Christiadi Daniel, Chai Kevin, Chuah Aaron, Loong Bronwyn, Andrews Thomas D, Chakera Aron, Walters Giles Desmond, Jiang Simon Hee-Tang
Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.
Department of Renal Medicine, The Canberra Hospital, Garran, ACT, Australia.
Front Med (Lausanne). 2024 Dec 11;11:1428073. doi: 10.3389/fmed.2024.1428073. eCollection 2024.
A static predictive model relying solely on baseline clinicopathological data cannot capture the heterogeneity in predictor trajectories observed in the progression of chronic kidney disease (CKD). To address this, we developed and validated a dynamic survival prediction model using longitudinal clinicopathological data to predict end-stage kidney disease (ESKD), with death as a competing risk.
We trained a sequence of random survival forests using a landmarking approach and optimized the model with a pre-specified prediction horizon of 5 years. The predicted cumulative incidence function (CIF) values were used to generate a personalized dynamic prediction plot.
The model was developed using baseline demographics and 13 longitudinal clinicopathological variables from 4,950 patients. Variable importance analysis for ESKD and death informed the creation of a sequence of reduced models that utilized six key variables: age, serum albumin, bicarbonate, chloride, eGFR, and hemoglobin. The models demonstrated robust predictive performance, with a median concordance index of 84.84% for ESKD and 84.1% for death. The median integrated Brier scores were 0.03 for ESKD and 0.038 for death across all landmark times. External validation with 8,729 patients confirmed these results.
We successfully developed and validated a dynamic survival prediction model using common longitudinal clinicopathological data. This model predicts ESKD with death as a competing risk and aims to assist clinicians in dialysis planning for patients with CKD.
仅依赖基线临床病理数据的静态预测模型无法捕捉慢性肾脏病（CKD）进展过程中预测指标轨迹的异质性。为解决这一问题，我们开发并验证了一种动态生存预测模型，该模型使用纵向临床病理数据来预测终末期肾病（ESKD），并将死亡作为竞争风险。
我们使用地标法训练了一系列随机生存森林模型，并将预测期预先设定为5年对模型进行优化。预测的累积发病率函数（CIF）值用于生成个性化动态预测图。
该模型是利用4950例患者的基线人口统计学数据和13个纵向临床病理变量开发的。对ESKD和死亡的变量重要性分析为一系列简化模型的创建提供了依据，这些模型使用了六个关键变量：年龄、血清白蛋白、碳酸氢盐、氯、估算肾小球滤过率（eGFR）和血红蛋白。这些模型表现出强大的预测性能，ESKD的中位数一致性指数为84.84%，死亡的中位数一致性指数为84.1%。在所有地标时间点，ESKD的中位数综合Brier评分为0.03，死亡的中位数综合Brier评分为0.038。对8729例患者的外部验证证实了这些结果。
我们成功开发并验证了一种使用常见纵向临床病理数据的动态生存预测模型。该模型以死亡为竞争风险预测ESKD，旨在协助临床医生为CKD患者制定透析计划。
