Evidence map›Paper›PMID 34278540›Full record

Trial reportQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2022

Mapping the Edmonton Symptom Assessment System-Revised: Renal to the EQ-5D-5L in patients with chronic kidney disease.

Jiabi Wen, Xuejing Jin, Fatima Al Sayah, Hilary Short, Arto Ohinmaa, Sara N Davison, Michael Walsh, Jeffrey A Johnson

Abstract readMulticenter StudyRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Jiabi WenSchool of Public Health, University of Alberta, Edmonton, AB, Canada.
Xuejing JinCentre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Fatima Al SayahSchool of Public Health, University of Alberta, Edmonton, AB, Canada.
Hilary ShortSchool of Public Health, University of Alberta, Edmonton, AB, Canada.
Arto OhinmaaSchool of Public Health, University of Alberta, Edmonton, AB, Canada.
Sara N DavisonDivision of Nephrology and Immunology, Department of Medicine, University of Alberta, Edmonton, AB, Canada.
Michael WalshDepartment of Medicine, McMaster University, Hamilton, ON, Canada.
Jeffrey A JohnsonSchool of Public Health, University of Alberta, Edmonton, AB, Canada. jeff.johnson@ualberta.ca.ORCID http://orcid.org/0000-0001-8290-2857
University of Alberta · CABeijing University of Chinese Medicine · CNPopulation Health Research Institute · CA

Funding

CIHR SCA-145103McMaster University Clive Kearon Mid Career Research Award
6 · The paper itself

Abstract

purposeThe Edmonton Symptom Assessment System-Revised: Renal (ESAS-r: Renal) is a disease-specific patient-reported outcome measure (PROM) that assesses symptoms common in chronic kidney disease (CKD). There is no preference-based scoring system for the ESAS-r: Renal or a mapping algorithm to predict health utility values. We aimed to develop a mapping algorithm from the ESAS-r: Renal to the Canadian EQ-5D-5L index scores.

methodsWe used data from a multi-centre cluster randomized-controlled trial of the routine measurement and reporting of PROMs in hemodialysis units in Northern Alberta, Canada. In two arms of the trial, both the ESAS-r: Renal and the EQ-5D-5L were administered to CKD patients undergoing hemodialysis. We used data from one arm for model estimation, and data from the other for validation. We explored direct and indirect mapping models; model selection was based on statistical fit and predictive power.

resultsComplete data were available for 506 patient records in the estimation sample and 242 in the validation sample. All models tended to perform better in patients with good health, and worse in those with poor health. Generalized estimating equations (GEE) and generalized linear model (GLM) on selected ESAS-r: Renal items were selected as final models as they fitted the best in estimation and validation sample.

conclusionWhen only ESAS-r: Renal data are available, one could use GEE and GLM to predict EQ-5D-5L index scores for use in economic evaluation. External validation on populations with different characteristics is warranted, especially where renal-specific symptoms are more prevalent.

Indexed as

Quality of LifeRenal Insufficiency, ChronicAlbertaHumansSurveys and QuestionnairesSymptom AssessmentChronic kidney diseaseEQ-5D-5LESAS-r: RenalHemodialysisMappingUtility

Identifiers

PMID34278540
OpenAlexW3186024635

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.