Towards automated identification of changes in laboratory measurement of renal function: implications for longitudinal research and observing trends in glomerular filtration rate (GFR)

TL;DR

This study presents an algorithm to detect changes in serum creatinine assays affecting eGFR calculations in routine data, highlighting potential biases in longitudinal kidney function research.

Contribution

The paper introduces a novel algorithm to identify shifts in eGFR calculation methods and SCr assays in large-scale routine healthcare data.

Findings

Detected changes in eGFR calculation in 63% of practices

Identified systematic bias caused by calibration changes

Routine data may misrepresent true renal function trends

Abstract

Introduction: Kidney function is reported using estimates of glomerular filtration rate (eGFR). However, eGFR values are recorded without reference to the creatinine (SCr) assays used to derive them, and newer assays were introduced at different time points across laboratories in UK. These changes may cause systematic bias in eGFR reported in routinely collected data; even though laboratory reported eGFR values have a correction factor applied. Design: An algorithm to detect changes in SCr which affect eGFR calculation method by comparing the mapping of SCr values on to eGFR values across a time-series of paired eGFR and SCr measurements. Setting: Routinely collected primary care data from 20,000 people with the richest renal function data from the Quality Improvement in Chronic Kidney Disease (QICKD) trial. Results: The algorithm identified a change in eGFR calculation method in 80 (63%) of the 127 included practices. This change was identified in 4,736 (23.7%) patient time series analysed. This change in calibration method was found to cause a significant step change in reported eGFR values producing a systematic bias. eGFR values could not be recalibrated by applying the Modification of Diet in Renal Disease (MDRD) equation to the laboratory reported SCr values. Conclusions: This algorithm can identify laboratory changes in eGFR calculation methods and changes in SCr assay. Failure to account for these changes may misconstrue renal function changes over time. Researchers using routine eGFR data should account for these effects.