Improving treatment decisions using personalized risk assessment from the International IgA Nephropathy Prediction Tool.
journal contributionposted on 10.06.2020, 10:48 by Sean J Barbour, Mark Canney, Rosanna Coppo, Hong Zhang, Zhi-Hong Liu, Yusuke Suzuki, Keiichi Matsuzaki, Ritsuko Katafuchi, Dilshani Induruwage, Lee Er, Heather N Reich, John Feehally, Jonathan Barratt, Daniel C Cattran, International IgA Nephropathy Network
Immunosuppression in IgA nephropathy (IgAN) should be reserved for patients at high-risk of disease progression, which KDIGO guidelines determine based solely on proteinuria 1g or more/day. To investigate if treatment decisions can be more accurately accomplished using individualized risk from the International IgAN Prediction Tool, we simulated allocation of a hypothetical immunosuppression therapy in an international cohort of adults with IgAN. Two decision rules for treatment were applied based on proteinuria 1g or more/day or predicted risk from the Prediction Tool above a threshold probability. An appropriate decision was defined as immunosuppression allocated to patients experiencing the primary outcome (50% decline in eGFR or ESKD) and withheld otherwise. The net benefit and net reduction in treatment are the proportion of patients appropriately allocated to receive or withhold immunosuppression, adjusted for the harm from inappropriate decisions, calculated for all threshold probabilities from 0-100%. Of 3299 patients followed for 5.1 years, 522 (15.8%) experienced the primary outcome. Treatment allocation based solely on proteinuria οϕ 1g or more/day had a negative net benefit (was harmful) because immunosuppression was increasingly allocated to patients without progressive disease. Compared to using proteinuria, treatment allocation using the Prediction Tool had a larger net benefit up to 23.4% (95% confidence interval 21.5-25.2%) and a larger net reduction in treatment up to 35.1% (32.3-37.8%). Thus, allocation of immunosuppression to high-risk patients with IgAN can be substantially improved using the Prediction Tool compared to using proteinuria.