背景:肾移植后患者最终面临移植物丢失的风险,同时需要透析或再次移植。移植物丢失后选择正确的肾脏替代治疗是肾移植受者的重要偏好敏感决定。然而,在之前的研究中,关于肾移植物丢失后治疗方案的讨论率低至13%.目前尚不清楚基于人工智能(AI)的风险预测模型的实施是否可以增加有关移植物丢失后治疗方案的对话数量,以及这可能如何影响相关的共享决策(SDM)。
目的:本研究旨在探讨基于AI的移植物丢失风险预测对移植物丢失后治疗选择的讨论频率的影响,以及相关的SDM流程。
方法:这是一个2年,prospective,随机化,2武装,平行组,德国肾脏移植中心的单中心试验。所有患者都将接受相同的常规肾移植后护理,通常包括在肾移植中心每3个月进行一次随访。对于介入治疗组的患者,医生将得到一个经过验证和先前发布的基于人工智能的风险预测系统的帮助,该系统估计明年移植物丢失的风险。从随机化后3个月开始直到随机化后24个月.研究人群将包括移植后12个月以上的122名肾移植受者,至少18岁的人,能够用德语交流,估计肾小球滤过率<30mL/min/1.73m2。多器官移植患者,或者不能用德语交流的人,以及未成年患者,不能参与。对于主要终点,我们在随机分组后12个月比较了在移植物丢失后就治疗方案进行了讨论的患者比例.此外,2种不同的SDM评估工具,协作平均分和控制偏好量表,在随机分组后12个月和24个月比较2组。此外,病人-医生谈话的录音,以及对患者的半结构化访谈,支持人员,和医生,是为了支持定量结果而执行的。
结果:本研究的登记工作正在进行中。第一批结果预计将于2025年提交发布。
结论:这是第一项研究在肾移植的背景下,基于AI的风险预测对医患互动的影响。我们使用混合方法方法,将随机设计与简单的定量终点(对话频率)相结合,SDM的不同定量测量,和几种定性研究方法(例如,医患对话和半结构化访谈的记录),以检查临床中基于AI的风险预测的实施情况。
背景:ClinicalTrials.govNCT06056518;https://clinicaltrials.gov/study/NCT06056518。
■PRR1-10.2196/54857。
BACKGROUND: 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).
OBJECTIVE: 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.
METHODS: 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 m2. 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.
RESULTS: The enrollment for the study is ongoing. The first results are expected to be submitted for publication in 2025.
CONCLUSIONS: 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.
BACKGROUND: ClinicalTrials.gov NCT06056518; https://clinicaltrials.gov/study/NCT06056518.
UNASSIGNED: PRR1-10.2196/54857.