Optimization of Rituximab Therapy in Adult Patients With PLA2R1-Associated Membranous Nephropathy With Artificial Intelligence

Abstract

Introduction: Rituximab is a first-line treatment for membranous nephropathy. Nephrotic syndrome limits rituximab exposure due to urinary drug loss. Rituximab underdosing (serum level <2 μg/ml at month-3) is a risk factor for treatment failure. We developed a machine learning algorithm to predict the risk of underdosing based on patients' characteristics at rituximab infusion. We investigated the relationship between the predicted risk of underdosing and the cumulative dose of rituximab required to achieve remission.

Methods: Rituximab concentrations were measured at month-3 in 92 sera from adult patients with primary membranous nephropathy, split into a training (75%) and a testing set (25%). A forward-backward machine-learning procedure determined the best combination of variables to predict rituximab underdosing in the training data set, which was tested in the test set. The performances were evaluated for accuracy, sensitivity, and specificity in 10-fold cross-validation training and test sets.

Results: The best variables combination to predict rituximab underdosing included age, gender, body surface area (BSA), anti-phospholipase A2 receptor type 1 (anti-PLA2R1) antibody titer on day-0, serum albumin on day-0 and day-15, and serum creatinine on day-0 and day-15. The accuracy, sensitivity, and specificity were respectively 79.4%, 78.7%, and 81.0% (training data set), and 79.2%, 84.6% and 72.7% (testing data set). In both sets, the algorithm performed significantly better than chance (P < 0.05). Patients with an initial high probability of underdosing experienced a longer time to remission with higher rituximab cumulative doses required to achieved remission.

Conclusion: This algorithm could allow for early intensification of rituximab regimen in patients at high estimated risk of underdosing to increase the likelihood of remission.

Keywords: artificial intelligence; immunomonitoring; machine learning; nephrotic syndrome; primary membranous nephropathy; rituximab.

Graphical abstract

Figure 1

Study flow chart. Four patients were included in both data sets. Patients receiving multiple cures were considered independent “pharmacokinetically” because the lag time between the 2 infusions was greater than 9 months and rituximab levels were undetectable before reinjections.

Figure 2

Ability of machine learning algorithm to predict the categorical target range of rituximab levels in the training (a) and testing (b) data set.

Figure 3

Clinical impact of the probability of rituximab underdosing on the time to reach remission (a and b) and the rituximab cumulative dose received (c and d). Patients were classified into 3 categories according to the percentage risk of rituximab underdosing estimated by our algorithm: unlikely (<50%) in green, moderately likely (50%–75%) in orange and very likely (>75%) in red. In Figure 3d, each diamond represents individual data from one of the 40 patients.