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Unbiased categorical classification of pediatric sleep disordered breathing

Karen Spruyt, Gino Verleye UGent and David Gozal (2010) SLEEP. 33(10). p.1341-1347
abstract
Study Objectives: To classify pediatric sleep disordered breathing (SDB) using unbiased approaches. In children, decisions regarding severity and treatment of SDB are conducted solely based on empirical observations. Although recognizable entities clearly exist under the SDB spectrum, neither the number of SDB categories nor their specific criteria have been critically defined. Design: retrospective cohort analysis and random prospective cohort Setting: community and clinical sample Patients or Participants: Urban 5- to 9-year-old community children undergoing overnight sleep study (NPSG), and a comparable prospectively recruited clinical SDB sample. Interventions: n/a Measurements and Results: Principal component analysis was used to identify the uniqueness of the polysomnographically derived measures that are routinely used in clinical settings: apnea-hypopnea index, apnea index, obstructive apnea index, nadir SpO(2), spontaneous arousal index and respiratory arousal index. These measures were then incorporated using unbiased data mining approaches to further characterize and discriminate across categorical phenotypes. Of 1,133 subjects, 52.8% were habitual snorers. Six categorical phenotypes clustered without any a priori hypothesis. Secondly, a non-hierarchical model that incorporated 6 NPSG-derived measures enabled unbiased identification of algorithms that predicted these 6 severity-based clusters. Thirdly, a hierarchical model was developed and performed well on all severity-based clusters. Classification and predictive models were subsequently cross-validated statistically as well as clinically, using 2 additional datasets that included 259 subjects. Modeling reached similar to 93% accuracy in cluster assignment. Conclusions: Data-driven analysis of conventional NPSG-derived indices identified 6 distinct clusters ranging from a cluster with normal indices toward clusters with more abnormal indices. Categorical assignment of individual cases to any of such clusters can be accurately predicted using a simple algorithm. These clusters,may further enable prospective unbiased characterization of clinical outcomes and of genotype-phenotype interactions across multiple datasets.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
PRESCHOOLERS, SPECTRUM, HYPERACTIVITY, INDEX, Sleep disordered breathing, snoring, spectrum, obstructive sleep apnea, APNEA SYNDROME, QUALITY-OF-LIFE, CHILDREN, SYMPTOMS
journal title
SLEEP
Sleep
volume
33
issue
10
pages
1341 - 1347
Web of Science type
Article
Web of Science id
000282249700011
JCR category
CLINICAL NEUROLOGY
JCR impact factor
5.486 (2010)
JCR rank
14/185 (2010)
JCR quartile
1 (2010)
ISSN
0161-8105
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1054648
handle
http://hdl.handle.net/1854/LU-1054648
date created
2010-10-06 11:36:41
date last changed
2015-06-17 09:14:33
@article{1054648,
  abstract     = {Study Objectives: To classify pediatric sleep disordered breathing (SDB) using unbiased approaches. In children, decisions regarding severity and treatment of SDB are conducted solely based on empirical observations. Although recognizable entities clearly exist under the SDB spectrum, neither the number of SDB categories nor their specific criteria have been critically defined. Design: retrospective cohort analysis and random prospective cohort Setting: community and clinical sample Patients or Participants: Urban 5- to 9-year-old community children undergoing overnight sleep study (NPSG), and a comparable prospectively recruited clinical SDB sample. Interventions: n/a Measurements and Results: Principal component analysis was used to identify the uniqueness of the polysomnographically derived measures that are routinely used in clinical settings: apnea-hypopnea index, apnea index, obstructive apnea index, nadir SpO(2), spontaneous arousal index and respiratory arousal index. These measures were then incorporated using unbiased data mining approaches to further characterize and discriminate across categorical phenotypes. Of 1,133 subjects, 52.8\% were habitual snorers. Six categorical phenotypes clustered without any a priori hypothesis. Secondly, a non-hierarchical model that incorporated 6 NPSG-derived measures enabled unbiased identification of algorithms that predicted these 6 severity-based clusters. Thirdly, a hierarchical model was developed and performed well on all severity-based clusters. Classification and predictive models were subsequently cross-validated statistically as well as clinically, using 2 additional datasets that included 259 subjects. Modeling reached similar to 93\% accuracy in cluster assignment. Conclusions: Data-driven analysis of conventional NPSG-derived indices identified 6 distinct clusters ranging from a cluster with normal indices toward clusters with more abnormal indices. Categorical assignment of individual cases to any of such clusters can be accurately predicted using a simple algorithm. These clusters,may further enable prospective unbiased characterization of clinical outcomes and of genotype-phenotype interactions across multiple datasets.},
  author       = {Spruyt, Karen  and Verleye, Gino and Gozal, David},
  issn         = {0161-8105},
  journal      = {SLEEP},
  keyword      = {PRESCHOOLERS,SPECTRUM,HYPERACTIVITY,INDEX,Sleep disordered breathing,snoring,spectrum,obstructive sleep apnea,APNEA SYNDROME,QUALITY-OF-LIFE,CHILDREN,SYMPTOMS},
  language     = {eng},
  number       = {10},
  pages        = {1341--1347},
  title        = {Unbiased categorical classification of pediatric sleep disordered breathing},
  volume       = {33},
  year         = {2010},
}

Chicago
Spruyt, Karen , Gino Verleye, and David Gozal. 2010. “Unbiased Categorical Classification of Pediatric Sleep Disordered Breathing.” Sleep 33 (10): 1341–1347.
APA
Spruyt, K., Verleye, G., & Gozal, D. (2010). Unbiased categorical classification of pediatric sleep disordered breathing. SLEEP, 33(10), 1341–1347.
Vancouver
1.
Spruyt K, Verleye G, Gozal D. Unbiased categorical classification of pediatric sleep disordered breathing. SLEEP. 2010;33(10):1341–7.
MLA
Spruyt, Karen , Gino Verleye, and David Gozal. “Unbiased Categorical Classification of Pediatric Sleep Disordered Breathing.” SLEEP 33.10 (2010): 1341–1347. Print.