Introduction: We aimed to estimate the rate of kidney function decline over 10 years in the general population and develop a machine learning model to predict it. Methods: We used the JMDC database from 2012 to 2021, which includes company employees and their family members in Japan, where annual health checks are mandated for people aged 40-74 years. We estimated the slope (average change) of estimated glomerular filtration rate (eGFR) over a period of 10 years. Then, using the annual health-check results and prescription claims for the first five years from 2012 to 2016 as predictor variables, we developed an XGBoost model, evaluated its prediction performance with the root mean squared error (RMSE), R2, and area under the receiver operating characteristic curve (AUROC) for rapid decliners (defined as the slope <-3 ml/min/1.73 m2/year) using 5-fold cross validation, and compared these indicators with those of the linear regression model using only eGFR data from 2012 to 2016. Results: We included 126 424 individuals (mean age, 45.2 years; male, 82.4%; mean eGFR, 79.0 ml/min/1.73 m2 in 2016). The mean slope was -0.89 (standard deviation, 0.96) ml/min/1.73 m2/year. The predictive performance of the XGBoost model (RMSE, 0.78; R2, 0.35; and AUROC, 0.89) was better than that of the linear regression model using only eGFR data (RMSE, 1.94; R2, -3.03; and AUROC, 0.79). Conclusion: Application of machine learning to annual health-check and claims data could predict the rate of kidney function decline, whereas the linear regression model using only eGFR data did not work.

O.K. and S.T. are employees of JMDC Inc. M.I. previously received honoraria from JMDC Inc. for conference presentations and academic consultations, but does not receive any fee for the present study.

none (self-funded)

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The data used in this study were anonymized and processed anonymously by JMDC, Inc. This study was approved by the Ethics Committee of The Research Institute of Healthcare Data Science (Date of approval, October 30, 2023; Approval number, RI 2023003).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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.

Yes

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).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

The data used in this study were licensed by JMDC Inc. Proposals and requests for data access should be directed to the corresponding authors via email.