Schwab Simon, Sidler Daniel, Haidar Fadi, Kuhn Christian, Schaub Stefan, Koller Michael, Mellac Katell, Stürzinger Ueli, Tischhauser Bruno, Binet Isabelle, Golshayan Déla, Müller Thomas, Elmer Andreas, Franscini Nicola, Krügel Nathalie, Fehr Thomas, Immer Franz
Swisstransplant, Bern, Switzerland.
Department of Nephrology and Hypertension, Inselspital, Bern University Hospital, Bern, Switzerland.
Diagn Progn Res. 2023 Mar 7;7(1):6. doi: 10.1186/s41512-022-00139-5.
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.
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.
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.
Open Science Framework ID: z6mvj.
已确定许多用于预测肾移植结果的潜在预后因素。然而，在瑞士，临床实践中尚未常规使用被广泛接受的移植结果预后模型或风险评分。我们旨在为瑞士肾移植后的移植物存活、生活质量和移植物功能预后开发三种预测模型。
临床肾脏预测模型（KIDMO）是利用来自一项全国多中心队列研究（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。主要结局是肾移植存活（将受者死亡作为竞争风险）；次要结局是12个月时的生活质量（患者报告的健康状况）和估计肾小球滤过率（eGFR）斜率。器官捐献者、移植及受者相关的临床信息将在器官分配时用作预测因素。我们将分别对主要结局和两个次要结局使用Fine & Gray亚分布模型和线性混合效应模型。将使用自抽样法、内部-外部交叉验证以及荟萃分析方法评估模型的乐观性、校准、辨别力和各移植中心之间的异质性。
瑞士移植领域缺乏对肾移植存活或患者报告结局的现有风险评分的全面评估。为了在临床实践中有用，预后评分需要有效、可靠、具有临床相关性，并且最好纳入决策过程以改善患者长期结局并为临床医生及其患者提供决策依据。通过考虑竞争风险并利用专家知识进行变量选择的先进方法应用于来自全国前瞻性多中心队列研究的数据。理想情况下，医疗服务提供者与患者可以预先确定他们愿意接受的来自 deceased-donor 肾脏的风险，并获得移植物存活、生活质量和移植物功能的估计值以供考虑。
开放科学框架标识符：z6mvj。
