Department of Nephrology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.
Department of Nephrology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, Jiangsu, China.
J Transl Med. 2023 Jul 31;21(1):517. doi: 10.1186/s12967-023-04387-x.
In patients undergoing percutaneous coronary intervention (PCI), contrast-induced acute kidney injury (CIAKI) is a frequent complication, especially in diabetics, and is connected with severe mortality and morbidity in the short and long term. Therefore, we aimed to develop a CIAKI predictive model for diabetic patients.
3514 patients with diabetes from four hospitals were separated into three cohorts: training, internal validation, and external validation. We developed six machine learning (ML) algorithms models: random forest (RF), gradient-boosted decision trees (GBDT), logistic regression (LR), least absolute shrinkage and selection operator with LR, extreme gradient boosting trees (XGBT), and support vector machine (SVM). The area under the receiver operating characteristic curve (AUC) of ML models was compared to the prior score model, and developed a brief CIAKI prediction model for diabetes (BCPMD). We also validated BCPMD model on the prospective cohort of 172 patients from one of the hospitals. To explain the prediction model, the shapley additive explanations (SHAP) approach was used.
In the six ML models, XGBT performed best in the cohort of internal (AUC: 0.816 (95% CI 0.777-0.853)) and external validation (AUC: 0.816 (95% CI 0.770-0.861)), and we determined the top 15 important predictors in XGBT model as BCPMD model variables. The features of BCPMD included acute coronary syndromes (ACS), urine protein level, diuretics, left ventricular ejection fraction (LVEF) (%), hemoglobin (g/L), congestive heart failure (CHF), stable Angina, uric acid (umol/L), preoperative diastolic blood pressure (DBP) (mmHg), contrast volumes (mL), albumin (g/L), baseline creatinine (umol/L), vessels of coronary artery disease, glucose (mmol/L) and diabetes history (yrs). Then, we validated BCPMD in the cohort of internal validation (AUC: 0.819 (95% CI 0.783-0.855)), the cohort of external validation (AUC: 0.805 (95% CI 0.755-0.850)) and the cohort of prospective validation (AUC: 0.801 (95% CI 0.688-0.887)). SHAP was constructed to provide personalized interpretation for each patient. Our model also has been developed into an online web risk calculator. MissForest was used to handle the missing values of the calculator.
We developed a novel risk calculator for CIAKI in diabetes based on the ML model, which can help clinicians achieve real-time prediction and explainable clinical decisions.
在接受经皮冠状动脉介入治疗（PCI）的患者中，对比剂诱导的急性肾损伤（CIAKI）是一种常见的并发症，尤其是在糖尿病患者中，并且与短期和长期的严重死亡率和发病率有关。因此，我们旨在为糖尿病患者开发一种 CIAKI 预测模型。
来自四家医院的 3514 名糖尿病患者被分为三个队列：训练、内部验证和外部验证。我们开发了六种机器学习（ML）算法模型：随机森林（RF）、梯度提升决策树（GBDT）、逻辑回归（LR）、带有 LR 的最小绝对收缩和选择算子、极端梯度提升树（XGBT）和支持向量机（SVM）。比较了 ML 模型的受试者工作特征曲线下面积（AUC）与先前评分模型，并为糖尿病患者开发了一种简单的 CIAKI 预测模型（BCPMD）。我们还在其中一家医院的前瞻性队列中对 172 名患者的 BCPMD 模型进行了验证。为了解释预测模型，使用了 Shapley 加性解释（SHAP）方法。
在这六种 ML 模型中，XGBT 在内部验证队列（AUC：0.816（95%CI 0.777-0.853））和外部验证队列（AUC：0.816（95%CI 0.770-0.861））中表现最佳，我们确定 XGBT 模型中的前 15 个重要预测因子为 BCPMD 模型变量。BCPMD 的特征包括急性冠状动脉综合征（ACS）、尿蛋白水平、利尿剂、左心室射血分数（LVEF）（%）、血红蛋白（g/L）、充血性心力衰竭（CHF）、稳定型心绞痛、尿酸（umol/L）、术前舒张压（DBP）（mmHg）、造影剂体积（mL）、白蛋白（g/L）、基线肌酐（umol/L）、冠状动脉疾病血管、血糖（mmol/L）和糖尿病病史（yrs）。然后，我们在内部验证队列（AUC：0.819（95%CI 0.783-0.855））、外部验证队列（AUC：0.805（95%CI 0.755-0.850））和前瞻性验证队列（AUC：0.801（95%CI 0.688-0.887））中验证了 BCPMD。使用 SHAP 构建了个性化解释每个患者的模型。我们的模型还已开发为在线网络风险计算器。使用 MissForest 处理计算器的缺失值。
我们基于机器学习模型为糖尿病患者开发了一种新的 CIAKI 风险计算器，可以帮助临床医生实现实时预测和可解释的临床决策。
