Cho, Yeoungjee

Key Points Atrial fibrillation after kidney transplantation is common and is associated with poor prognosis. Artificial intelligence applied to 12-lead electrocardiograms pretransplant accurately predicts the risk of new-onset atrial fibrillation in our cohort. High-risk artificial intelligence electrocardiography score was independently associated with increased risk of mortality and allograft failure. Background Incident atrial fibrillation (AF) is common after kidney transplantation (KTx) and is associated with worse clinical outcomes. Artificial intelligence electrocardiography (AI-ECG) algorithms have demonstrated efficacy in predicting risk of new-onset AF in the general population; however, their prognostic value in KTx recipients is relatively unknown. Methods Retrospective analysis was conducted on KTx recipients without AF, with at least one pretransplant ECG between 2011 and 2021 across three tertiary centers in the United States (Mayo Clinic sites in MN, AZ, and FL). A previously validated AI-ECG algorithm estimated the probability of incident AF for each patient. Based on AI-ECG probabilities, patients were categorized into high-risk and low-risk groups, with the optimal AI-ECG score cutoff determined. The incidence of new-onset AF, allograft failure, and mortality was compared between groups. Results Overall, 6246 patients (age 53.5±13.8 years; 58.9% male) were included. Pretransplant AI-ECG probability of AF ≥5% was the optimal cutoff for high risk of incident AF (sensitivity 72%, specificity 62%). High-risk scores were associated with true new-onset AF at 30 days (adjusted hazard ratio [aHR], 2.89; 95% confidence intervals [CI], 2.05 to 4.09; P < 0.001), 3 years (aHR, 2.54; 95% CI, 1.99 to 3.26, P < 0.001), and 5 years post-transplant (aHR, 2.48; 95% CI, 1.99 to 3.09, P < 0.001). High-risk AI-ECG scores were also associated with increased mortality (aHR, 1.56; 95% CI, 1.30 to 1.88, P < 0.001) and overall allograft failure (aHR, 1.50; 95% CI, 1.30 to 1.75, P < 0.001) through the 5-year follow-up. Conclusions This pretransplant AI-ECG parameter identified patients at increased risk of new-onset AF post-KTx and provided prognostic utility. Overall, this easy to obtain tool allows for risk stratification of patients who may benefit from closer monitoring, targeted risk factor modification, and early intervention.

ABSTRACTAimPatients with autosomal dominant polycystic kidney disease (ADPKD) are at increased risk of premature mortality, morbidities and complications, which severely impair quality of life. However, patient‐centered outcomes are not consistently reported in trials in ADPKD, which can limit shared decision‐making. We aimed to identify outcomes important to patients and caregivers and the reasons for their priorities.MethodsNominal group technique was adopted involving patients with ADPKD and caregivers who were purposively selected from eight centres across Australia, France and the Republic of Korea. Participants identified, ranked and discussed outcomes for trials in ADPKD. We calculated an importance score (0–1) for each outcome and conducted thematic analyses.ResultsAcross 17 groups, 154 participants (121 patients, 33 caregivers) aged 19 to 78 (mean 54.5 years) identified 55 outcomes. The 10 highest ranked outcomes were: kidney function (importance score 0.36), end‐stage kidney disease (0.32), survival (0.21), cyst size/growth (0.20), cyst pain/bleeding (0.18), blood pressure (0.17), ability to work (0.16), cerebral aneurysm/stroke (0.14), mobility/physical function (0.12), and fatigue (0.12). Three themes were identified: threatening semblance of normality, inability to control and making sense of diverse risks.ConclusionFor patients with ADPKD and their caregivers, kidney function, delayed progression to end‐stage kidney disease and survival were the highest priorities, and were focused on achieving normality, and maintaining control over health and lifestyle. Implementing these patient‐important outcomes may improve the meaning and relevance of trials to inform clinical care in ADPKD.