{"doi":"10.2337/dc24-1624","title":"Derivation and Validation of D-RISK: An Electronic Health Record–Driven Risk Score to Detect Undiagnosed Dysglycemia in Clinical Practice","abstract":"OBJECTIVE: We derive and validate D-RISK, an electronic health record (EHR)-driven risk score to optimize and facilitate screening for undiagnosed dysglycemia (prediabetes plus diabetes) in clinical practice. RESEARCH DESIGN AND METHODS: We used retrospective EHR data (derivation sample) and a prospective diabetes screening study (validation sample) to develop D-RISK. Logistic regression with backward selection was used to predict dysglycemia (HbA1c ≥5.7%) using diabetes risk factors consistently captured in structured EHR data. Model coefficients were converted to a points-based risk score. We report discrimination, sensitivity, and specificity and compare D-RISK to the American Diabetes Association (ADA) risk test and the ADA and United States Preventive Services Task Force (USPSTF) screening guidelines. RESULTS: The derivation cohort included 11,387 patients (mean age 48 years; 65% female; 42% Hispanic; 32% non-Hispanic Black; mean BMI 32; 29% with hypertension). D-RISK included age, race, BMI, hypertension, and random glucose. The area under curve (AUC) for the risk score was 0.75 (95% CI 0.74-0.76). In the validation screening study (n = 519), the AUC was 0.71 (95% CI 0.66-0.75) which was better than the ADA and USPSTF diabetes screening guidelines (AUC = 0.52 and AUC = 0.58, respectively; P < 0.001 for both). Discrimination was similar to the ADA risk test (AUC = 0.67) using patient-reported data to supplement EHR data, although D-RISK was more sensitive (75% vs. 61%) at the recommended screening thresholds. CONCLUSIONS: Designed for use in EHR, D-RISK performs better than commonly used screening guidelines and risk scores and may help detect undiagnosed cases of dysglycemia in clinical practice.","journal":"Diabetes Care","year":2025,"id":540112,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6749,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":231195,"name":"Ildiko Lingvay","orcid":"0000-0001-7006-7401","position":1,"is_corresponding":false},{"id":1427547,"name":"Luigi Meneghini","orcid":"0000-0003-4539-2725","position":2,"is_corresponding":false},{"id":1218983,"name":"Brett Moran","orcid":null,"position":3,"is_corresponding":false},{"id":515354,"name":"Noel Santini","orcid":"0000-0002-6517-834X","position":4,"is_corresponding":false},{"id":1069763,"name":"Song Zhang","orcid":"0000-0001-7997-274X","position":5,"is_corresponding":false},{"id":294599,"name":"Ethan A. Halm","orcid":"0000-0003-3042-2741","position":6,"is_corresponding":false},{"id":527486,"name":"Michael E. Bowen","orcid":"0000-0003-4089-1584","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:52:34.520788Z","pmid":"39823295","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}