Springer Science and Business Media LLC Acta Medica Okayama 1342-1751 29 5 2025 Development and validation of an algorithm for identifying patients undergoing dialysis from patients with advanced chronic kidney disease 650 661 EN Takahiro Imaizumi Department of Nephrology, Nagoya University Graduate School of Medicine Takashi Yokota Institute of Health Science Innovation for Medical Care, Hokkaido University Hospital Kouta Funakoshi Kyusyu University Hospital Kazushi Yasuda Department of Nephrology, Nagoya University Graduate School of Medicine Akiko Hattori Department of Nephrology, Nagoya University Graduate School of Medicine Akemi Morohashi Department of Advanced Medicine, Nagoya University Hospital Tatsumi Kusakabe Institute of Health Science Innovation for Medical Care, Hokkaido University Hospital Masumi Shojima Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University Sayoko Nagamine Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University Toshiaki Nakano Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University Yong Huang Division of Medical Informatics, Okayama University Hospital Hiroshi Morinaga Department of Comprehensive Therapy for Chronic Kidney Disease, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Miki Ohta Clinical Research Promotion Center, The University of Tokyo Hospital Satomi Nagashima Department of Healthcare Information Management, The University of Tokyo Hospital Ryusuke Inoue Medical Information Technology Center, Tohoku University Hospital Naoki Nakamura Medical Information Technology Center, Tohoku University Hospital Hideki Ota Medical Information Technology Center, Tohoku University Hospital Tatsuya Maruyama Clinical Research Promotion Center, The University of Tokyo Hospital Hideo Gobara Division of Medical Informatics, Okayama University Hospital Akira Endoh Department of Medical Informatics, Hokkaido University Hospital Masahiko Ando Department of Nephrology, Nagoya University Graduate School of Medicine Yoshimune Shiratori Medical IT Center, Nagoya University Hospital Shoichi Maruyama Department of Nephrology, Nagoya University Graduate School of Medicine Background Identifying patients on dialysis among those with an estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m2 remains challenging. To facilitate clinical research in advanced chronic kidney disease (CKD) using electronic health records, we aimed to develop algorithms to identify dialysis patients using laboratory data obtained in routine practice.<br> Methods We collected clinical data of patients with an eGFR < 15 mL/min/1.73 m2 from six clinical research core hospitals across Japan: four hospitals for the derivation cohort and two for the validation cohort. The candidate factors for the classification models were identified using logistic regression with stepwise backward selection. To ensure transplant patients were not included in the non-dialysis population, we excluded individuals with the disease code Z94.0.<br> Results We collected data from 1142 patients, with 640 (56%) currently undergoing hemodialysis or peritoneal dialysis (PD), including 426 of 763 patients in the derivation cohort and 214 of 379 patients in the validation cohort. The prescription of PD solutions perfectly identified patients undergoing dialysis. After excluding patients prescribed PD solutions, seven laboratory parameters were included in the algorithm. The areas under the receiver operation characteristic curve were 0.95 and 0.98 and the positive and negative predictive values were 90.9% and 91.4% in the derivation cohort and 96.2% and 94.6% in the validation cohort, respectively. The calibrations were almost linear.<br> Conclusions We identified patients on dialysis among those with an eGFR < 15 ml/min/1.73 m2. This study paves the way for database research in nephrology, especially for patients with non-dialysis-dependent advanced CKD. No potential conflict of interest relevant to this article was reported.