Developing Electronic Health Record Algorithms that Accurately Identify Patients with Systemic Lupus Erythematosus. Barnado, A., Casey, C., Carroll, R. J., Wheless, L., Denny, J. C., & Crofford, L. J. Arthritis care & research, 69(5):687–693, May, 2017. ![Developing Electronic Health Record Algorithms that Accurately Identify Patients with Systemic Lupus Erythematosus [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Objective To study systemic lupus erythematosus (SLE) in the electronic health record (EHR), we must accurately identify patients with SLE. Our objective was to develop and validate novel EHR algorithms that use International Classification of Diseases, Ninth Revision Clinical Modification (ICD-9) codes, laboratory testing, and medications to identify SLE patients. Methods We used Vanderbilt's Synthetic Derivative (SD), a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least one SLE ICD-9 code (710.0) yielding 5959 individuals. To create a training set, 200 were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist. Positive predictive values (PPVs) and sensitivity were calculated for combinations of code counts of the SLE ICD-9 code, a positive anti-nuclear antibody (ANA), ever use of medications, and a keyword of “lupus” in the problem list. The algorithms with the highest PPV were each internally validated using a random set of 100 individuals from the remaining 5759 subjects. Results The algorithm with the highest PPV at 95% in the training set and 91% in the validation set was 3 or more counts of the SLE ICD-9 code, ANA positive (≥ 1:40), and ever use of both disease-modifying antirheumatic drugs (DMARDs) and steroids while excluding individuals with systemic sclerosis and dermatomyositis ICD-9 codes. Conclusion We developed and validated the first EHR algorithm that incorporates lab values and medications with the SLE ICD-9 code to identify patients with SLE accurately.
@article{barnado_developing_2017,
title = {Developing {Electronic} {Health} {Record} {Algorithms} that {Accurately} {Identify} {Patients} with {Systemic} {Lupus} {Erythematosus}},
volume = {69},
issn = {2151-464X},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5219863/},
doi = {10.1002/acr.22989},
abstract = {Objective
To study systemic lupus erythematosus (SLE) in the electronic health record (EHR), we must accurately identify patients with SLE. Our objective was to develop and validate novel EHR algorithms that use International Classification of Diseases, Ninth Revision Clinical Modification (ICD-9) codes, laboratory testing, and medications to identify SLE patients.
Methods
We used Vanderbilt's Synthetic Derivative (SD), a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least one SLE ICD-9 code (710.0) yielding 5959 individuals. To create a training set, 200 were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist. Positive predictive values (PPVs) and sensitivity were calculated for combinations of code counts of the SLE ICD-9 code, a positive anti-nuclear antibody (ANA), ever use of medications, and a keyword of “lupus” in the problem list. The algorithms with the highest PPV were each internally validated using a random set of 100 individuals from the remaining 5759 subjects.
Results
The algorithm with the highest PPV at 95\% in the training set and 91\% in the validation set was 3 or more counts of the SLE ICD-9 code, ANA positive (≥ 1:40), and ever use of both disease-modifying antirheumatic drugs (DMARDs) and steroids while excluding individuals with systemic sclerosis and dermatomyositis ICD-9 codes.
