Wang Ya, Zhang Jiajia, Chen Xiaoyan, Sun Min, Li Yanqing, Wang Yanan, Gu Yan, Cai Yinyin
Neurosurgical Intensive Care Unit, Department of Neurosurgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, People's Republic of China.
Neurosurgery Section Two, Department of Neurosurgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, People's Republic of China.
Infect Drug Resist. 2023 Oct 9;16:6603-6615. doi: 10.2147/IDR.S411976. eCollection 2023.
To develop a predictive model for assessing the risk of multidrug-resistant organisms (MDROs) infection and validate its effectiveness.
We conducted a study on a total of 2516 patients admitted to the neurosurgery intensive care unit (NICU) of a Grade-III hospital in Nantong City, Jiangsu Province, China, between January 2014 and February 2022. Patients meeting the inclusion criteria were selected using convenience sampling. The patients were randomly divided into modeling and validation groups in a 7:3 ratio. To address the category imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE) to adjust the MDROs infection ratio from 203:1558 to 812:609 in the training set. Univariate analysis and logistic regression analysis were performed to identify risk factors associated with MDROs infection in the NICU. A risk prediction model was developed, and a nomogram was created. Receiver operating characteristic (ROC) analysis was used to assess the predictive performance of the model.
Logistic regression analysis revealed that sex, hospitalization time, febrile time, invasive operations, postoperative prophylactic use of antibiotics, mechanical ventilator time, central venous catheter indwelling time, urethral catheter indwelling time, ALB, PLT, WBC, and L% were independent predictors of MDROs infection in the NICU. The area under the ROC curve for the training set and validation set were 0.880 (95% CI: 0.857-0.904) and 0.831 (95% CI: 0.786-0.876), respectively. The model's prediction curve closely matched the ideal curve, indicating excellent predictive performance.
The prediction model developed in this study demonstrates good accuracy in assessing the risk of MDROs infection. It serves as a valuable tool for neurosurgical intensive care practitioners, providing an objective means to effectively evaluate and target the risk of MDROs infection.
建立一种用于评估多重耐药菌（MDROs）感染风险的预测模型并验证其有效性。
2014年1月至2022年2月期间，我们对江苏省南通市某三级医院神经外科重症监护病房（NICU）收治的2516例患者进行了一项研究。采用方便抽样法选取符合纳入标准的患者。患者按7:3的比例随机分为建模组和验证组。为解决类别不平衡问题，我们采用合成少数过采样技术（SMOTE）将训练集中MDROs感染率从203:1558调整至812:609。进行单因素分析和逻辑回归分析以确定NICU中与MDROs感染相关的危险因素。建立了风险预测模型并绘制了列线图。采用受试者工作特征（ROC）分析评估模型的预测性能。
逻辑回归分析显示，性别、住院时间、发热时间、侵入性操作、术后预防性使用抗生素、机械通气时间、中心静脉导管留置时间、尿道导管留置时间、白蛋白（ALB）、血小板（PLT）、白细胞（WBC）和淋巴细胞百分比（L%）是NICU中MDROs感染的独立预测因素。训练集和验证集的ROC曲线下面积分别为0.880（95%可信区间：0.857 - 0.904）和0.831（95%可信区间：0.786 - 0.876）。模型的预测曲线与理想曲线密切匹配，表明具有良好的预测性能。
本研究建立的预测模型在评估MDROs感染风险方面具有良好的准确性。它是神经外科重症监护从业者的宝贵工具，为有效评估和针对MDROs感染风险提供了一种客观手段。
