Nephrology Department, Xiangya Hospital, Central South University, Changsha, China.
Hematology Department, The Third Xiangya Hospital, Central South University, Changsha, China.
Kidney Blood Press Res. 2017;42(6):1045-1052. doi: 10.1159/000485592. Epub 2017 Dec 4.
BACKGROUND/AIMS: Renal biopsy is the gold standard to determine the pathologic type of primary nephrotic syndrome, which is critical for diagnosis, choice of treatment and evaluation of prognosis. However, in some cases, renal biopsy cannot be performed.
To explore the possibility of predicting the histology type of primary nephrotic syndrome without the need for biopsy, we trained and validated a machine learning algorithm using data from 222 patients with biopsy-confirmed primary nephrotic syndrome treated at our hospital between May 2008 and January 2016. The model was then tested prospectively on another sample of 63 patients with biopsy-confirmed primary nephrotic syndrome.
Overall accuracy of prediction from the retrospective set of 222 patients was 62.2% across all types of nephrotic syndrome. The accuracy of model prediction for the prospectively collected dataset of 63 patients was 61.9%. The algorithm identified 17 of 33 variables as contributing strongly to type of renal pathology.
To our knowledge, this is the first such application of machine learning to predict the pathologic type of primary nephrotic syndrome, which may be clinically useful by itself as well as helpful for guiding future efforts at machine learning-based prediction in other disease contexts.
背景/目的：肾活检是确定原发性肾病综合征病理类型的金标准，对于诊断、治疗选择和预后评估至关重要。然而，在某些情况下，无法进行肾活检。
为了探索在无需活检的情况下预测原发性肾病综合征组织学类型的可能性，我们使用 2008 年 5 月至 2016 年 1 月期间在我院接受活检证实的原发性肾病综合征治疗的 222 例患者的数据，训练并验证了一种机器学习算法。然后，我们在另一组 63 例经活检证实的原发性肾病综合征患者中前瞻性地测试了该模型。
在所有类型的肾病综合征中，对 222 例回顾性患者数据集的预测总体准确率为 62.2%。该算法对前瞻性收集的 63 例患者数据集的预测准确率为 61.9%。该算法确定了 17 个变量作为对肾脏病理类型有重要贡献的变量。
据我们所知，这是首次将机器学习应用于预测原发性肾病综合征的病理类型，该算法本身可能具有临床应用价值，也有助于指导未来在其他疾病背景下基于机器学习的预测。
