Data Driven Approach to Characterize Rapid Decline in Autosomal Dominant Polycystic Kidney Disease

John J. Sim,Yu-Hsiang Shu,Simran K. Bhandari, Qiaoling Chen, Teresa N. Harrison,Min Young Lee, Mercedes A. Munis, Kerresa Morrissette,Shirin Sundar,Kristin Pareja, Ali Nourbakhsh,Cynthia J. Willey

medrxiv(2024)

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摘要
Background Autosomal dominant polycystic kidney disease (ADPKD) is a genetic kidney disease with high phenotypic variability. Insights into ADPKD progression could lead to earlier detection and management prior to end stage kidney disease (ESKD). We sought to identify patients with rapid decline (RD) in kidney function and to determine clinical factors associated with RD using a data-driven approach. Methods A retrospective cohort study was performed among patients with incident ADPKD (1/1/2002-12/31/2018). Latent class mixed models were used to identify RD patients using rapidly declining eGFR trajectories over time. Predictors of RD were selected based on agreements among feature selection methods, including logistic, regularized, and random forest modeling. The final model was built on the selected predictors and clinically relevant covariates. Results Among 1,744 patients with incident ADPKD, 125 (7%) were identified as RD. Feature selection included 42 clinical measurements for adaptation with multiple imputations; mean (SD) eGFR was 85.2 (47.3) and 72.9 (34.4) in the RD and non-RD groups, respectively. Multiple imputed datasets identified variables as important features to distinguish RD and non-RD groups with the final prediction model determined as a balance between area under the curve (AUC) and clinical relevance which included 6 predictors: age, sex, hypertension, cerebrovascular disease, hemoglobin, and proteinuria. Results showed 72%-sensitivity, 70%-specificity, 70%-accuracy, and 0.77-AUC in identifying RD. 5-year ESKD rates were 38% and 7% among RD and non-RD groups, respectively. Conclusion Using real-world routine clinical data among patients with incident ADPKD, we observed that six variables highly predicted RD in kidney function. ### Competing Interest Statement Shirin Sundar,Kristin Pareja, and Ali Nourbakhsh are current or former employees of Otsuka Pharmaceutical Development & Commercialization, Inc., Princeton, NJ ### Clinical Trial NA ### Clinical Protocols NA ### Funding Statement Yes ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Not Applicable The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The KPSC Institutional Review Board (IRB) reviewed and approved the protocol of this study (#11823). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Not Applicable I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Not Applicable I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Not Applicable Data are provided in manuscript and in supplemental attachments. Additional information will be made available upon request to corresponding author. NA
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