Francesco Paolo Schena1,2, Vito Walter Anelli3, Tommaso Di Noia3, Giovanni Tripepi4, Daniela Isabel Abbrescia5, Maria Stangou6, Aikaterini Papagianni6, Maria Luisa Russo7, Graziella D'Arrigo4, Carlo Manno8. 1. Department of Emergency and Organ Transplantation, Nephrology, University of Bari "Aldo Moro", Bari, Italy. paolo.schena@uniba.it. 2. Schena Foundation, Polyclinic, Bari, Italy. paolo.schena@uniba.it. 3. Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy. 4. CNR-IFC, Clinical Epidemiology and Pathophysiology of Renal Diseases and Hypertension, Renal Unit, General Hospital, Reggio Calabria, Italy. 5. Schena Foundation, Polyclinic, Bari, Italy. 6. Department of Nephrology, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloníki, Greece. 7. Fondazione Ricerca Molinette, Turin, Italy. 8. Department of Emergency and Organ Transplantation, Nephrology, University of Bari "Aldo Moro", Bari, Italy.
Abstract
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.
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.
Authors: Sean J Barbour; Rosanna Coppo; Hong Zhang; Zhi-Hong Liu; Yusuke Suzuki; Keiichi Matsuzaki; Ritsuko Katafuchi; Lee Er; Gabriela Espino-Hernandez; S Joseph Kim; Heather N Reich; John Feehally; Daniel C Cattran Journal: JAMA Intern Med Date: 2019-07-01 Impact factor: 21.873
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