Department of Ultrasound, Meng Cheng County Hospital of Chinese Medicine, Bozhou City, Anhui Province, China.
J Diabetes Investig. 2024 Nov;15(11):1637-1650. doi: 10.1111/jdi.14294. Epub 2024 Aug 22.
The risk of cardiovascular complications is significantly elevated in patients with diabetic kidney disease (DKD). Recognizing the link between the progression of DKD and an increased risk of cardiovascular disease (CVD), it is crucial to focus on the early prediction and management of CVD risk factors among these patients to potentially enhance their health outcomes.
This study sought to bridge the existing gap by developing and validating machine learning (ML) models that utilize clinical data and shear wave elastography (SWE) radiomics features to identify patients at risk of CVD, ultimately aiming to improve the management of DKD.
This study conducted a retrospective analysis of 586 patients with DKD, dividing them into training and external validation cohorts. We categorized patients based on the presence or absence of CVD. Utilizing SWE imaging, we extracted and standardized radiomics features to develop multiple ML models. These models underwent internal validation using radiomics features alone, clinical data, or a combination thereof. The optimal model was then identified, and its feature importance was assessed through the Shapley Additive Explanations (SHAP) method, before proceeding to external validation.
Among the 586 patients analyzed, 30.7% (180/586) were identified as at risk for CVD. The study pinpointed six significant radiomics features related to CVD, alongside six critical pieces of clinical data. The Support Vector Machine (SVM) model outperformed others in both internal and external validations. Further, SHAP analysis highlighted five principal determinants of CVD risk, comprising three clinical indicators and two SWE radiomics features.
This study highlights the effectiveness of an SVM model that combines clinical and radiomics features in predicting CVD risk among DKD patients. It enables early prediction of CVD in this patient group, thereby supporting the implementation of timely and suitable interventions.
患有糖尿病肾病（DKD）的患者发生心血管并发症的风险显著增加。鉴于 DKD 的进展与心血管疾病（CVD）风险增加之间存在关联，因此，关注这些患者 CVD 风险因素的早期预测和管理对于改善其健康结局至关重要。
本研究旨在通过开发和验证机器学习（ML）模型来弥补这一空白，这些模型利用临床数据和剪切波弹性成像（SWE）放射组学特征来识别发生 CVD 的风险患者，最终目标是改善 DKD 的管理。
本研究对 586 例 DKD 患者进行了回顾性分析，将其分为训练集和外部验证集。我们根据患者是否存在 CVD 进行分类。使用 SWE 成像，我们提取和标准化了放射组学特征，以开发多个 ML 模型。这些模型仅使用放射组学特征、临床数据或两者的组合进行内部验证。然后确定最佳模型，并通过 Shapley 加性解释（SHAP）方法评估其特征重要性，之后再进行外部验证。
在所分析的 586 例患者中，30.7%（180/586）被确定为 CVD 风险患者。研究确定了与 CVD 相关的 6 个显著放射组学特征和 6 个关键临床数据。支持向量机（SVM）模型在内部和外部验证中均优于其他模型。此外，SHAP 分析突出了 CVD 风险的五个主要决定因素，包括三个临床指标和两个 SWE 放射组学特征。
本研究强调了 SVM 模型在预测 DKD 患者 CVD 风险中的有效性，该模型结合了临床和放射组学特征。它可以早期预测该患者群体的 CVD，从而支持及时和适当的干预措施的实施。
