A systematic review of statistical methodology used to evaluate progression of chronic kidney disease using electronic healthcare records

Abstract

Background Electronic healthcare records (EHRs) are a useful resource to study chronic kidney disease (CKD) progression prior to starting dialysis, but pose methodological challenges as kidney function tests are not done on everybody, nor are tests evenly spaced. We sought to review previous research of CKD progression using renal function tests in EHRs, investigating methodology used and investigators’ recognition of data quality issues. Methods and findings We searched for studies investigating CKD progression using EHRs in 4 databases (Medline, Embase, Global Health and Web of Science) available as of August 2021. Of 80 articles eligible for review, 59 (74%) were published in the last 5.5 years, mostly using EHRs from the UK, USA and East Asian countries. 33 articles (41%) studied rates of change in eGFR, 23 (29%) studied changes in eGFR from baseline and 15 (19%) studied progression to binary eGFR thresholds. Sample completeness data was available in 44 studies (55%) with analysis populations including less than 75% of the target population in 26 studies (33%). Losses to follow-up went unreported in 62 studies (78%) and 11 studies (14%) defined their cohort based on complete data during follow up. Methods capable of handling data quality issues and other methodological challenges were used in a minority of studies. Conclusions Studies based on renal function tests in EHRs may have overstated reliability of findings in the presence of informative missingness. Future renal research requires more explicit statements of data completeness and consideration of i) selection bias and representativeness of sample to the intended target population, ii) ascertainment bias where follow-up depends on risk, and iii) the impact of competing mortality. We recommend that renal progression studies should use statistical methods that take into account variability in renal function, informative censoring and population heterogeneity as appropriate to the study question.

Estimating and predicting kidney function decline in the general population

“…Second, loss to follow-up could cause selection bias, especially if unhealthy people are more likely to be lost to follow-up. 7 In the present study, older people and women were more likely to quit the JMDC database. We speculate that the main reasons for the loss to follow-up were social (e.g., retirement at age 60-65, retirement due to pregnancy and childbirth) and not directly associated with the health status of study participants, but we could not confirm the exact reasons.…”

“…The strength of this study is that we used consecutive 10-year annual health checkup data for the general adult population. A systematic review of kidney disease progression 7 indicated that following the same population for a long time is practically difficult, and only one study was reported to achieve the mean follow-up of over 10 years. 35 The mandatory health checkup system for people aged 40–74 years in Japan made the present study feasible.…”

“…Such prediction model would be useful for stratifying the general population and identifying those with rapid decline in kidney function. To date, no consensus has been reached on the definition of rapid decliners, 7 and therefore, the prediction of a continuous (rather than a dichotomous) outcome would have a wider application.…”

“…5 An old study estimated that the rate of GFR decline was -0.75 ml/min/year in the generally healthy population. 6 Since then, there have been a number of studies estimating the rate of kidney function decline, 7 but their study periods are often short and only a few studies targeting the general population without CKD are reported.…”