Evidence map›Paper›PMID 37535812›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2023

The value of parental medical records for the prediction of diabetes and cardiovascular disease: a novel method for generating and incorporating family histories.

Yuval Barak-Corren, David Tsurel, Daphna Keidar, Ilan Gofer, Dafna Shahaf, Maya Leventer-Roberts, Noam Barda, Ben Y Reis

Open access · greenAbstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.3field-weighted citation impact, top 17% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 3 institutions in 2 countries.

Yuval Barak-CorrenPredictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.
David TsurelPredictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.
Daphna KeidarPredictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.
Ilan GoferClalit Research Institute, Ramat Gan, Israel.
Dafna ShahafThe Hebrew University of Jerusalem, Jerusalem, Israel.
Maya Leventer-RobertsClalit Research Institute, Ramat Gan, Israel.
Noam BardaClalit Research Institute, Ramat Gan, Israel.
Ben Y ReisPredictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.ORCID 0000-0001-9908-5523
Boston Children's Hospital · USClalit Health Services · ILHebrew University of Jerusalem · IL

Funding

Data-Driven Family Histories: Precision Medicine Across GenerationsR01HG009129 · NHGRI · BOSTON CHILDREN'S HOSPITAL · PI REIS, BEN Y · 2016 to 2018
$1.1M
NHGRI NIH HHS R01 HG009129NIH HHS R01-HG009129
6 · The paper itself

Abstract

objectiveTo determine whether data-driven family histories (DDFH) derived from linked EHRs of patients and their parents can improve prediction of patients' 10-year risk of diabetes and atherosclerotic cardiovascular disease (ASCVD). MATERIALS AND

methodsA retrospective cohort study using data from Israel's largest healthcare organization. A random sample of 200 000 subjects aged 40-60 years on the index date (January 1, 2010) was included. Subjects with insufficient history (<1 year) or insufficient follow-up (<10 years) were excluded. Two separate XGBoost models were developed-1 for diabetes and 1 for ASCVD-to predict the 10-year risk for each outcome based on data available prior to the index date of January 1, 2010.

resultsOverall, the study included 110 734 subject-father-mother triplets. There were 22 153 cases of diabetes (20%) and 11 715 cases of ASCVD (10.6%). The addition of parental information significantly improved prediction of diabetes risk (P < .001), but not ASCVD risk. For both outcomes, maternal medical history was more predictive than paternal medical history. A binary variable summarizing parental disease state delivered similar predictive results to the full parental EHR. DISCUSSION: The increasing availability of EHRs for multiple family generations makes DDFH possible and can assist in delivering more personalized and precise medicine to patients. Consent frameworks must be established to enable sharing of information across generations, and the results suggest that sharing the full records may not be necessary.

conclusionDDFH can address limitations of patient self-reported family history, and it improves clinical predictions for some conditions, but not for all, and particularly among younger adults.

Indexed as

AtherosclerosisCardiovascular DiseasesDiabetes MellitusAdultHumansMedical RecordsParentsRetrospective StudiesRisk AssessmentRisk Factorscardiovascular diseaseclinical predictiondiabetesfamily historyhealth informatics

Identifiers

PMID37535812
PMCPMC10654871
OpenAlexW4385562413

What OpenQuestion holds

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Registered trials

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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.