Evidence map›Paper›PMID 36879332›Full record

ArticleDiagnostic and prognostic research2023

Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol.

Simon Schwab, Daniel Sidler, Fadi Haidar, Christian Kuhn, Stefan Schaub, Michael Koller, Katell Mellac, Ueli Stürzinger, Bruno Tischhauser, Isabelle Binet and 9 more

Abstract read
In one paragraph

Article in Diagnostic and prognostic research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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.

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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

19 authors.

Simon SchwabSwisstransplant, Bern, Switzerland. simon.schwab@swisstransplant.org.ORCID http://orcid.org/0000-0002-1588-2689
Daniel SidlerDepartment of Nephrology and Hypertension, Inselspital, Bern University Hospital, Bern, Switzerland.
Fadi HaidarDepartment of Medicine, Division of Nephrology, University Hospital of Geneva, Geneva, Switzerland.
Christian KuhnNephrology and Transplantation Medicine, Kantonsspital St. Gallen, St. Gallen, Switzerland.
Stefan SchaubClinic for Transplantation Immunology and Nephrology, University Hospital Basel, Basel, Switzerland.
Michael KollerClinic for Transplantation Immunology and Nephrology, University Hospital Basel, Basel, Switzerland.
Katell MellacClinic for Transplantation Immunology and Nephrology, University Hospital Basel, Basel, Switzerland.
Ueli StürzingerSTCS Patient Advisory Board, University Hospital Basel, Basel, Switzerland.
Bruno TischhauserSTCS Patient Advisory Board, University Hospital Basel, Basel, Switzerland.
Isabelle BinetNephrology and Transplantation Medicine, Kantonsspital St. Gallen, St. Gallen, Switzerland.
Déla GolshayanTransplantation Center, Lausanne University Hospital, Lausanne, Switzerland.
Thomas MüllerDepartment of Nephrology, University Hospital Zurich, Zurich, Switzerland.
Andreas ElmerSwisstransplant, Bern, Switzerland.
Nicola FransciniSwisstransplant, Bern, Switzerland.
Nathalie KrügelSwisstransplant, Bern, Switzerland.
Thomas FehrDepartment of Internal Medicine, Cantonal Hospital Graubünden, Chur, Switzerland.
Franz ImmerSwisstransplant, Bern, Switzerland.
Swisstransplant Kidney Working Group (STAN)
Swiss Transplant Cohort Study

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany potential prognostic factors for predicting kidney transplantation outcomes have been identified. However, in Switzerland, no widely accepted prognostic model or risk score for transplantation outcomes is being routinely used in clinical practice yet. We aim to develop three prediction models for the prognosis of graft survival, quality of life, and graft function following transplantation in Switzerland.

methodsThe clinical kidney prediction models (KIDMO) are developed with data from a national multi-center cohort study (Swiss Transplant Cohort Study; STCS) and the Swiss Organ Allocation System (SOAS). The primary outcome is the kidney graft survival (with death of recipient as competing risk); the secondary outcomes are the quality of life (patient-reported health status) at 12 months and estimated glomerular filtration rate (eGFR) slope. Organ donor, transplantation, and recipient-related clinical information will be used as predictors at the time of organ allocation. We will use a Fine & Gray subdistribution model and linear mixed-effects models for the primary and the two secondary outcomes, respectively. Model optimism, calibration, discrimination, and heterogeneity between transplant centres will be assessed using bootstrapping, internal-external cross-validation, and methods from meta-analysis. DISCUSSION: Thorough evaluation of the existing risk scores for the kidney graft survival or patient-reported outcomes has been lacking in the Swiss transplant setting. In order to be useful in clinical practice, a prognostic score needs to be valid, reliable, clinically relevant, and preferably integrated into the decision-making process to improve long-term patient outcomes and support informed decisions for clinicians and their patients. The state-of-the-art methodology by taking into account competing risks and variable selection using expert knowledge is applied to data from a nationwide prospective multi-center cohort study. Ideally, healthcare providers together with patients can predetermine the risk they are willing to accept from a deceased-donor kidney, with graft survival, quality of life, and graft function estimates available for their consideration. STUDY REGISTRATION: Open Science Framework ID: z6mvj.

Indexed as

eGFREstimated glomerular filtration rateGraft survivalKidney transplantationPatient-reported health statusPrediction modelPrognosisPrognostic modelQuality of lifeRisk calculatorRisk score

Identifiers

PMID36879332
PMCPMC9990297

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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.