Search

ObjectiveOur objective was to develop algorithms to identify lupus clinical classification criteria attributes using structured data found in the electronic health record (EHR) and determine whether they could be used to describe a cohort of people with lupus and discriminate them from a defined healthy control cohort.MethodsWe created gold standard lupus and healthy patient cohorts that were fully adjudicated for the American College of Rheumatology (ACR), Systemic Lupus International Collaborating Clinics (SLICC) and European League Against Rheumatism/ACR (EULAR/ACR) classification criteria and had matched EHR data. We implemented rule-based algorithms using structured data within the EHR system for each attribute of the three classification criteria. Individual criteria attribute and classification criteria algorithms as a whole were assessed over our combined cohorts and the overall performance of the algorithms was measured through sensitivity and specificity.ResultsIndividual classification criteria attributes had a wide range of sensitivities, 7% (oral ulcers) to 97% (haematological disorders) and specificities, 56% (haematological disorders) to 98% (photosensitivity), but all could be identified in EHR data. In general, algorithms based on laboratory results performed better than those primarily based on diagnosis codes. All three classification criteria systems effectively distinguished members of our case and control cohorts, but the SLICC criteria-based algorithm had the highest overall performance (76% sensitivity, 99% specificity).ConclusionsIt is possible to characterise disease manifestations in people with lupus using classification criteria-based algorithms that assess structured EHR data. These algorithms may reduce chart review burden and are a foundation for identifying subpopulations of patients with lupus based on disease presentation to support precision medicine applications.

We developed a computable phenotype for systemic lupus erythematosus (SLE) based on the Systemic Lupus International Collaborative Clinics clinical classification criteria set for SLE. We evaluated the phenotype over registry and EHR data for the same patient population to determine concordance of criteria detected in both datasets and to assess which types of structured data detected individual classification criteria. We identified a concordance of 68% between registry and EHR data relying solely on structured data.

Background Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease with diverse manifestations that can occur over a long period of time. Electronic health record (EHR) data presents a rich source of information that can be used to understand the varied presentation of SLE. We examined three clinical classification criteria for SLE to assess whether they could be a foundation for phenotype-based detection of patients with SLE in EHR data. Methods We assessed algorithm performance over 600 medical records from the Northwestern Medicine Electronic Data Warehouse, 472 of which had definite SLE and 128 which did not, based on chart review. We developed algorithms, based on the American College of Rheumatology (ACR), Systemic Lupus International Collaborating Clinics (SLICC) and the proposed European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria using only structured data elements (diagnosis codes (ICD9/ICD10) and lab results) to determine whether patients met the classification criteria for definite SLE. Results As shown in table 1, the overall identification rate of SLE ranged from 58% to 78% across the three algorithms. All three criteria-based algorithms had greater than 95% specificity and greater than 98% positive predictive value (PPV). Sensitivity of the algorithms ranged from 52% to 69% and negative predictive value (NPV) from 35% to 55%. The SLICC-based algorithm had the overall highest performance, detecting 78% of the patients with definite SLE as determined by chart review, with 99% PPV, 69% sensitivity, 98% specificity and 55% NPV. Conclusions The ACR-, SLICC- and proposed EULAR/ACR- based EHR algorithms all detect a significant proportion of patients that were classified as having definite SLE by chart review, with high PPV and specificity. Low NPV of all three algorithms likely reflects undetected cases of SLE resulting from low detection of clinical and laboratory criteria (such as arthralgia and ANA tests) that are not consistently documented in structured data in the medical record. Use of structured data improves portability of the algorithms to other EHR datasets, but may have reduced the ability of the algorithms to detect important/highly weighted classification criteria that are documented primarily in free text notes. All three algorithms may improve through use of natural language processing (NLP) of physician notes for criteria that were difficult to detect using only diagnosis codes and labs, but may reduce portability as a result of the customization required for NLP to be effective. Funding Source(s): National Institute of Arthritis and Musculoskeletal and Skin Diseases (R21 AR072263) and NHGRI U01HG006388

Background SLE is difficult to diagnose given diverse manifestations that occur over time and across care sites. Electronic health records (EHR) are now used in a majority of health care settings throughout the country, and present a rich source of information about patients which can be mined for earlier diagnosis identification, to improve quality of care, or enable clinical studies. To identify SLE patients in EHR data, we developed a rules-based algorithm based on the SLICC classification criteria and compared against a gold standard SLE patient registry data set. Methods We identified 513 patients in the Chicago Lupus Database (CLD) fulfilling 4 or more of the ACR classification criteria for SLE who also had records in the Northwestern Medicine Electronic Data Warehouse (NMEDW). ICD-9/10 codes were used to identify clinical SLICC SLE classification criteria items. Laboratory results were identified using lab test names in combination with threshold numeric values in order to determine whether patients met the SLICC lab test classification criteria requirements. Results As shown in table 1, of 513 patients with SLE in the CLD, we detected the following SLICC classification criteria, in the NMEDW: clinical- chronic cutaneous 97%; acute cutaneous 98%; renal 65%; serositis 52%; arthritis 34%; neuro 29%; ulcers 16%; alopecia 1%; and labs- dsDNA 89%; hemolytic anemia 80%; complement 74%; leukopenia/lymphopenia 73%; APL 64%; ANA 52%; thrombocytopenia 22%; Coombs 17%; Sm 0%. Of 513 patients with SLE in the CLD based on ACR criteria, 513 had at least 1 clinical criteria, 466 had at least 1 immunologic criteria, and 471 had 4 or more criteria. Using EHR data from the NMEDW, and rules for the SLICC classification criteria that were based on ICD9/10 codes and labs and required identification of at least one clinical and one immunological criteria, we categorized 450/513 (88%) patients as having definite lupus. Conclusions Query of patient EHR data with ICD-9/10 codes and lab tests for specific SLICC classification criteria items requires refinement to improve identification of some criteria. Using the SLICC classification rule for definite SLE, we were able to identify 88% of those with definite SLE by the ACR criteria, using ICD9/10 codes and labs. Text searching of notes (by simple string matching or natural language processing) may improve identification of individual SLICC criteria (e.g. renal biopsy) and may be critical for mining physicians’ notes for criteria that are not well documented with diagnosis codes or lab results. Acknowledgements Funding-NIH/NIAMS R21AR074081.