Dataset Information

Using a data-driven approach for the development and evaluation of phenotype algorithms for systemic lupus erythematosus.

ABSTRACT:

Background

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown origin. The objective of this research was to develop phenotype algorithms for SLE suitable for use in epidemiological studies using empirical evidence from observational databases.

Methods

We used a process for empirically determining and evaluating phenotype algorithms for health conditions to be analyzed in observational research. The process started with a literature search to discover prior algorithms used for SLE. We then used a set of Observational Health Data Sciences and Informatics (OHDSI) open-source tools to refine and validate the algorithms. These included tools to discover codes for SLE that may have been missed in prior studies and to determine possible low specificity and index date misclassification in algorithms for correction.

Results

We developed four algorithms using our process: two algorithms for prevalent SLE and two for incident SLE. The algorithms for both incident and prevalent cases are comprised of a more specific version and a more sensitive version. Each of the algorithms corrects for possible index date misclassification. After validation, we found the highest positive predictive value estimate for the prevalent, specific algorithm (89%). The highest sensitivity estimate was found for the sensitive, prevalent algorithm (77%).

Conclusion

We developed phenotype algorithms for SLE using a data-driven approach. The four final algorithms may be used directly in observational studies. The validation of these algorithms provides researchers an added measure of confidence that the algorithms are selecting subjects correctly and allows for the application of quantitative bias analysis.

SUBMITTER: Swerdel JN

PROVIDER: S-EPMC9934349 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Using a data-driven approach for the development and evaluation of phenotype algorithms for systemic lupus erythematosus.

Swerdel Joel N JN Ramcharran Darmendra D Hardin Jill J

PloS one 20230216 2

<h4>Background</h4>Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown origin. The objective of this research was to develop phenotype algorithms for SLE suitable for use in epidemiological studies using empirical evidence from observational databases.<h4>Methods</h4>We used a process for empirically determining and evaluating phenotype algorithms for health conditions to be analyzed in observational research. The process started with a literature search to discover pri ...[more]

PMID: 36795690

Similar Datasets

Project description:BackgroundThe demyelinating syndromes of the central nervous system (CNS) that occur in the context of systemic lupus erythematosus (SLE) may represent a manifestation of neuropsychiatric lupus (NPSLE) or an overlap of SLE and multiple sclerosis (MS). The differential diagnosis between the two entities has important clinical implications because the therapeutic management differs.ObjectivesTo characterize CNS demyelinating syndromes in a large SLE cohort as neuropsychiatric SLE (NPSLE) or SLE-MS overlap using a multidisciplinary approach and existing diagnostic (for MS) and classification criteria (for SLE).MethodsPatients from the "Attikon" lupus cohort (n = 707) were evaluated for demyelinating syndromes. Clinical, laboratory, and neuroimaging data were recorded for each patient. Following multidisciplinary evaluation and application of criteria, the demyelinating syndrome was attributed to either SLE or MS. Patients with transverse myelitis were not included in this study.ResultsWe identified 26 patients with demyelinating syndromes (3.7%). Of them, 12 were diagnosed as primary SLE-demyelination (46.2%) and 14 as overlap SLE-MS (53.8%). The two groups did not differ with respect to rheumatologic and neurologic manifestations or autoantibodies. SLE patients with demyelination manifested mild extra-CNS disease mainly involving joints and skin, while severe non-CNS manifestations were rare. However, these patients were less likely to have elevated IgG index (OR 0.055 95% CI: 0.008-0.40) and positive oligoclonal bands (OR 0.09 95% CI: 0.014-0.56), as well as brain lesions in the spinal cord, infratentorial, periventricular, and juxtacortical regions. A single brain region was affected in 9 patients with SLE-demyelination (75%), while all patients with MS-SLE had multiple affected brain regions. MS-SLE overlap was associated with an increased likelihood of neurologic relapses (OR 18.2, 95% CI: 1.76-188), while SLE-demyelination patients were less likely to exhibit neurological deficits (EDSS >0) at the last follow-up visit (50 vs. 78.6% in SLE-MS, respectively).ConclusionsDemyelination in the context of SLE follows a more benign course compared to a frank SLE-MS overlap. Extension of follow-up will ascertain whether patients with SLE-demyelination evolve to MS, or this is a bona fide NPSLE syndrome.

Dataset Information

Using a data-driven approach for the development and evaluation of phenotype algorithms for systemic lupus erythematosus.

Background

Methods

Results

Conclusion

Publications

Using a data-driven approach for the development and evaluation of phenotype algorithms for systemic lupus erythematosus.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets