Predicting the effect of sirolimus on disease activity in patients with systemic lupus erythematosus using machine learning

Abstract

What Is Known and Objectives: The present study aimed to predict the effect of sirolimus on disease activity in patients with systemic lupus erythematosus (SLE) using machine learning and to recommend appropriate sirolimus dosage regimen for patients with SLE.Methods: The E max model was selected for machine learning, where the evaluation indicator was the change rate of systemic lupus erythematosus disease activity index from baseline value.Results: A total 103 patients with SLE were included for modelling, where the E max , ET 50 were À53.9%, 1.53 months in the final model respectively, and the evaluation of the final model was good. Further simulation found that the follow-up time to achieve 25%, 50%, 75% and 80% (plateau) E max of sirolimus effecting on disease activity in patients with SLE were 0.51, 1.53, 4.59 and 6.12 months, respectively. In addition, the sirolimus dosage was flexible and adjusted according to drug concentration, where the intersection of sirolimus concentration range included in this study was about 8-10 ng/ml.What Is New and Conclusions: This study was the first time to predict the effect of sirolimus on disease activity in patients with SLE and in order to achieve better therapeutic effect maintaining a concentration of 8-10 ng/ml sirolimus for at least 6.12 months was necessary.

Systemic lupus in the era of machine learning medicine

“…161 162 Other outcomes for patients with SLE included reduced risk of breast cancer with the presence of prognostic genetic biomarkers (ie, IRF7, IFI35 and EIF2AK2 gene expression) identified with LASSO. 165 Models for the prediction of joint erosions LR model (AUC 0.806), 164 herpes infection (RF, AUC 0.942) 167 and hypothyroidism (RF, AUC 0.772) 166 have also been developed using clinical and serological data. Among the selected features for these models, autoantibodies were found to be important predictors, for example, anti-carbamylated protein and anti-citrullinated protein antibodies for joint erosion 164 and anti-dsDNA and anti-SSB/La for hypothyroidism.…”

“…Among the selected features for these models, autoantibodies were found to be important predictors, for example, anti-carbamylated protein and anti-citrullinated protein antibodies for joint erosion 164 and anti-dsDNA and anti-SSB/La for hypothyroidism. 167 ML models showed promise in predicting the risk of hospitalisation and length of stay from EMR data (best performing models LSTM and XGBoost, AUC 0.88) 20 41 42 142 169 and associated healthcare costs from administrative databases. 17 142 FUTURE CONSIDERATIONS AI applications have become ubiquitous in medicine, and their impact on SLE care and research is no exception.…”