Evidence map›Paper›PMID 36426646›Full record

ArticleAmerican journal of medical genetics. Part A2023

Validation of a predictive model for obstructive sleep apnea in people with Down syndrome.

Brian G Skotko, Alexandra Garza Flores, Ibrahim Elsharkawi, Vasiliki Patsiogiannis, Mary Ellen McDonough, Damiano Verda, Marco Muselli, Roberto Hornero, David Gozal, Eric A Macklin

Open access · greenAbstract read
In one paragraph

Article in American journal of medical genetics. Part A, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 36% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 7 institutions in 3 countries.

Brian G SkotkoDown Syndrome Program, Division of Medical Genetics and Metabolism, Department of Pediatrics, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID 0000-0002-5232-9882
Alexandra Garza FloresDown Syndrome Program, Division of Medical Genetics and Metabolism, Department of Pediatrics, Massachusetts General Hospital, Boston, Massachusetts, USA.
Ibrahim ElsharkawiDown Syndrome Program, Division of Medical Genetics and Metabolism, Department of Pediatrics, Massachusetts General Hospital, Boston, Massachusetts, USA.
Vasiliki PatsiogiannisDown Syndrome Program, Division of Medical Genetics and Metabolism, Department of Pediatrics, Massachusetts General Hospital, Boston, Massachusetts, USA.
Mary Ellen McDonoughDown Syndrome Program, Division of Medical Genetics and Metabolism, Department of Pediatrics, Massachusetts General Hospital, Boston, Massachusetts, USA.
Damiano VerdaRulex Innovation Labs, Genoa, Italy.ORCID 0000-0001-9912-3454
Marco MuselliRulex Innovation Labs, Genoa, Italy.
Roberto HorneroBiomedical Engineering Group, University of Valladolid, Valladolid, Spain.
David GozalDepartment of Child Health and The Child Health Research Institute, The University of Missouri School of Medicine, Columbia, Missouri, USA.ORCID 0000-0001-8195-6036
Eric A MacklinBiostatistics Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0003-1618-3502
Massachusetts General Hospital · USHarvard University · USChildren's Hospital of Philadelphia · USInstitute of Electronics, Computer and Telecommunication Engineering · ITUniversidad de Valladolid · ESUniversity of Genoa · ITUniversity of Missouri Health System · US

Funding

TRAINING GRANT IN GENETICST32GM007748 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI Anne O'Donnell-Luria, Louise Wilkins-Haug · 1985 to 2026
$12.0M
Predicting Obstructive Sleep Apnea in Down SyndromeF32HD068101 · NICHD · MASSACHUSETTS GENERAL HOSPITAL · PI SKOTKO, BRIAN GARY · 2011 to 2012
$116k
NICHD NIH HHS F32 HD068101NIGMS NIH HHS T32 GM007748
6 · The paper itself

Abstract

Detecting obstructive sleep apnea (OSA) is important to both prevent significant comorbidities in people with Down syndrome (DS) and untangle contributions to other behavioral and mental health diagnoses. However, laboratory-based polysomnograms are often poorly tolerated, unavailable, or not covered by health insurance for this population. In previous work, our team developed a prediction model that seemed to hold promise in identifying which people with DS might not have significant apnea and, consequently, might be able to forgo a diagnostic polysomnogram. In this study, we sought to validate these findings in a novel set of participants with DS. We recruited an additional 64 participants with DS, ages 3-35 years. Caregivers completed the same validated questionnaires, and our study team collected vital signs, physical exam findings, and medical histories that were previously shown to be predictive. Patients then had a laboratory-based polysomnogram. The best modeling had a validated negative predictive value of 50% for an apnea-hypopnea index (AHI) > 1/hTST and 73.7% for AHI >5/hTST. The positive predictive values were 60% and 39.1%, respectively. As such, a clinically reliable screening tool for OSA in people with DS was not achieved. Patients with DS should continue to be monitored for OSA according to current healthcare guidelines.

Indexed as

Down SyndromeSleep Apnea, ObstructiveAdolescentAdultChildChild, PreschoolComorbidityHumansPolysomnographySurveys and QuestionnairesYoung AdultDown syndromeobstructive sleep apneatrisomy 21

Identifiers

PMID36426646
PMCPMC9988250
OpenAlexW4310080470

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.