Validation of My Family Health Portrait for six common heritable conditions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-06

AUTHORS

Flavia M. Facio, W. Gregory Feero, Amy Linn, Neal Oden, Kandamurugu Manickam, Leslie G. Biesecker

ABSTRACT

PURPOSE: To assess the ability of My Family Health Portrait to accurately collect family history for six common heritable disorders. BACKGROUND: Family history is useful to assess disease risk but is not widely used. We compared the pedigree from My Family Health Portrait, an online tool for collection of family history, to a pedigree supplemented by a genetics professional. METHODS: One hundred fifty volunteers collected their family histories using My Family Health Portrait. A genetic counselor interviewed the volunteers to validate the entries and add diagnoses, as needed. The content and the affection assignments of the pedigrees were compared. The pedigrees were entered into Family Healthware to assess risks for the diseases. RESULTS: The sensitivity of My Family Health Portrait varied among the six diseases (67-100%) compared to the supplemented pedigree. The specificities ranged from 92 to 100%. When the pedigrees were used to generate risk scores, My Family Health Portrait yielded identical risks to the supplemented pedigree for 94-99% of the volunteers for diabetes and colon, breast, and ovarian cancer. The agreement was lower for coronary artery disease (68%) and stroke (83%). CONCLUSIONS: These data support the validity of My Family Health Portrait pedigrees for four common conditions--diabetes and colon, breast, and ovarian cancer. The tool performed less well for coronary artery disease and stroke. We recommend that the tool be improved to better capture information for these two common conditions. More... »

PAGES

370-375

Identifiers

URI

http://scigraph.springernature.com/pub.10.1097/gim.0b013e3181e15bd5

DOI

http://dx.doi.org/10.1097/gim.0b013e3181e15bd5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005976314

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20479646


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1097/gim.0b013e3181e15bd5'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1097/gim.0b013e3181e15bd5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1097/gim.0b013e3181e15bd5'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1097/gim.0b013e3181e15bd5'


 

This table displays all metadata directly associated to this object as RDF triples.

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