Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehakro, Jongno-gu, Seoul, 03080, South Korea.
Department of Internal Medicine, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu-si, Gyeonggi-do, South Korea.
Sci Rep. 2021 Aug 3;11(1):15704. doi: 10.1038/s41598-021-95019-1.
The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783-0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.
肾细胞癌（RCC）肾切除术后急性肾损伤（AKI）的精确预测是一个重要问题，因为它与随后的肾功能障碍和高死亡率有关。在此，我们研究了机器学习（ML）算法是否比传统的逻辑回归（LR）模型更能预测术后 AKI 风险。回顾了 2003 年 1 月至 2017 年 12 月期间接受单侧肾切除术的 4104 例 RCC 患者。开发了支持向量机、随机森林、极端梯度增强和 LightGBM 等 ML 模型，并比较了基于 AUC、准确性和 F1 评分的性能与基于 LR 的评分模型。术后 AKI 发生在 1167 例患者（28.4%）中。所有 ML 模型的性能指标值均高于基于 LR 的评分模型。其中，LightGBM 模型的 AUC 值最高，为 0.810（0.783-0.837）。决策曲线分析表明，在所有阈值概率范围内，ML 模型比基于 LR 的评分模型具有更大的净获益。ML 算法的应用提高了 RCC 肾切除术后 AKI 的预测能力，这些模型的性能优于传统的基于 LR 的模型。
