subject><subject>Humans</subject><subject>Infant</subject><subject>Infant, Newborn</subject><subject>Infant, Premature</subject><subject>Intensive care</subject><subject>Intensive Care Units, Neonatal - statistics & numerical data</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neonatal care</subject><subject>Neurodevelopmental Disorders - diagnosis</subject><subject>Neurodevelopmental Disorders - mortality</subject><subject>Newborn babies</subject><subject>Original</subject><subject>Original Article</subject><subject>Pediatrics</subject><subject>Premature babies</subject><subject>Premature birth</subject><subject>Prognosis</subject><subject>Retrospective Studies</subject><subject>Sensitivity and Specificity</subject><subject>Statistics, Nonparametric</subject><issn>0340-6199</issn><issn>1432-1076</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqNkU1rFTEUhoMo9rb1B7iRATduxuZj8rUR5HKtQqEbuw6Z3JM2ZSaZJjMt_nsz3LZUQXCTr_Oc95ycF6H3BH8mGMuzgnHHSIuJaKmqC3-FNqRjtCVYitdog1mHW0G0PkLHpdzimqOJeouOqNS0Y1xs0N02jZPNoaTYJN_MD6lxaRzrze52540bbCnBB2fnkGJpfMrNfAPNlGEf3Pq2ZkVYctrDPQxpGiHOdmjSMlcdaEJc2RnyWI_exrmcojfeDgXePe4n6Orb7uf2e3txef5j-_WidRyzuSWqZ5hKAIcpo6JjXnLSg7OKUcWxByWwF5SQnuwFyF54sL32nPVMSJDATtCXg-609CPsXe0r28FMOYw2_zLJBvNnJIYbc53uDa-j0VhUgU-PAjndLVBmM4biYBhshLQUQ5SovWjF9X-gnZBEC7yiH_9Cb9OSY53EKlhrayW7SpED5XIqJYN_7ptgs3pvDt6b6r1ZvTe85nx4-eHnjCezK0APQKmheA35Rel_qv4GAwC7xQ</recordid><startdate>20170201</startdate><enddate>20170201</enddate><creator>Bruns, Nora</creator><creator>Dransfeld, Frauke</creator><creator>Hüning, Britta</creator><creator>Hobrecht, Julia</creator><creator>Storbeck, Tobias</creator><creator>Weiss, Christel</creator><creator>Felderhoff-Müser, Ursula</creator><creator>Müller, Hanna</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>C6C</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>3V.</scope><scope>7RV</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9-</scope><scope>K9.</scope><scope>KB0</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-3809-1887</orcidid></search><sort><creationdate>20170201</creationdate><title>Comparison of two common aEEG classifications for the prediction of neurodevelopmental outcome in preterm infants</title><author>Bruns, Nora ; Dransfeld, Frauke ; Hüning, Britta ; Hobrecht, Julia ; Storbeck, Tobias ; Weiss, Christel ; Felderhoff-Müser, Ursula ; Müller, Hanna</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c503t-18b3027eec0232643f751beca832850fe860f6211b1d6e7b6feab9f53b367e7e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Analysis of Variance</topic><topic>Brain - physiology</topic><topic>Chi-Square Distribution</topic><topic>Child Development</topic><topic>Child, Preschool</topic><topic>Classification</topic><topic>Convulsions & seizures</topic><topic>Electroencephalography - classification</topic><topic>Electroencephalography - methods</topic><topic>Electroencephalography - statistics & numerical data</topic><topic>Female</topic><topic>Gestational Age</topic><topic>Humans</topic><topic>Infant</topic><topic>Infant, Newborn</topic><topic>Infant, Premature</topic><topic>Intensive care</topic><topic>Intensive Care Units, Neonatal - statistics & numerical data</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neonatal care</topic><topic>Neurodevelopmental Disorders - diagnosis</topic><topic>Neurodevelopmental Disorders - mortality</topic><topic>Newborn babies</topic><topic>Original</topic><topic>Original Article</topic><topic>Pediatrics</topic><topic>Premature babies</topic><topic>Premature birth</topic><topic>Prognosis</topic><topic>Retrospective Studies</topic><topic>Sensitivity and Specificity</topic><topic>Statistics, Nonparametric</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bruns, Nora</creatorcontrib><creatorcontrib>Dransfeld, Frauke</creatorcontrib><creatorcontrib>Hüning, Britta</creatorcontrib><creatorcontrib>Hobrecht, Julia</creatorcontrib><creatorcontrib>Storbeck, Tobias</creatorcontrib><creatorcontrib>Weiss, Christel</creatorcontrib><creatorcontrib>Felderhoff-Müser, Ursula</creatorcontrib><creatorcontrib>Müller, Hanna</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>European