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A Validated Phenotyping Algorithm for Genetic Association Studies in Age-related Macular Degeneration.

Simonett JM, Sohrab MA, Pacheco J, Armstrong LL, Rzhetskaya M, Smith M, Geoffrey Hayes M, Fawzi AA - Sci Rep (2015)

Bottom Line: With the rapid advancement of DNA sequencing technologies, many AMD-associated genetic polymorphisms have been identified.Currently, the most time consuming steps of these studies are patient recruitment and phenotyping.With the rapid growth of EMR-linked DNA biorepositories, patient selection algorithms can greatly increase the efficiency of genetic association study.

View Article: PubMed Central - PubMed

Affiliation: Department of Ophthalmology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611.

ABSTRACT
Age-related macular degeneration (AMD), a multifactorial, neurodegenerative disease, is a leading cause of vision loss. With the rapid advancement of DNA sequencing technologies, many AMD-associated genetic polymorphisms have been identified. Currently, the most time consuming steps of these studies are patient recruitment and phenotyping. In this study, we describe the development of an automated algorithm to identify neovascular (wet) AMD, non-neovascular (dry) AMD and control subjects using electronic medical record (EMR)-based criteria. Positive predictive value (91.7%) and negative predictive value (97.5%) were calculated using expert chart review as the gold standard to assess algorithm performance. We applied the algorithm to an EMR-linked DNA bio-repository to study previously identified AMD-associated single nucleotide polymorphisms (SNPs), using case/control status determined by the algorithm. Risk alleles of three SNPs, rs1061170 (CFH), rs1410996 (CFH), and rs10490924 (ARMS2) were found to be significantly associated with the AMD case/control status as defined by the algorithm. With the rapid growth of EMR-linked DNA biorepositories, patient selection algorithms can greatly increase the efficiency of genetic association study. We have found that stepwise validation of such an algorithm can result in reliable cohort selection and, when coupled within an EMR-linked DNA biorepository, replicates previously published AMD-associated SNPs.

No MeSH data available.


Related in: MedlinePlus

High-throughput clinical phenotyping algorithm outline.Final HTCP algorithm applied to EMR-linked DNA biorepository. Red criteria were added after first round of case selection/expert chart review. ICD-9: International Classification of Disease-9, CPT: current procedural terminology.
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f1: High-throughput clinical phenotyping algorithm outline.Final HTCP algorithm applied to EMR-linked DNA biorepository. Red criteria were added after first round of case selection/expert chart review. ICD-9: International Classification of Disease-9, CPT: current procedural terminology.

Mentions: We developed the algorithm to identify all AMD cases using the criterion of AMD ICD-9 codes entered by an ophthalmologist (362.50, 362.51, 362.52, 362.16, 362.57). To classify cases as “wet” AMD cases within this population, we additionally required a current procedural terminology (CPT) code (J2778: ranibizumab injection, J9035, J3490 or J3590: bevacizumab injection), or an order or prescription for ranibizumab , bevacizumab, or aflibercept. This initial algorithm was tested by unsupervised random selection of 20 suspected AMD patient charts (10 dry and 10 wet cases of AMD) from the Northwestern University Department of Ophthalmology. Based on this initlal pilot study, we revised the HTCP algorithm to require subjects to be ≥60 years of age at the time of the first AMD diagnosis and to have ≥2 visits that were associated with the AMD ICD-9 codes. Furthermore, an ICD-9 code starting with 362.5 on the same date as the procedural CPT code or medication order was required for “wet” AMD classification. All AMD cases not meeting the wet AMD criteria were labeled as “dry” AMD (Fig. 1). Patients were classified as controls if they had ≥1 ophthalmology visit within the last two years, were ≥60 years of age at the time of the visit, and did not receive an AMD or AMD-associated diagnosis (we excluded the following non-specific or unrelated ICD-9 codes 362 or 377.21).


A Validated Phenotyping Algorithm for Genetic Association Studies in Age-related Macular Degeneration.

Simonett JM, Sohrab MA, Pacheco J, Armstrong LL, Rzhetskaya M, Smith M, Geoffrey Hayes M, Fawzi AA - Sci Rep (2015)

High-throughput clinical phenotyping algorithm outline.Final HTCP algorithm applied to EMR-linked DNA biorepository. Red criteria were added after first round of case selection/expert chart review. ICD-9: International Classification of Disease-9, CPT: current procedural terminology.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4530462&req=5

f1: High-throughput clinical phenotyping algorithm outline.Final HTCP algorithm applied to EMR-linked DNA biorepository. Red criteria were added after first round of case selection/expert chart review. ICD-9: International Classification of Disease-9, CPT: current procedural terminology.
Mentions: We developed the algorithm to identify all AMD cases using the criterion of AMD ICD-9 codes entered by an ophthalmologist (362.50, 362.51, 362.52, 362.16, 362.57). To classify cases as “wet” AMD cases within this population, we additionally required a current procedural terminology (CPT) code (J2778: ranibizumab injection, J9035, J3490 or J3590: bevacizumab injection), or an order or prescription for ranibizumab , bevacizumab, or aflibercept. This initial algorithm was tested by unsupervised random selection of 20 suspected AMD patient charts (10 dry and 10 wet cases of AMD) from the Northwestern University Department of Ophthalmology. Based on this initlal pilot study, we revised the HTCP algorithm to require subjects to be ≥60 years of age at the time of the first AMD diagnosis and to have ≥2 visits that were associated with the AMD ICD-9 codes. Furthermore, an ICD-9 code starting with 362.5 on the same date as the procedural CPT code or medication order was required for “wet” AMD classification. All AMD cases not meeting the wet AMD criteria were labeled as “dry” AMD (Fig. 1). Patients were classified as controls if they had ≥1 ophthalmology visit within the last two years, were ≥60 years of age at the time of the visit, and did not receive an AMD or AMD-associated diagnosis (we excluded the following non-specific or unrelated ICD-9 codes 362 or 377.21).

Bottom Line: With the rapid advancement of DNA sequencing technologies, many AMD-associated genetic polymorphisms have been identified.Currently, the most time consuming steps of these studies are patient recruitment and phenotyping.With the rapid growth of EMR-linked DNA biorepositories, patient selection algorithms can greatly increase the efficiency of genetic association study.

View Article: PubMed Central - PubMed

Affiliation: Department of Ophthalmology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611.

ABSTRACT
Age-related macular degeneration (AMD), a multifactorial, neurodegenerative disease, is a leading cause of vision loss. With the rapid advancement of DNA sequencing technologies, many AMD-associated genetic polymorphisms have been identified. Currently, the most time consuming steps of these studies are patient recruitment and phenotyping. In this study, we describe the development of an automated algorithm to identify neovascular (wet) AMD, non-neovascular (dry) AMD and control subjects using electronic medical record (EMR)-based criteria. Positive predictive value (91.7%) and negative predictive value (97.5%) were calculated using expert chart review as the gold standard to assess algorithm performance. We applied the algorithm to an EMR-linked DNA bio-repository to study previously identified AMD-associated single nucleotide polymorphisms (SNPs), using case/control status determined by the algorithm. Risk alleles of three SNPs, rs1061170 (CFH), rs1410996 (CFH), and rs10490924 (ARMS2) were found to be significantly associated with the AMD case/control status as defined by the algorithm. With the rapid growth of EMR-linked DNA biorepositories, patient selection algorithms can greatly increase the efficiency of genetic association study. We have found that stepwise validation of such an algorithm can result in reliable cohort selection and, when coupled within an EMR-linked DNA biorepository, replicates previously published AMD-associated SNPs.

No MeSH data available.


Related in: MedlinePlus