Frontiers in digital health · 2026
A two-phase digital tool to predict and prevent post-transplant hyperuricemia.
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Wu Min, Liang Limian, Tian Tian, Chen Chao, Lai Zongqiang, Yu Yunni, Zheng Yourou, Li Wei, Chen Shiyun, He Tian, Qiao Wenmei, Chen Qijun, Zhou Zhenping, Liu Miaona, Sun Haixia
Аннотация
BACKGROUND: Post-transplant hyperuricemia (PTHU) is a frequent and clinically significant metabolic complication among kidney transplant recipients. Current models lack personalized pre-transplant prevention strategies, and few studies define the optimal tacrolimus level for PTHU prevention. To address this unmet need, we developed and evaluated an integrated digital health tool consisting of a pre-operative risk-prediction algorithm and a post-operative tacrolimus dosing management module.
METHOD: We enrolled 195 kidney transplant recipients. Pre-transplantation laboratory assessments and serial tacrolimus blood-concentration (Ctac) measurements longitudinally were conducted, while documenting adverse events. Logistic regression was used to identify the risk factors, and then incorporated into a nomogram. A subsequent dosing optimization algorithm was derived from these results.
RESULTS: We identified four independent risk factors-standard deviation of red blood cell distribution width (RDW-SD), prothrombin time activity (PTA), serum calcium, and Ctac. Notably, the model achieved strong discriminative performance, with AUROCs of 0.802 (95% CI 0.723-0.881, p < 0.001) pre-operatively and 0.824 (95% CI 0.734-0.913, p < 0.001) post-operatively. Using an optimal cutoff of 0.817, the dosing algorithm calculates an individualized maximum safe tacrolimus concentration for each patient.
CONCLUSION: This study provides a pre-operative risk prediction model and a post-operative dosing tool that, used in tandem, can aid in the prevention of PTHU and the protection of renal allograft function.
METHOD: We enrolled 195 kidney transplant recipients. Pre-transplantation laboratory assessments and serial tacrolimus blood-concentration (Ctac) measurements longitudinally were conducted, while documenting adverse events. Logistic regression was used to identify the risk factors, and then incorporated into a nomogram. A subsequent dosing optimization algorithm was derived from these results.
RESULTS: We identified four independent risk factors-standard deviation of red blood cell distribution width (RDW-SD), prothrombin time activity (PTA), serum calcium, and Ctac. Notably, the model achieved strong discriminative performance, with AUROCs of 0.802 (95% CI 0.723-0.881, p < 0.001) pre-operatively and 0.824 (95% CI 0.734-0.913, p < 0.001) post-operatively. Using an optimal cutoff of 0.817, the dosing algorithm calculates an individualized maximum safe tacrolimus concentration for each patient.
CONCLUSION: This study provides a pre-operative risk prediction model and a post-operative dosing tool that, used in tandem, can aid in the prevention of PTHU and the protection of renal allograft function.