A Preliminary Predictive Model for Proliferative Lupus Nephritis in Juvenile Systemic Lupus Erythematosus

Proliferative lupus nephritis, which is diagnosed by renal biopsy, has significant impact on the treatment choices and long-term prognosis of juvenile SLE (jSLE). Renal biopsies are however not always possible or available, thus leading to an ongoing search for alternative biomarkers. This study aimed to develop a clinical predictive machine learning model using routine standard parameters as an alternative tool to evaluate the probability of proliferative lupus nephritis (ISN/RPS Class III or IV). Data were collected retrospectively from jSLE patients seen at Selayang Hospital from 2004 to 2021. A total of 22 variables including demographic, clinical and laboratory features were analyzed. A recursive feature elimination technique was used to identify factors to predict pediatric proliferative lupus nephritis. Various models were then used to build predictive machine learning models and assessed for sensitivity, specificity and accuracy. There were 194 jSLE patients (165 females), of which 111 had lupus nephritis (54 proliferative pattern). A combination of 11 variables consisting of gender, ethnicity, fever, nephrotic state, hypertension, urine red blood cells (RBC), C3, C4, duration of illness, serum albumin, and proteinuria demonstrated the highest accuracy of 79.4% in predicting proliferative lupus nephritis. A decision-tree model performed the best with an AROC of 69.9%, accuracy of 73.85%, sensitivity of 78.72% and specificity of 61.11%. A potential clinically useful predictive model using a combination of 11 non-invasive variables to collectively predict pediatric proliferative lupus nephritis in daily practice was developed.