Osmanodja Bilgin, Sassi Zeineb, Eickmann Sascha, Hansen Carla Maria, Roller Roland, Burchardt Aljoscha, Samhammer David, Dabrock Peter, Möller Sebastian, Budde Klemens, Herrmann Anne
Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Department of Epidemiology and Preventive Medicine, Medical Sociology, University Regensburg, Regensburg, Germany.
JMIR Res Protoc. 2024 Apr 1;13:e54857. doi: 10.2196/54857.
Patients after kidney transplantation eventually face the risk of graft loss with the concomitant need for dialysis or retransplantation. Choosing the right kidney replacement therapy after graft loss is an important preference-sensitive decision for kidney transplant recipients. However, the rate of conversations about treatment options after kidney graft loss has been shown to be as low as 13% in previous studies. It is unknown whether the implementation of artificial intelligence (AI)-based risk prediction models can increase the number of conversations about treatment options after graft loss and how this might influence the associated shared decision-making (SDM).
This study aims to explore the impact of AI-based risk prediction for the risk of graft loss on the frequency of conversations about the treatment options after graft loss, as well as the associated SDM process.
This is a 2-year, prospective, randomized, 2-armed, parallel-group, single-center trial in a German kidney transplant center. All patients will receive the same routine post-kidney transplant care that usually includes follow-up visits every 3 months at the kidney transplant center. For patients in the intervention arm, physicians will be assisted by a validated and previously published AI-based risk prediction system that estimates the risk for graft loss in the next year, starting from 3 months after randomization until 24 months after randomization. The study population will consist of 122 kidney transplant recipients >12 months after transplantation, who are at least 18 years of age, are able to communicate in German, and have an estimated glomerular filtration rate <30 mL/min/1.73 m. Patients with multi-organ transplantation, or who are not able to communicate in German, as well as underage patients, cannot participate. For the primary end point, the proportion of patients who have had a conversation about their treatment options after graft loss is compared at 12 months after randomization. Additionally, 2 different assessment tools for SDM, the CollaboRATE mean score and the Control Preference Scale, are compared between the 2 groups at 12 months and 24 months after randomization. Furthermore, recordings of patient-physician conversations, as well as semistructured interviews with patients, support persons, and physicians, are performed to support the quantitative results.
The enrollment for the study is ongoing. The first results are expected to be submitted for publication in 2025.
This is the first study to examine the influence of AI-based risk prediction on physician-patient interaction in the context of kidney transplantation. We use a mixed methods approach by combining a randomized design with a simple quantitative end point (frequency of conversations), different quantitative measurements for SDM, and several qualitative research methods (eg, records of physician-patient conversations and semistructured interviews) to examine the implementation of AI-based risk prediction in the clinic.
ClinicalTrials.gov NCT06056518; https://clinicaltrials.gov/study/NCT06056518.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/54857.
肾移植患者最终面临移植肾失功的风险，同时需要进行透析或再次移植。肾移植受者在移植肾失功后选择合适的肾脏替代治疗是一项重要的偏好敏感决策。然而，先前研究表明，关于移植肾失功后治疗选择的讨论率低至13%。目前尚不清楚基于人工智能（AI）的风险预测模型的应用是否能增加移植肾失功后治疗选择的讨论次数，以及这可能如何影响相关的共同决策（SDM）。
本研究旨在探讨基于AI的移植肾失功风险预测对移植肾失功后治疗选择讨论频率以及相关SDM过程的影响。
这是一项在德国一家肾移植中心进行的为期2年的前瞻性、随机、双臂、平行组、单中心试验。所有患者将接受相同的肾移植术后常规护理，通常包括每3个月在肾移植中心进行随访。对于干预组的患者，医生将借助一个经过验证且先前已发表的基于AI的风险预测系统，该系统从随机分组后3个月开始直至随机分组后24个月，估计下一年移植肾失功的风险。研究人群将包括122例移植后超过12个月的肾移植受者，他们至少18岁，能够用德语交流，且估计肾小球滤过率<30 mL/min/1.73 m²。多器官移植患者、不能用德语交流的患者以及未成年患者不能参与。对于主要终点，在随机分组后12个月比较移植肾失功后讨论过治疗选择的患者比例。此外，在随机分组后12个月和24个月，比较两组之间用于SDM的两种不同评估工具，即CollaboRATE平均得分和控制偏好量表。此外，进行医患对话录音以及对患者、支持人员和医生的半结构化访谈，以支持定量结果。
该研究正在招募患者。预计首批结果将于2025年提交发表。
这是第一项研究基于AI的风险预测对肾移植背景下医患互动影响的研究。我们采用混合方法，将随机设计与简单的定量终点（讨论频率）、SDM的不同定量测量以及几种定性研究方法（如医患对话记录和半结构化访谈）相结合，以研究基于AI的风险预测在临床中的应用。
ClinicalTrials.gov NCT06056518；https://clinicaltrials.gov/study/NCT06056518。
国际注册报告识别码（IRRID）：PRR1-10.2196/54857。
