Dataset Information

Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

ABSTRACT: Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

SUBMITTER: Polubriaginof FCG

PROVIDER: S-EPMC6015747 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Polubriaginof Fernanda C G FCG Vanguri Rami R Quinnies Kayla K Belbin Gillian M GM Yahi Alexandre A Salmasian Hojjat H Lorberbaum Tal T Nwankwo Victor V Li Li L Shervey Mark M MM Glowe Patricia P Ionita-Laza Iuliana I Simmerling Mary M Hripcsak George G Bakken Suzanne S Goldstein David D Kiryluk Krzysztof K Kenny Eimear E EE Dudley Joel J Vawdrey David K DK Tatonetti Nicholas P NP

Cell 20180517 7

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency co ...[more]

PMID: 29779949

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Project description:Clinically recorded pain scores are abundant in patient health records but are rarely used in research. The use of this information could help improve clinical outcomes. For example, a recent report by the Institute of Medicine stated that ineffective use of clinical information contributes to undertreatment of patient subpopulations--especially women. This study used diagnosis-associated pain scores from a large hospital database to document sex differences in reported pain. We used de-identified electronic medical records from Stanford Hospital and Clinics for more than 72,000 patients. Each record contained at least 1 disease-associated pain score. We found over 160,000 pain scores in more than 250 primary diagnoses, and analyzed differences in disease-specific pain reported by men and women. After filtering for diagnoses with minimum encounter numbers, we found diagnosis-specific sex differences in reported pain. The most significant differences occurred in patients with disorders of the musculoskeletal, circulatory, respiratory and digestive systems, followed by infectious diseases, and injury and poisoning. We also discovered sex-specific differences in pain intensity in previously unreported diseases, including disorders of the cervical region, and acute sinusitis (P = .01, .017, respectively). Pain scores were collected during hospital encounters. No information about the use of pre-encounter over-the-counter medications was available. To our knowledge, this is the largest data-driven study documenting sex differences of disease-associated pain. It highlights the utility of electronic medical record data to corroborate and expand on results of smaller clinical studies. Our findings emphasize the need for future research examining the mechanisms underlying differences in pain.This article highlights the potential of electronic medical records to conduct large-scale pain studies. Our results are consistent with previous studies reporting pain differences between sexes and also suggest that clinicians should pay increased attention to this idea.

Project description:BACKGROUND:Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. METHODS:Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. RESULTS:The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model. CONCLUSIONS:The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.

Dataset Information

Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

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Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

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