Liu Bo, Wu Di, Jiang Yong'An, Liu Hua
Department of Neurosurgery, Second People's Hospital of Hunan Province, Changsha, Hunan, P. R. China.
Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, P. R. China.
Cell Biochem Biophys. 2025 May 21. doi: 10.1007/s12013-025-01771-w.
Intracranial hemorrhage (ICH) is a critical and urgent condition in clinical practice. Recent research has highlighted acute kidney injury (AKI) that frequently impacts patient prognosis. For clinicians, early intervention is crucial, and the advancement of machine learning brings promising prospects for predicting this disease. Therefore, this study aims to develop innovative machine learning models for the prediction and diagnosis of acute kidney injury (AKI) in patients with intracerebral hemorrhage (ICH). AKI data of ICH patients were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. To construct the models, we utilized various techniques including random survival forest (RSF), elastic network (Enet), Least Absolute Shrinkage and Selection Operator (Lasso), stepwise logistic regression (stepwise LR), and ten machine learning algorithms. Optimal parameters were obtained through a ten-fold crossover, and training and testing groups were employed for the integrated machine models' training and validation. We conducted a quantitative evaluation of the model's performance and assessed its clinical application to determine its advantages. Furthermore, we compared the base model with traditional models such as the Sequential Organ Failure Assessment (SOFA) and the bespoke Simplified Acute Physiology Score (SAPS) II model. A total of 1856 patients with intracerebral hemorrhage (ICH) were enrolled in the study, consisting of 1633 non-AKI patients and 223 AKI patients. Among the various machine learning models tested, XGBoost exhibited the highest predictive accuracy and demonstrated excellent clinical applicability as a standalone model. When combining integrated models, RSF+XGBoost, LR[forward]+Lasso, LR[forward]+RSF, and Lasso+XGBoost, all achieved the highest AUC values (AUC = 1.000). Machine learning models can serve as valuable diagnostic tools in identifying the occurrence of acute kidney injury (AKI) in intracerebral hemorrhage (ICH) cases. Whether utilized individually or in combination, these models have the potential to assist clinicians in proactively developing effective interventions.
颅内出血（ICH）是临床实践中的一种危急重症。近期研究强调了急性肾损伤（AKI）对患者预后的频繁影响。对于临床医生而言，早期干预至关重要，而机器学习的发展为预测该疾病带来了广阔前景。因此，本研究旨在开发创新的机器学习模型，用于预测和诊断脑出血（ICH）患者的急性肾损伤（AKI）。脑出血患者的急性肾损伤数据从重症监护医学信息集市IV（MIMIC-IV）数据库中提取。为构建模型，我们运用了多种技术，包括随机生存森林（RSF）、弹性网络（Enet）、最小绝对收缩和选择算子（Lasso）、逐步逻辑回归（逐步LR）以及十种机器学习算法。通过十折交叉验证获得最优参数，并使用训练组和测试组对集成机器模型进行训练和验证。我们对模型性能进行了定量评估，并评估其临床应用以确定其优势。此外，我们将基础模型与传统模型如序贯器官衰竭评估（SOFA）和定制的简化急性生理学评分（SAPS）II模型进行了比较。本研究共纳入1856例脑出血（ICH）患者，其中1633例为非急性肾损伤患者，223例为急性肾损伤患者。在测试的各种机器学习模型中，XGBoost表现出最高的预测准确性，并作为独立模型展现出出色的临床适用性。当组合集成模型时，RSF+XGBoost、LR[前向]+Lasso、LR[前向]+RSF和Lasso+XGBoost均达到了最高的AUC值（AUC = 1.000）。机器学习模型可作为有价值的诊断工具，用于识别脑出血（ICH）病例中急性肾损伤（AKI）的发生。无论单独使用还是联合使用，这些模型都有可能帮助临床医生提前制定有效的干预措施。
