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, Déla Golshayan, Thomas Müller, Andreas Elmer, Nicola Franscini, Nathalie Krügel, Thomas Fehr, Franz Immer
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Abstract

Background: Many 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.

Methods: The 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.

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肾移植受者预后临床预测模型(KIDMO):研究方案。
背景:目前已发现许多预测肾移植结果的潜在预后因素。然而,在瑞士,目前还没有被广泛接受的移植预后模型或风险评分被常规用于临床实践。我们的目标是为瑞士移植后的移植物存活率、生活质量和移植物功能的预后建立三个预测模型:临床肾脏预测模型(KIDMO)是根据一项全国性多中心队列研究(瑞士移植队列研究;STCS)和瑞士器官分配系统(SOAS)的数据开发的。主要结果是肾移植存活率(受者死亡为竞争风险);次要结果是 12 个月的生活质量(患者报告的健康状况)和估计肾小球滤过率(eGFR)斜率。在分配器官时,将使用器官捐献者、移植和受者相关的临床信息作为预测指标。我们将对主要结果和两个次要结果分别使用 Fine & Gray 子分布模型和线性混合效应模型。我们将使用引导法、内部-外部交叉验证法和荟萃分析法对模型的乐观程度、校准、区分度和移植中心之间的异质性进行评估:在瑞士的移植环境中,还缺乏对现有肾移植存活率或患者报告结果风险评分的全面评估。为了在临床实践中发挥作用,预后评分必须有效、可靠、与临床相关,最好能与决策过程相结合,以改善患者的长期预后,支持临床医生及其患者做出明智的决定。在一项全国性前瞻性多中心队列研究的数据中,采用了最先进的方法,利用专家知识考虑了竞争风险和变量选择。理想情况下,医疗服务提供者和患者可以预先确定他们愿意接受的已故供肾风险,并提供移植物存活率、生活质量和移植物功能估计值供他们考虑:研究注册:开放科学框架 ID:z6mvj。
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