Department of Health Technology and Informatics, Hong Kong Polytechnic University, Kowloon, Hong Kong.
Department of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
Acad Radiol. 2023 Sep;30 Suppl 1:S295-S304. doi: 10.1016/j.acra.2023.02.018. Epub 2023 Mar 25.
Accurate identification of risk information about fibrosis severity is crucial for clinical decision-making and clinical management of patients with chronic kidney disease (CKD). This study aimed to develop an ultrasound (US)-derived computer-aided diagnosis tool for identifying CKD patients at high risk of developing moderate-severe renal fibrosis, in order to optimize treatment regimens and follow-up strategies.
A total of 162 CKD patients undergoing renal biopsies and US examinations were prospectively enrolled and randomly divided into training (n = 114) and validation (n = 48) cohorts. A multivariate logistic regression approach was employed to develop the diagnostic tool named S-CKD for differentiating moderate-severe renal fibrosis from mild one in the training cohort by integrating the significant variables, which were screened out from demographic characteristics and conventional US features via the least absolute shrinkage and selection operator regression algorithm. The S-CKD was then deployed as both an online web-based and an offline document-based, easy-to-use auxiliary device. In both the training and validation cohorts, the S-CKD's diagnostic performance was evaluated through discrimination and calibration. The clinical benefit of using S-CKD was revealed by decision curve analysis (DCA) and clinical impact curves.
The proposed S-CKD achieved an area under the receiver operating characteristic curve of 0.84 (95% confidence interval (CI): 0.77-0.91) and 0.81 (95% CI: 0.68-0.94) in the training and validation cohorts, respectively, indicating satisfactory diagnosis performance. Results of the calibration curves showed that S-CKD has excellent predictive accuracy (Hosmer-Lemeshow test: training cohort, p = 0.497; validation cohort, p = 0.205). The DCA and clinical impact curves exhibited a high clinical application value of the S-CKD at a wide range of risk probabilities.
The S-CKD tool developed in this study is capable of discriminating between mild and moderate-severe renal fibrosis in patients with CKD and achieving promising clinical benefits, which may aid clinicians in personalizing medical decision-making and follow-up arrangement.
准确识别纤维化严重程度的风险信息对慢性肾脏病（CKD）患者的临床决策和临床管理至关重要。本研究旨在开发一种基于超声（US）的计算机辅助诊断工具，用于识别发生中重度肾纤维化风险较高的 CKD 患者，以优化治疗方案和随访策略。
前瞻性纳入并随机分配 162 例接受肾活检和 US 检查的 CKD 患者至训练队列（n=114）和验证队列（n=48）。采用多变量逻辑回归方法，通过最小绝对收缩和选择算子回归算法，筛选出人口统计学特征和常规 US 特征中的显著变量，建立了名为 S-CKD 的诊断工具，用于区分训练队列中中重度与轻度肾纤维化。然后，S-CKD 被开发为在线网络和离线文档两种易于使用的辅助工具。在训练和验证队列中，通过判别和校准评估 S-CKD 的诊断性能。通过决策曲线分析（DCA）和临床影响曲线揭示 S-CKD 的临床获益。
所提出的 S-CKD 在训练和验证队列中的受试者工作特征曲线下面积分别为 0.84（95%置信区间（CI）：0.77-0.91）和 0.81（95% CI：0.68-0.94），表明具有良好的诊断性能。校准曲线结果表明，S-CKD 具有出色的预测准确性（Hosmer-Lemeshow 检验：训练队列，p=0.497；验证队列，p=0.205）。DCA 和临床影响曲线表明，S-CKD 在广泛的风险概率范围内具有较高的临床应用价值。
本研究开发的 S-CKD 工具能够区分 CKD 患者的轻度和中重度肾纤维化，并具有良好的临床获益，可能有助于临床医生制定个性化的医疗决策和随访安排。
