{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Streed CG"],"funding":["American Heart Association","Doris Duke Charitable Foundation","American Heart Association-American Stroke Association","National Heart, Lung, and Blood Institute","NHLBI NIH HHS","Boston University Chobanian and Avedisian School of Medicine Department of Medicine Career Investment"],"pagination":["1047-1055"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10198536"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["30(6)"],"pubmed_abstract":["<h4>Objective</h4>To adapt and validate an algorithm to ascertain transgender and gender diverse (TGD) patients within electronic health record (EHR) data.<h4>Methods</h4>Using a previously unvalidated algorithm of identifying TGD persons within administrative claims data in a multistep, hierarchical process, we validated this algorithm in an EHR data set with self-reported gender identity.<h4>Results</h4>Within an EHR data set of 52 746 adults with self-reported gender identity (gold standard) a previously unvalidated algorithm to identify TGD persons via TGD-related diagnosis and procedure codes, and gender-affirming hormone therapy prescription data had a sensitivity of 87.3% (95% confidence interval [CI] 86.4-88.2), specificity of 98.7% (95% CI 98.6-98.8), positive predictive value (PPV) of 88.7% (95% CI 87.9-89.4), and negative predictive value (NPV) of 98.5% (95% CI 98.4-98.6). The area under the curve (AUC) was 0.930 (95% CI 0.925-0.935). Steps to further categorize patients as presumably TGD men versus women based on prescription data performed well: sensitivity of 97.6%, specificity of 92.7%, PPV of 93.2%, and NPV of 97.4%. The AUC was 0.95 (95% CI 0.94-0.96).<h4>Conclusions</h4>In the absence of self-reported gender identity data, an algorithm to identify TGD patients in administrative data using TGD-related diagnosis and procedure codes, and gender-affirming hormone prescriptions performs well."],"journal":["Journal of the American Medical Informatics Association : JAMIA"],"pubmed_title":["Validation of an administrative algorithm for transgender and gender diverse persons against self-report data in electronic health records."],"pmcid":["PMC10198536"],"funding_grant_id":["K01 HL151902","R01HL141434","R01HL092577","2022061","NHLBI 1K01HL151902-01A1","R01 HL092577","AHA 20CDA35320148","R01 HL141434","AHA_18SFRN34110082"],"pubmed_authors":["Paasche-Orlow MK","Streed CG","Jasuja GK","King D","Tangpricha V","Grasso C","Poteat T","Mukherjee M","Shapira-Daniels A","Cabral H","Reisner SL","Mayer KH","Benjamin EJ"],"additional_accession":[]},"is_claimable":false,"name":"Validation of an administrative algorithm for transgender and gender diverse persons against self-report data in electronic health records.","description":"<h4>Objective</h4>To adapt and validate an algorithm to ascertain transgender and gender diverse (TGD) patients within electronic health record (EHR) data.<h4>Methods</h4>Using a previously unvalidated algorithm of identifying TGD persons within administrative claims data in a multistep, hierarchical process, we validated this algorithm in an EHR data set with self-reported gender identity.<h4>Results</h4>Within an EHR data set of 52 746 adults with self-reported gender identity (gold standard) a previously unvalidated algorithm to identify TGD persons via TGD-related diagnosis and procedure codes, and gender-affirming hormone therapy prescription data had a sensitivity of 87.3% (95% confidence interval [CI] 86.4-88.2), specificity of 98.7% (95% CI 98.6-98.8), positive predictive value (PPV) of 88.7% (95% CI 87.9-89.4), and negative predictive value (NPV) of 98.5% (95% CI 98.4-98.6). The area under the curve (AUC) was 0.930 (95% CI 0.925-0.935). Steps to further categorize patients as presumably TGD men versus women based on prescription data performed well: sensitivity of 97.6%, specificity of 92.7%, PPV of 93.2%, and NPV of 97.4%. The AUC was 0.95 (95% CI 0.94-0.96).<h4>Conclusions</h4>In the absence of self-reported gender identity data, an algorithm to identify TGD patients in administrative data using TGD-related diagnosis and procedure codes, and gender-affirming hormone prescriptions performs well.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2026-06-03T21:31:46.344Z","creation":"2025-07-12T03:04:16.081Z"},"accession":"S-EPMC10198536","cross_references":{"pubmed":["36921287"],"doi":["10.1093/jamia/ocad039"]}}