Dataset Information

Development and validation of a simple lupus severity index using ACR criteria for classification of SLE.

ABSTRACT: To develop a simple systemic lupus erythematosus (SLE) severity index that requires knowledge of only American College of Rheumatology (ACR) criteria and subcriteria.This study used demographic, mortality and medical records data of 1915 patients with lupus from the Lupus Family Registry and Repository. The data were randomly split (2:1 ratio) into independent training and validation sets. A logistic regression with ridge penalty was used to model the probability of being prescribed major immunosuppressive drugs-a surrogate indicator of lupus severity. ACR criteria and subcriteria were used as predictor variables in this model, and the resulting regression coefficient estimates obtained from the training data were used as item weightings to construct the severity index.The resulting index was tested on the independent validation dataset and was found to have high predictive accuracy for immunosuppressive use and early mortality. The index was also found to be strongly correlated with a previously existing severity score for lupus. In addition, demographic factors known to influence lupus severity (eg, age of onset, gender and ethnicity) all showed robust associations with our severity index that were consistent with observed clinical trends.This new index can be easily computed using ACR criteria, which may be among the most readily available data elements from patient medical records. This tool may be useful in lupus research, especially large dataset analyses to stratify patients by disease severity, an important prognostic indicator in SLE.

SUBMITTER: Bello GA

PROVIDER: S-EPMC4800735 | biostudies-other | 2016

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:ImportanceClassification criteria are the standardized definitions that are used to enroll uniform cohorts for research studies. They emphasize high specificity and are distinct from diagnostic criteria. No universally recognized classification criteria currently exist for discoid lupus erythematosus (DLE), which has led to problematic heterogeneity in observational and interventional clinical studies across the field.ObjectiveTo create and validate classification criteria for DLE using 12 previously defined candidate criteria items.Design, setting, and participantsFor this diagnostic study, candidate criteria items were prospectively applied by dermatologists and dermatopathologists at clinical visits of patients with DLE or a condition that could be confused for DLE, termed a DLE mimicker, at academic dermatology practices across the United States, Poland, Japan, and South Korea. Data were collected from December 1, 2017, to February 1, 2019, and analyzed from March 1 to September 19, 2019.Main outcomes and measuresClinical features among these 2 groups were calculated and compared with χ2 or Fisher exact tests. Candidate models were identified using best subsets logistic regression analysis. Improvement tests, fit statistics, and discrimination were considered to choose a final model.ResultsNine sites contributed 215 patients, 15 of whom had missing or incomplete data. The final model for DLE classification criteria includes only clinical variables: atrophic scarring (3 points), location in the conchal bowl (2 points), preference for the head and neck (2 points), dyspigmentation (1 point), follicular hyperkeratosis and/or plugging (1 point), and erythematous to violaceous in color (1 point), with an area under the receiving operating characteristic curve of 0.91 (95% CI, 0.87-0.95). A score of at least 5 points yields a sensitivity of 84.1% and a specificity of 75.9% in the classification of DLE, with increasing scores yielding higher specificity.Conclusions and relevanceThese findings provide the initial validation of classification criteria for DLE for use in observational and clinical trials.

Project description:A significant portion of frontotemporal lobar degeneration (FTLD) is due to inherited gene mutations, and we are unaware of a large sequential series that includes a recently discovered inherited cause of FTLD. There is also great need to develop clinical tools and approaches that will assist clinicians in the identification and counseling of patients with FTLD and their families regarding the likelihood of an identifiable genetic cause.To ascertain the frequency of inherited FTLD and develop validated pedigree classification criteria for FTLD that provide a standardized means to evaluate pedigree information and insight into the likelihood of mutation-positive genetic test results for C9orf72, MAPT, and GRN.Information about pedigrees and DNA was collected from 306 serially assessed patients with a clinical diagnosis of FTLD. This information included gene test results for C9orf72, MAPT, and GRN. Pedigree classification criteria were developed based on a literature review of FTLD genetics and pedigree tools and then refined by reviewing mutation-positive and -negative pedigrees to determine differentiating characteristics.Academic medical center.Patients with FTLD.Familial risk.The rate of C9orf72, MAPT, or GRN mutation-positive FTLD in this series was 15.4%. Categories designating the risk level for hereditary cause were termed high, medium, low, apparent sporadic, and unknown significance. Thirty-nine pedigrees (12.7%) met criteria for high, 31 (10.1%) for medium, 46 (15.0%) for low, 91 (29.7%) for apparent sporadic, and 99 (32.4%) for unknown significance. The mutation-detection rates were as follows: high, 64.1%; medium, 29%; low, 10.9%; apparent sporadic, 1.1%; and unknown significance, 7.1%. Mutation-detection rates differed significantly between the high and other categories.Mutation rates are high in FTLD spectrum disorders, and the proposed criteria provide a validated standard for the classification of FTLD pedigrees. The combination of pedigree criteria and mutation-detection rates has important implications for genetic counseling and testing in clinical settings.

Dataset Information

Development and validation of a simple lupus severity index using ACR criteria for classification of SLE.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets