Ma Xuejie, Mai Yaoqiong, Ma Yin, Ma Xiaowei
Intensive Care Unit, Cardiocerebral Vascular Disease Hospital, General Hospital of Ningxia Medical University, Yinchuan, 750003, Ningxia Hui Autonomous Region, China.
General Hospital of Ningxia Medical University (First Clinical Medical College), Yinchuan, 750003, Ningxia Hui Autonomous Region, China.
Sci Rep. 2025 Mar 27;15(1):10580. doi: 10.1038/s41598-025-95604-8.
Sepsis is a serious threat to human life. Early prediction of high-risk populations for sepsis is necessary especially in elderly patients. Artificial intelligence shows benefits in early warning. The aim of the study was to construct an early machine warning model for elderly sepsis patients and evaluate its performance. We collected elderly patients from General Hospital of Ningxia Medical University emergency department and intensive care unit from 01 January 2021 to 01 August 2023. The clinical data was divided into a training set and a test set. A total of 2976 patients and 12 features were screened. We used 8 machine learning models to build the warning model. In conclusion, we developed a model based on XGBoost with an AUROC of 0.971, AUPRC of 0.862, accuracy of 0.95, specificity of 0.964 and F1 score of 0.776. Of all the features, baseline APTT played the most important role, followed by baseline lymphocyte count. Higher level of baseline APTT and lower level of baseline lymphocyte count may indicate higher risk of sepsis occurrence. We developed a high-performance early warning model for sepsis in old age based on machine learning in order to facilitate early treatment but also need further external validation.
脓毒症是对人类生命的严重威胁。尤其是对老年患者而言，早期预测脓毒症的高危人群很有必要。人工智能在早期预警方面显示出优势。本研究的目的是构建老年脓毒症患者的早期机器预警模型并评估其性能。我们收集了宁夏医科大学总医院急诊科和重症监护病房在2021年1月1日至2023年8月1日期间的老年患者。临床数据被分为训练集和测试集。共筛选出2976例患者和12项特征。我们使用8种机器学习模型构建预警模型。总之，我们开发了一种基于XGBoost的模型，其曲线下面积（AUROC）为0.971，精确率-召回率曲线下面积（AUPRC）为0.862，准确率为0.95，特异性为0.964，F1分数为0.776。在所有特征中，基线活化部分凝血活酶时间（APTT）起最重要作用，其次是基线淋巴细胞计数。较高的基线APTT水平和较低的基线淋巴细胞计数可能表明脓毒症发生风险较高。我们基于机器学习开发了一种针对老年脓毒症的高性能早期预警模型，以便于早期治疗，但还需要进一步的外部验证。
