Utilization of electronic health record data to evaluate the association of urban environment with systemic lupus erythematosus symptoms.
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ABSTRACT:
SUBMITTER: Song J
PROVIDER: S-EPMC10234196 | biostudies-literature | 2023 Jun
REPOSITORIES: biostudies-literature
Publications
Utilization of electronic health record data to evaluate the association of urban environment with systemic lupus erythematosus symptoms.
Song Janet J Forrest Noah N Gordon Adam A Kottyan Leah L Mittendorf Kathleen F KF Wei Wei-Qi WQ Ramsey-Goldman Rosalind R Walunas Theresa T Kho Abel A
Rheumatology (Oxford, England) 20230601 6
Similar Datasets
Project description:ObjectiveTo 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 (ICD-9), Clinical Modification codes, laboratory testing, and medications to identify SLE patients.MethodsWe used Vanderbilt's Synthetic Derivative, a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least 1 SLE ICD-9 code (710.0), yielding 5,959 individuals. To create a training set, 200 subjects 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 antinuclear 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 5,759 subjects.ResultsThe 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 and steroids, while excluding individuals with systemic sclerosis and dermatomyositis ICD-9 codes.ConclusionWe developed and validated the first EHR algorithm that incorporates laboratory values and medications with the SLE ICD-9 code to identify patients with SLE accurately.
| S-EPMC5219863 | biostudies-literature
Project description:ObjectiveElectronic health records (EHRs) represent powerful tools to study rare diseases. Our objective was to develop and validate EHR algorithms to identify systemic lupus erythematosus (SLE) births across centers.MethodsWe developed algorithms in a training set using an EHR with over 3 million subjects and validated the algorithms at 2 other centers. Subjects at all 3 centers were selected using ≥1 code for SLE International Classification of Diseases, Ninth Revision (ICD-9) or SLE International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Clinical Modification (ICD-10-CM) and ≥1 ICD-9 or ICD-10-CM delivery code. A subject was a case if diagnosed with SLE by a rheumatologist and had a birth documented. We tested algorithms using SLE ICD-9 or ICD-10-CM codes, antimalarial use, a positive antinuclear antibody ≥1:160, and ever checked double-stranded DNA or complement, using both rule-based and machine learning methods. Positive predictive values (PPVs) and sensitivities were calculated. We assessed the impact of case definition, coding provider, and subject race on algorithm performance.ResultsAlgorithms performed similarly across all 3 centers. Increasing the number of SLE codes, adding clinical data, and having a rheumatologist use the SLE code all increased the likelihood of identifying true SLE patients. All the algorithms had higher PPVs in African American versus White SLE births. Using machine learning methods, the total number of SLE codes and an SLE code from a rheumatologist were the most important variables in the model for SLE case status.ConclusionWe developed and validated algorithms that use multiple types of data to identify SLE births in the EHR. Algorithms performed better in African American mothers than in White mothers.
| S-EPMC8164642 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2014-06-03 | E-GEOD-46923 | biostudies-arrayexpress
Project description:ObjectiveTo determine whether electronic health record (EHR) data components could be identified and used to assess bone health quality indicators in patients with systemic lupus erythematosus as a foundation for population health management.MethodsWe identified patients in our EHR system who had diagnosis codes for lupus from 2012 to 2017 and characterized them based on the frequency and dosage of prescribed glucocorticoid medications. The medical records of patients who received repeated high-dose glucocorticoid orders were further reviewed for osteoporosis, osteoporotic fractures, receipt of appropriate preventive screening, and orders for protective medications based on established quality indicators. Descriptive statistics were calculated to summarize results.ResultsWe identified 617 patients with a lupus diagnosis; 414 received glucocorticoid prescriptions, 189 received chronic, high-dose; and 83 received chronic, low-dose prescription orders. Of those with chronic high-dose glucocorticoid prescriptions, 14% had an osteoporosis diagnosis, 3% had an osteoporotic fracture, 51% received a prescription for calcium/vitamin D, 43% had bone mineral density screening orders, 20% received a spine radiograph order, 29% had a documented T-score, 12% received a prescription for osteoporosis medication, and 6% had a documented osteoporosis screening. We were able to identify data elements in the EHR for all nine components of the osteoporosis management quality indicator.ConclusionsIt is possible to identify data in the EHR for all attributes of the quality indicator for osteoporosis in lupus patients who receive chronic high-dose glucocorticoids. However, missing data and need to extract data from text-based notes may make development of population management tools challenging.
