Algorithm for calculating high disease activity in SLE

BACKGROUND: The ability to identify lupus patients in high disease activity status (HDAS) without knowledge of the SLEDAI could have application in selection of patients for treatment escalation or enrolment in trials. We sought to generate an algorithm that could calculate via model fitting the presence of HDAS using simple demographic and laboratory values. METHODS: We examined the association of high disease activity (HDA) with demographic and laboratory parameters using prospectively collected data. An HDA visit is recorded when SLEDAI-2K ≥10. We utilized the use of combinatorial search to find algorithms to build a mathematical model predictive of HDA. Performance of each algorithm was evaluated using multi-class area under the receiver operating characteristic curve and the final model was compared with the naïve Bayes classifier, and analysed using the confusion matrix for accuracy and misclassification rate. RESULTS: Data on 286 patients, followed for a median of 5.1 years were studied for a total of 5680 visits. Sixteen laboratory parameters were found to be significantly associated with HDA. A total of 216 algorithms were evaluated and the final algorithm chosen was based on seven pathology measures and three demographic variables. It has an accuracy of 88.6% and misclassification rate of 11.4%. When compared with the naïve Bayes classifier [area under the curve (AUC) = 0.663], our algorithm has a better accuracy with AUC = 0.829. CONCLUSION: This study shows that building an accurate model to calculate HDA using routinely available clinical parameters is feasible. Future studies to independently validate the algorithm will be needed to confirm its predictive performance.