Background: Recently, a tool based on two different artificial neural networks has been developed. The first network predicts kidney failure (KF) development while the second predicts the time frame to reach this outcome. In this study, we conducted a post-hoc analysis to evaluate the discordant results obtained by the tool.
Methods: The tool performance was analyzed in a retrospective cohort of 1116 adult IgAN patients, as were the causes of discordance between the predicted and observed cases of KF.
Results: There was discordance between the predicted and observed KF in 216 IgAN patients (19.35%) all of whom were elderly, hypertensive, had high serum creatinine levels, reduced renal function and moderate or severe renal lesions. Many of these patients did not receive therapy or were non-responders to therapy. In other IgAN patients the tool predicted KF but the outcome was not reached because patients responded to therapy. Therefore, in the discordant group (prediction did not match the observed outcome) the proportion of patients having or not having KF was strongly associated with treatment (P < 0.0001).
Conclusions: The post-hoc analysis shows that discordance in a low number of patients is not an error, but rather the effect of positive response to therapy. Thus, the tool could both help physicians to determine the prognosis of the disease and help patients to plan for their future.
Keywords: Chronic kidney disease; Clinical decision support system; Immunoglobulin A nephropathy; Kidney biopsy; Kidney failure.
© 2022. The Author(s) under exclusive licence to Italian Society of Nephrology.