Kwon Soie, Park Sehoon, Yang Sunah, Shin Chaiho, Lee Jeonghwan, Ryu Jiwon, Kim Sejoong, Cho Jeong Min, Yoon Hyung-Jin, Kim Dong Ki, Joo Kwon Wook, Kim Yon Su, Park Minsu, Kim Kwangsoo, Lee Hajeong
Department of Internal Medicine, Chung-Ang University Seoul Hospital, Seoul, Republic of Korea.
Department of Internal Medicine, Chung-Ang University, Seoul, Republic of Korea.
Clin Kidney J. 2025 Feb 17;18(5):sfaf045. doi: 10.1093/ckj/sfaf045. eCollection 2025 May.
Postoperative acute kidney injury (PO-AKI) is a critical complication of adverse kidney outcomes, both short and long-term. We aimed to expand our pre-existing PO-AKI prediction model to predict mid-to long-term adverse kidney outcomes.
We included patients who underwent major non-cardiac surgeries from the original SPARK cohort, two external validation cohorts, and a temporal validation cohort. Mid-to-long-term adverse kidney outcomes were defined as end-stage kidney disease progression or death within 1 or 3 years after surgery. We verified and tuned the original Simple Postoperative AKI RisK (SPARK) model to predict mid-to-long-term adverse kidney events.
We included 33 636 patients in development, 71 232 patients in external validation, and 33 944 patients in temporal validation cohorts, respectively. One- and 3-year adverse kidney events occurred in 5.5% and 13.2% in the development cohort, respectively. The original SPARK score demonstrated an acceptable discriminative power for 1-year and 3-year adverse outcome risks with C indices mostly >0.7. However, the power was relatively poor when restricted to high-risk patients or those who actually developed PO-AKI. When we re-calculated the regression coefficients from a Cox model, the discriminative performances were better, especially for those with high-risk characteristics (e.g. 1-year outcome, C-index 0.72 in developmental and 0.73‒0.77 in validation datasets). Furthermore, when the model integrated the PO-AKI stage and history of malignancy with the SPARK variables, the performance was significantly enhanced (1-year, C-index 0.86 in development and 0.86‒0.88 in validation results). With the above findings, we constructed an online postoperative risk prediction system (https://snuhnephrology.github.io/postop/).
The addition of two clinical factors and recalibration of SPARK variables significantly improved mid-to-long-term postoperative risk prediction for mortality or dialysis after non-cardiac surgery. Our calculator helps clinicians easily predict a mid-to-long-term risk and PO-AKI occurrence by entering a few variables.
术后急性肾损伤（PO-AKI）是一种严重的并发症，会导致短期和长期的不良肾脏结局。我们旨在扩展我们原有的PO-AKI预测模型，以预测中到长期的不良肾脏结局。
我们纳入了来自原始SPARK队列、两个外部验证队列和一个时间验证队列的接受重大非心脏手术的患者。中到长期不良肾脏结局定义为术后1年或3年内终末期肾病进展或死亡。我们验证并调整了原有的简易术后急性肾损伤风险（SPARK）模型，以预测中到长期不良肾脏事件。
我们分别在开发队列中纳入了33636例患者，在外部验证队列中纳入了71232例患者，在时间验证队列中纳入了33944例患者。在开发队列中，1年和3年的不良肾脏事件发生率分别为5.5%和13.2%。原有的SPARK评分对1年和3年不良结局风险显示出可接受的区分能力，C指数大多>0.7。然而，当仅限于高危患者或实际发生PO-AKI的患者时，其区分能力相对较差。当我们从Cox模型重新计算回归系数时，区分性能更好，尤其是对于具有高危特征的患者（例如，1年结局，开发数据集中的C指数为0.72，验证数据集中为0.73-0.77）。此外，当模型将PO-AKI分期和恶性肿瘤病史与SPARK变量相结合时，性能显著提高（1年，开发数据集中的C指数为0.86，验证结果中为0.86-0.88）。基于上述发现，我们构建了一个在线术后风险预测系统（https://snuhnephrology.github.io/postop/）。
增加两个临床因素并重新校准SPARK变量显著改善了非心脏手术后中到长期死亡或透析的术后风险预测。我们的计算器通过输入几个变量，帮助临床医生轻松预测中到长期风险和PO-AKI的发生。
