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Genomewide Association Study of Fracture Nonunion Using Electronic Health Records.

ABSTRACT: Nonunion is a clinically significant complication of fracture associated with worse outcomes, including increased pain, disability, and higher healthcare costs. The risk for nonunion is likely to be complex and multifactorial, and as such, the biology underlying such risk remains poorly understood. Genetic studies represent one approach to identify implicated biology for further investigation, but to date the lack of large cohorts for study has limited such efforts. We utilized the electronic health records of two large academic medical centers in Boston to identify individuals with fracture nonunion and control individuals with fracture but no evidence of nonunion. We conducted a genomewide association study among 1760 individuals of Northern European ancestry with upper or lower extremity fracture, including 131 with nonunion, to examine whether common variants were associated with nonunion in this cohort. In all, one locus in the Calcyon (CALY) gene exceeded a genomewide threshold for statistical significance (p?=?1.95e-8), with eight additional loci associated with p?

SUBMITTER: McCoy TH

PROVIDER: S-EPMC6339539 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Genomewide Association Study of Fracture Nonunion Using Electronic Health Records.

McCoy Thomas H TH Fragomen Austin T AT Hart Kamber L KL Pellegrini Amelia M AM Raskin Kevin A KA Perlis Roy H RH

JBMR plus 20180620 1

Nonunion is a clinically significant complication of fracture associated with worse outcomes, including increased pain, disability, and higher healthcare costs. The risk for nonunion is likely to be complex and multifactorial, and as such, the biology underlying such risk remains poorly understood. Genetic studies represent one approach to identify implicated biology for further investigation, but to date the lack of large cohorts for study has limited such efforts. We utilized the electronic he ...[more]

PMID: 30680360

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Project description:BackgroundHip fracture is associated with immobility, morbidity, mortality, and high medical cost. Due to limited availability of dual-energy X-ray absorptiometry (DXA), hip fracture prediction models without using bone mineral density (BMD) data are essential. We aimed to develop and validate 10-year sex-specific hip fracture prediction models using electronic health records (EHR) without BMD.MethodsIn this retrospective, population-based cohort study, anonymized medical records were retrieved from the Clinical Data Analysis and Reporting System for public healthcare service users in Hong Kong aged ≥60 years as of 31 December 2005. A total of 161,051 individuals (91,926 female; 69,125 male) with complete follow-up from 1 January 2006 till the study end date on 31 December 2015 were included in the derivation cohort. The sex-stratified derivation cohort was randomly divided into 80% training and 20% internal testing datasets. An independent validation cohort comprised 3046 community-dwelling participants aged ≥60 years as of 31 December 2005 from the Hong Kong Osteoporosis Study, a prospective cohort which recruited participants between 1995 and 2010. With 395 potential predictors (age, diagnosis, and drug prescription records from EHR), 10-year sex-specific hip fracture prediction models were developed using stepwise selection by logistic regression (LR) and four machine learning (ML) algorithms (gradient boosting machine, random forest, eXtreme gradient boosting, and single-layer neural networks) in the training cohort. Model performance was evaluated in both internal and independent validation cohorts.FindingsIn female, the LR model had the highest AUC (0.815; 95% Confidence Interval [CI]: 0.805-0.825) and adequate calibration in internal validation. Reclassification metrics showed the LR model had better discrimination and classification performance than the ML algorithms. Similar performance was attained by the LR model in independent validation, with high AUC (0.841; 95% CI: 0.807-0.87) comparable to other ML algorithms. In internal validation for male, LR model had high AUC (0.818; 95% CI: 0.801-0.834) and it outperformed all ML models as indicated by reclassification metrics, with adequate calibration. In independent validation, the LR model had high AUC (0.898; 95% CI: 0.857-0.939) comparable to ML algorithms. Reclassification metrics demonstrated that LR model had the best discrimination performance.InterpretationEven without using BMD data, the 10-year hip fracture prediction models developed by conventional LR had better discrimination performance than the models developed by ML algorithms. Upon further validation in independent cohorts, the LR models could be integrated into the routine clinical workflow, aiding the identification of people at high risk for DXA scan.FundingHealth and Medical Research Fund, Health Bureau, Hong Kong SAR Government (reference: 17181381).

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Genomewide Association Study of Fracture Nonunion Using Electronic Health Records.

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Genomewide Association Study of Fracture Nonunion Using Electronic Health Records.

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