Evidence map›Paper›PMID 42112138›Full record

ArticleKidney medicine2026

Identification of Maintenance Dialysis Recipients From an Electronic Clinical Information System and Effects on Estimates of Incidence and Prevalence.

Ping Liu, Mitchell Rath, Robert R Quinn, Ngan N Lam, Tyrone G Harrison, Paul E Ronksley, Jennifer M MacRae, Mark Suffak, Mark Courtney, Nikhil Shah and 4 more

Abstract read
In one paragraph

Article in Kidney medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

14 authors.

Ping LiuDepartments of Medicine and Community Health Sciences, University of Calgary, Calgary, AB, Canada.
Mitchell RathAlberta Health Services, Provincial Research and Data Services, Calgary, AB, Canada.
Robert R QuinnDepartments of Medicine and Community Health Sciences, University of Calgary, Calgary, AB, Canada.
Ngan N LamDepartments of Medicine and Community Health Sciences, University of Calgary, Calgary, AB, Canada.
Tyrone G HarrisonDepartments of Medicine and Community Health Sciences, University of Calgary, Calgary, AB, Canada.
Paul E RonksleyDepartments of Medicine and Community Health Sciences, University of Calgary, Calgary, AB, Canada.
Jennifer M MacRaeDepartments of Medicine and Community Health Sciences, University of Calgary, Calgary, AB, Canada.
Mark SuffakDepartments of Medicine and Community Health Sciences, University of Calgary, Calgary, AB, Canada.
Mark CourtneyDepartment of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada.
Nikhil ShahDepartment of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada.
Neesh PannuDepartment of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada.
Pietro RavaniDepartments of Medicine and Community Health Sciences, University of Calgary, Calgary, AB, Canada.
Braden J MannsDepartments of Medicine and Community Health Sciences, University of Calgary, Calgary, AB, Canada.
Matthew T JamesDepartments of Medicine and Community Health Sciences, University of Calgary, Calgary, AB, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rationale & Objective: Clinical information systems (CISs) can support case finding. However, standardized methods for identifying maintenance dialysis recipients and assessing how CIS transitions influence disease burden estimates are lacking. We aimed to develop and evaluate methods for identifying maintenance dialysis recipients across CIS transitions and examine trends in incidence and prevalence across the implementation timeline. Study Design: Population-based retrospective cohort study. Setting & Participants: Alberta residents aged ≥20 years, who initiated (incident) or received (prevalent) maintenance dialysis from January 2012 to March 2024, were identified using clinical data (historical kidney care program data and a new Epic-based CIS) versus administrative claims. Study periods included preimplementation (January 2012 to November 2019; use of historical program data), transition (November 2019 to November 2022; combination of data sources), and full implementation of the CIS (November 2022 to March 2024). Claims data were available throughout. Maintenance dialysis was defined as in-center hemodialysis for >90 days or initiation of peritoneal/home hemodialysis, regardless of duration. Results: The algorithms identified 7,766 incident cases from clinical data and 7,556 from claims, with 7,391 (93.2%) overlapping. Incidence rates increased slightly over time and were comparable between data sources. Prevalence estimates were higher in claims than historical program data preimplementation but converged during the full CIS implementation period. In 2023, CIS-based incidence was 218.5/million population versus 199.9/million population from claims, and CIS-based prevalence was 1,018.3 versus 1,006.8 from claims. Dialysis initiation dates aligned within 28 days for 84.6% of patients, and period prevalence agreed well between data sources. Limitations: Exclusion of patients with kidney failure receiving dialysis for <90 days. Conclusions: Standardized methods for identifying incident maintenance dialysis recipients showed agreement between CIS and claims-based data, whereas the use of historical kidney care program data underidentified prevalent patients in Alberta. These approaches can optimize the identification of maintenance dialysis recipients from routinely collected health information.

Indexed as

administrative health dataalgorithmcase definitionclaimsclinical information systemsepidemiologyincidenceMaintenance dialysisprevalence

Identifiers

PMID42112138
PMCPMC13156745

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.