start-ver=1.4
cd-journal=joma
no-vol=29
cd-vols=
no-issue=5
article-no=
start-page=650
end-page=661
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2025
dt-pub=20250106
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Development and validation of an algorithm for identifying patients undergoing dialysis from patients with advanced chronic kidney disease
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=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.
en-copyright=
kn-copyright=

en-aut-name=ImaizumiTakahiro
en-aut-sei=Imaizumi
en-aut-mei=Takahiro
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=YokotaTakashi
en-aut-sei=Yokota
en-aut-mei=Takashi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=FunakoshiKouta
en-aut-sei=Funakoshi
en-aut-mei=Kouta
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

en-aut-name=YasudaKazushi
en-aut-sei=Yasuda
en-aut-mei=Kazushi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=

en-aut-name=HattoriAkiko
en-aut-sei=Hattori
en-aut-mei=Akiko
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=

en-aut-name=MorohashiAkemi
en-aut-sei=Morohashi
en-aut-mei=Akemi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=6
ORCID=

en-aut-name=KusakabeTatsumi
en-aut-sei=Kusakabe
en-aut-mei=Tatsumi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=7
ORCID=

en-aut-name=ShojimaMasumi
en-aut-sei=Shojima
en-aut-mei=Masumi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=8
ORCID=

en-aut-name=NagamineSayoko
en-aut-sei=Nagamine
en-aut-mei=Sayoko
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=9
ORCID=

en-aut-name=NakanoToshiaki
en-aut-sei=Nakano
en-aut-mei=Toshiaki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=10
ORCID=

en-aut-name=HuangYong
en-aut-sei=Huang
en-aut-mei=Yong
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=11
ORCID=

en-aut-name=MorinagaHiroshi
en-aut-sei=Morinaga
en-aut-mei=Hiroshi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=12
ORCID=

en-aut-name=OhtaMiki
en-aut-sei=Ohta
en-aut-mei=Miki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=13
ORCID=

en-aut-name=NagashimaSatomi
en-aut-sei=Nagashima
en-aut-mei=Satomi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=14
ORCID=

en-aut-name=InoueRyusuke
en-aut-sei=Inoue
en-aut-mei=Ryusuke
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=15
ORCID=

en-aut-name=NakamuraNaoki
en-aut-sei=Nakamura
en-aut-mei=Naoki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=16
ORCID=

en-aut-name=OtaHideki
en-aut-sei=Ota
en-aut-mei=Hideki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=17
ORCID=

en-aut-name=MaruyamaTatsuya
en-aut-sei=Maruyama
en-aut-mei=Tatsuya
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=18
ORCID=

en-aut-name=GobaraHideo
en-aut-sei=Gobara
en-aut-mei=Hideo
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=19
ORCID=

en-aut-name=EndohAkira
en-aut-sei=Endoh
en-aut-mei=Akira
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=20
ORCID=

en-aut-name=AndoMasahiko
en-aut-sei=Ando
en-aut-mei=Masahiko
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=21
ORCID=

en-aut-name=ShiratoriYoshimune
en-aut-sei=Shiratori
en-aut-mei=Yoshimune
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=22
ORCID=

en-aut-name=MaruyamaShoichi
en-aut-sei=Maruyama
en-aut-mei=Shoichi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=23
ORCID=

affil-num=1
en-affil=Department of Nephrology, Nagoya University Graduate School of Medicine
kn-affil=

affil-num=2
en-affil=Institute of Health Science Innovation for Medical Care, Hokkaido University Hospital
kn-affil=

affil-num=3
en-affil=Kyusyu University Hospital
kn-affil=

affil-num=4
en-affil=Department of Nephrology, Nagoya University Graduate School of Medicine
kn-affil=

affil-num=5
en-affil=Department of Nephrology, Nagoya University Graduate School of Medicine
kn-affil=

affil-num=6
en-affil=Department of Advanced Medicine, Nagoya University Hospital
kn-affil=

affil-num=7
en-affil=Institute of Health Science Innovation for Medical Care, Hokkaido University Hospital
kn-affil=

affil-num=8
en-affil=Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University
kn-affil=

affil-num=9
en-affil=Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University
kn-affil=

affil-num=10
en-affil=Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University
kn-affil=

affil-num=11
en-affil=Division of Medical Informatics, Okayama University Hospital
kn-affil=

affil-num=12
en-affil=Department of Comprehensive Therapy for Chronic Kidney Disease, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
kn-affil=

affil-num=13
en-affil=Clinical Research Promotion Center, The University of Tokyo Hospital
kn-affil=

affil-num=14
en-affil=Department of Healthcare Information Management, The University of Tokyo Hospital
kn-affil=

affil-num=15
en-affil=Medical Information Technology Center, Tohoku University Hospital
kn-affil=

affil-num=16
en-affil=Medical Information Technology Center, Tohoku University Hospital
kn-affil=

affil-num=17
en-affil=Medical Information Technology Center, Tohoku University Hospital
kn-affil=

affil-num=18
en-affil=Clinical Research Promotion Center, The University of Tokyo Hospital
kn-affil=

affil-num=19
en-affil=Division of Medical Informatics, Okayama University Hospital
kn-affil=

affil-num=20
en-affil=Department of Medical Informatics, Hokkaido University Hospital
kn-affil=

affil-num=21
en-affil=Department of Nephrology, Nagoya University Graduate School of Medicine
kn-affil=

affil-num=22
en-affil=Medical IT Center, Nagoya University Hospital
kn-affil=

affil-num=23
en-affil=Department of Nephrology, Nagoya University Graduate School of Medicine
kn-affil=
en-keyword=Chronic kidney disease
kn-keyword=Chronic kidney disease
en-keyword=Algorithm
kn-keyword=Algorithm
en-keyword=Classification
kn-keyword=Classification
en-keyword=Dialysis
kn-keyword=Dialysis
END