journal of pediatrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bruns, Nora</au><au>Dransfeld, Frauke</au><au>Hüning, Britta</au><au>Hobrecht, Julia</au><au>Storbeck, Tobias</au><au>Weiss, Christel</au><au>Felderhoff-Müser, Ursula</au><au>Müller, Hanna</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of two common aEEG classifications for the prediction of neurodevelopmental outcome in preterm infants</atitle><jtitle>European journal of pediatrics</jtitle><stitle>Eur J Pediatr</stitle><addtitle>Eur J Pediatr</addtitle><date>2017-02-01</date><risdate>2017</risdate><volume>176</volume><issue>2</issue><spage>163</spage><epage>171</epage><pages>163-171</pages><issn>0340-6199</issn><eissn>1432-1076</eissn><abstract>Neurodevelopmental outcome after prematurity is crucial. The aim was to compare two amplitude-integrated EEG (aEEG) classifications (Hellström-Westas (HW), Burdjalov) for outcome prediction. We recruited 65 infants ≤32 weeks gestational age with aEEG recordings within the first 72 h of life and Bayley testing at 24 months corrected age or death. Statistical analyses were performed for each 24 h section to determine whether very immature/depressed or mature/developed patterns predict survival/neurological outcome and to find predictors for mental development index (MDI) and psychomotor development index (PDI) at 24 months corrected age. On day 2, deceased infants showed no cycling in 80% (HW,
p
= 0.0140) and 100% (Burdjalov,
p
= 0.0041). The Burdjalov total score significantly differed between groups on day 2 (
p
= 0.0284) and the adapted Burdjalov total score on day 2 (
p
= 0.0183) and day 3 (
p
= 0.0472). Cycling on day 3 (HW;
p
= 0.0059) and background on day 3 (HW;
p
= 0.0212) are independent predictors for MDI (
p
= 0.0016) whereas no independent predictor for PDI was found (multiple regression analyses).
Conclusion
: Cycling in both classifications is a valuable tool to assess chance of survival. The classification by HW is also associated with long-term mental outcome.
What is Known:
•
Neurodevelopmental outcome after preterm birth remains one of the major concerns in neonatology.
•aEEG is used to measure brain activity and brain maturation in preterm infants.
What is New:
•
The two common aEEG classifications and scoring systems described by Hellström-Westas and Burdjalov are valuable tools to predict neurodevelopmental outcome when performed within the first 72 h of life.
•Both aEEG classifications are useful to predict chance of survival. The classification by Hellström-Westas can also predict long-term outcome at corrected age of 2 years.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>27924356</pmid><doi>10.1007/s00431-016-2816-5</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-3809-1887</orcidid><oa>free_for_read</oa></addata></record> |
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source | MEDLINE; SpringerLink Journals - AutoHoldings |
subjects | Analysis of Variance Brain - physiology Chi-Square Distribution Child Development Child, Preschool Classification Convulsions & seizures Electroencephalography - classification Electroencephalography - methods Electroencephalography - statistics & numerical data Female Gestational Age Humans Infant Infant, Newborn Infant, Premature Intensive care Intensive Care Units, Neonatal - statistics & numerical data Male Medicine Medicine & Public Health Neonatal care Neurodevelopmental Disorders - diagnosis Neurodevelopmental Disorders - mortality Newborn babies Original Original Article Pediatrics Premature babies Premature birth Prognosis Retrospective Studies Sensitivity and Specificity Statistics, Nonparametric |
title | Comparison of two common aEEG classifications for the prediction of neurodevelopmental outcome in preterm infants |
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