Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloA predictive model for obstructive sleep apnea and Down syndrome
Anno di pubblicazione2017
Autore/iSkotko, Brian G.; Macklin, Eric A.; Muselli, Marco; Voelz, Lauren; McDonough, Mary Ellen; Davidson, Emily; Allareddy, Veerasathpurush; Jayaratne, Yasas S. N.; Bruun, Richard; Ching, Nicholas; Weintraub, Gil; Gozal, David; Rosen, Dennis
Affiliazioni autoriMassachusetts Gen Hosp; Harvard Med Sch; Massachusetts Gen Hosp; Harvard Med Sch; Rulex Inc; Italian Natl Res Council; Boston Childrens Hosp; Univ Iowa; Univ Connecticut; Boston Childrens Hosp; Childrens Dent; Beth Israel Deaconess Med Ctr; Univ Chicago; Boston Childrens Hosp
Autori CNR e affiliazioni
  • inglese
AbstractObstructive sleep apnea (OSA) occurs frequently in people with Down syndrome (DS) with reported prevalences ranging between 55% and 97%, compared to 1-4% in the neurotypical pediatric population. Sleep studies are often uncomfortable, costly, and poorly tolerated by individuals with DS. The objective of this study was to construct a tool to identify individuals with DS unlikely to have moderate or severe sleep OSA and in whom sleep studies might offer little benefit. An observational, prospective cohort study was performed in an outpatient clinic and overnight sleep study center with 130 DS patients, ages 3-24 years. Exclusion criteria included previous adenoid and/or tonsil removal, a sleep study within the past 6 months, or being treated for apnea with continuous positive airway pressure. This study involved a physical examination/medical history, lateral cephalogram, 3D photograph, validated sleep questionnaires, an overnight polysomnogram, and urine samples. The main outcome measure was the apnea-hypopnea index. Using a Logic Learning Machine, the best model had a cross-validated negative predictive value of 73% for mild obstructive sleep apnea and 90% for moderate or severe obstructive sleep apnea; positive predictive values were 55% and 25%, respectively. The model included variables from survey questions, medication history, anthropometric measurements, vital signs, patient's age, and physical examination findings. With simple procedures that can be collected at minimal cost, the proposed model could predict which patients with DS were unlikely to have moderate to severe obstructive sleep apnea and thus may not need a diagnostic sleep study.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da889
Pagine a896
Pagine totali8
RivistaAmerican journal of medical genetics. Part A
Attiva dal 2003
Editore: Wiley-Liss, - Hoboken, N.J.
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1552-4825
Titolo chiave: American journal of medical genetics. Part A
Titolo proprio: American journal of medical genetics.
Titolo abbreviato: Am. j. med. genet., Part A
Numero volume della rivista173
Fascicolo della rivista4
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000397855700007)
Parole chiaveDown syndrome, obstructive sleep apnea, trisomy 21
Link (URL, URI)-
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Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli/Attività/Sottoprogetti CNR
  • INT.P02.002.001 : Machine Learning for Biological Data
Progetti Europei-