Conclusion
We developed and validated the first EHR algorithm that incorporates lab values and medications with the SLE ICD-9 code to identify patients with SLE accurately.},
number = {5},
urldate = {2022-12-12},
journal = {Arthritis care \& research},
author = {Barnado, April and Casey, Carolyn and Carroll, Robert J. and Wheless, Lee and Denny, Joshua C. and Crofford, Leslie J.},
month = may,
year = {2017},
pmid = {27390187},
pmcid = {PMC5219863},
pages = {687--693},
}
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Methods We used Vanderbilt's Synthetic Derivative (SD), a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least one SLE ICD-9 code (710.0) yielding 5959 individuals. To create a training set, 200 were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist. Positive predictive values (PPVs) and sensitivity were calculated for combinations of code counts of the SLE ICD-9 code, a positive anti-nuclear antibody (ANA), ever use of medications, and a keyword of “lupus” in the problem list. The algorithms with the highest PPV were each internally validated using a random set of 100 individuals from the remaining 5759 subjects. Results The algorithm with the highest PPV at 95% in the training set and 91% in the validation set was 3 or more counts of the SLE ICD-9 code, ANA positive (≥ 1:40), and ever use of both disease-modifying antirheumatic drugs (DMARDs) and steroids while excluding individuals with systemic sclerosis and dermatomyositis ICD-9 codes. Conclusion We developed and validated the first EHR algorithm that incorporates lab values and medications with the SLE ICD-9 code to identify patients with SLE accurately.","number":"5","urldate":"2022-12-12","journal":"Arthritis care & research","author":[{"propositions":[],"lastnames":["Barnado"],"firstnames":["April"],"suffixes":[]},{"propositions":[],"lastnames":["Casey"],"firstnames":["Carolyn"],"suffixes":[]},{"propositions":[],"lastnames":["Carroll"],"firstnames":["Robert","J."],"suffixes":[]},{"propositions":[],"lastnames":["Wheless"],"firstnames":["Lee"],"suffixes":[]},{"propositions":[],"lastnames":["Denny"],"firstnames":["Joshua","C."],"suffixes":[]},{"propositions":[],"lastnames":["Crofford"],"firstnames":["Leslie","J."],"suffixes":[]}],"month":"May","year":"2017","pmid":"27390187","pmcid":"PMC5219863","pages":"687–693","bibtex":"@article{barnado_developing_2017,\n\ttitle = {Developing {Electronic} {Health} {Record} {Algorithms} that {Accurately} {Identify} {Patients} with {Systemic} {Lupus} {Erythematosus}},\n\tvolume = {69},\n\tissn = {2151-464X},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5219863/},\n\tdoi = {10.1002/acr.22989},\n\tabstract = {Objective\nTo study systemic lupus erythematosus (SLE) in the electronic health record (EHR), we must accurately identify patients with SLE. Our objective was to develop and validate novel EHR algorithms that use International Classification of Diseases, Ninth Revision Clinical Modification (ICD-9) codes, laboratory testing, and medications to identify SLE patients.\n\nMethods\nWe used Vanderbilt's Synthetic Derivative (SD), a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least one SLE ICD-9 code (710.0) yielding 5959 individuals. To create a training set, 200 were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist. Positive predictive values (PPVs) and sensitivity were calculated for combinations of code counts of the SLE ICD-9 code, a positive anti-nuclear antibody (ANA), ever use of medications, and a keyword of “lupus” in the problem list. The algorithms with the highest PPV were each internally validated using a random set of 100 individuals from the remaining 5759 subjects.\n\nResults\nThe algorithm with the highest PPV at 95\\% in the training set and 91\\% in the validation set was 3 or more counts of the SLE ICD-9 code, ANA positive (≥ 1:40), and ever use of both disease-modifying antirheumatic drugs (DMARDs) and steroids while excluding individuals with systemic sclerosis and dermatomyositis ICD-9 codes.\n\nConclusion\nWe developed and validated the first EHR algorithm that incorporates lab values and medications with the SLE ICD-9 code to identify patients with SLE accurately.},\n\tnumber = {5},\n\turldate = {2022-12-12},\n\tjournal = {Arthritis care \\& research},\n\tauthor = {Barnado, April and Casey, Carolyn and Carroll, Robert J. and Wheless, Lee and Denny, Joshua C. and Crofford, Leslie J.},\n\tmonth = may,\n\tyear = {2017},\n\tpmid = {27390187},\n\tpmcid = {PMC5219863},\n\tpages = {687--693},\n}\n\n","author_short":["Barnado, A.","Casey, C.","Carroll, R. 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