| S-EPMC9588551 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2014-06-03 | GSE46923 | GEO
Project description:ObjectivesThe objectives of this study were to determine the trajectories of depressive symptoms in patients with SLE and to identify baseline characteristics that are associated with a patient's trajectory of depression.MethodsData from the Lupus Outcomes Study at the University of California, San Francisco were analysed. Depressive symptomatology was assessed in years two through seven using the Center for Epidemiologic Studies Depression Scale (CES-D), with higher scores representing more severe depressive symptoms. Group-based trajectory modelling was used to determine latent classes of CES-D scores over time. Ordinal logistic regression analyses were performed to identify baseline characteristics associated with worse classes of depressive symptoms.ResultsCES-D scores for 763 individuals with SLE over 6 years were mapped into four distinct classes. Class 1 (36%) and class 2 (32%) comprised the largest proportion of the cohort and were defined by the lowest and low CES-D scores (no depression), respectively. Class 3 (22%) and class 4 (10%) had high and the highest scores (depression), respectively. Greater age [odds ratio (OR): 0.97, 95% CI: 0.96, 0.99] and higher education level (OR: 0.79, 95% CI: 0.70, 0.89) at baseline were associated with lower odds of membership in worse classes of depressive symptoms. Conversely, lower income (OR: 1.73, 95% CI: 1.03, 2.92), worse SF-36 physical functioning scores (OR: 1.12, 95% CI: 1.12, 1.13) and worse SF-36 bodily pain scores (OR: 1.58, 95% CI: 1.55, 1.61) were positively associated with membership in worse classes of depressive symptoms.ConclusionFour classes of depressive symptoms were identified in patients with SLE. Understanding the trajectories of depressive symptoms and the associated risk factors can aid in the management of these symptoms in individuals living with SLE.
| S-EPMC9891412 | biostudies-literature
Project description:Objective: Late-onset systemic lupus erythematosus (LO-SLE) is defined as SLE diagnosed at age 50 years or later. Current studies on LO-SLE are small and have conflicting results.Methods: Using a large, electronic health record (EHR)-based cohort of SLE individuals, we compared demographics, disease characteristics, SLE-specific antibodies, and medication prescribing practices in LO (n = 123) vs. NLO-SLE (n = 402) individuals.Results: The median age (interquartile range) at SLE diagnosis was 60 (56-67) years for LO-SLE and 28 (20-38) years for NLO-SLE. Both groups were predominantly female (85% vs. 91%, p = 0.10). LO-SLE individuals were more likely to be White than NLO-SLE individuals (74% vs. 60%, p = 0.005) and less likely to have positive dsDNA (39% vs. 58%, p = 0.001) and RNP (17% vs. 32%, p = 0.02) with no differences in Smith, SSA, and SSB. Autoantibody positivity declined with increasing age at SLE diagnosis. LO-SLE individuals were less likely to develop SLE nephritis (9% vs. 29%, p < 0.001) and less likely to be prescribed multiple classes of SLE medications including antimalarials (90% vs. 95%, p = 0.04), azathioprine (17% vs. 31%, p = 0.002), mycophenolate mofetil (12% vs. 38%, p < 0.001), and belimumab (2% vs. 8%, p = 0.02).Conclusion: LO-SLE individuals may be less likely to fit an expected course for SLE with less frequent positive autoantibodies at diagnosis and lower rates of nephritis, even after adjusting for race. Understanding how age impacts SLE disease presentation could help reduce diagnostic delays in SLE.
| S-EPMC10954386 | biostudies-literature
Project description: Not available
| S-EPMC1440614 | biostudies-literature
Project description: Not available
| S-EPMC2048842 | biostudies-literature
