Development and validation of an algorithm for identifying patients undergoing dialysis from patients with advanced chronic kidney disease

TL;DR

This study developed a highly accurate algorithm to identify dialysis patients among those with severe kidney disease using routine lab data, improving research capabilities in nephrology.

Contribution

A novel algorithm using lab data to distinguish dialysis patients from non-dialysis patients with advanced CKD was developed and validated.

Findings

The algorithm achieved high accuracy with AUCs of 0.95 and 0.98 in derivation and validation cohorts.

Positive and negative predictive values were over 90% in both cohorts.

Excluding PD solution prescriptions improved algorithm specificity.

Abstract

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.

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.

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.

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.

The online version contains supplementary material available at 10.1007/s10157-024-02614-3.

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