ics linked to increased risk are identified. METHODS: This study was registered at ClinicalTrials.gov (NCT05466370) and was conducted after local ethics committee approval. We evaluated 3366 transfusion episodes from a university hospital between October 31, 2016, and August 31, 2020. Random forest models were tuned and trained via Python auto-sklearn package to predict acute kidney injury (AKI). The models included recipients’ and donors’ demographic parameters and laboratory values, donor questionnaire results, and the age of the pRBCs. Bootstrapping on the test dataset was used to calculate the means and standard deviations of various performance metrics. RESULTS: AKI as defined by a modified Kidney Disease Improving Global Outcomes (KDIGO) criterion developed after 17.4% transfusion episodes (base rate). AKI could be predicted with an area under the curve of the receiver operating characteristic (AUC-ROC) of 0.73 ± 0.02. The negative (NPV) and positive (PPV) predictive values were 0.90 ± 0.02 and 0.32 ± 0.03, respectively. Feature importance and relative risk analyses revealed that donor features were far less important than recipient features for predicting posttransfusion AKI. CONCLUSIONS: Surprisingly, only the recipients’ characteristics played a decisive role in AKI prediction. Based on this result, we speculate that the selection of a specific pRBC may have less influence than recipient characteristics....

中文翻译：

---

输血后肾功能恶化的潜在预测因素

确定了与风险增加相关的 ICS。方法：本研究在 ClinicalTrials.gov (NCT05466370) 上注册，并在当地伦理委员会批准后进行。我们评估了 2016 年 10 月 31 日至 2020 年 8 月 31 日期间来自某大学医院的 3366 次输血事件。通过 Python auto-sklearn 包调整和训练随机森林模型，以预测急性肾损伤 (AKI)。这些模型包括接受者和捐赠者的人口统计参数和实验室值、捐赠者问卷结果以及 pRBC 的年龄。使用测试数据集上的引导来计算各种性能指标的平均值和标准差。结果：根据改良的肾脏疾病改善全球结果 (KDIGO) 标准定义的 AKI 在 17.4% 的输血事件（基本率）后发生。AKI 可以通过受试者工作特征 (AUC-ROC) 曲线下面积 0.73 ± 0.02 进行预测。阴性 (NPV) 和阳性 (PPV) 预测值分别为 0.90 ± 0.02 和 0.32 ± 0.03。特征重要性和相对风险分析表明，对于预测输血后 AKI，供体特征远不如受者特征重要。结论：令人惊讶的是，只有接受者的特征在 AKI 预测中起决定性作用。基于这一结果，我们推测特定 pRBC 的选择可能比受体特征的影响更小......