Fan Zibo, Xu Xinmin, Li Qun, Zhou Tong, Wang Aibin, Ma Chengjie, Chen Zhihai, Lu Lianhe, Zhang Yuanyuan, Wang Yajie, Zhang Wei
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, No. 8, Jingshun East Street, Chaoyang District, Beijing, 100015, China.
BMC Infect Dis. 2025 Aug 25;25(1):1067. doi: 10.1186/s12879-025-11455-8.
Viral respiratory infections (VRTIs) caused by influenza (Flu) and COVID-19 pose significant global health challenges. Clinical outcomes are further exacerbated by infections with hospital acquired drug resistant pathogens (DRPs).
A retrospective analysis was conducted on the data of 1,051 hospitalized patients with VRTIs from 2018 to 2024 at Beijing Ditan Hospital. Firstly, 280 drug-resistant strains were isolated from 185 patients with hospital-acquired DRPs infections for extended antibiogram analysis. Secondly, Interpretable machine learning (ML) was employed to predict the risk factors for hospital acquired DRPs infections in patients with VRTIs. Using the optimal feature subset, seven ML prediction models were developed. Parameter tuning was performed via 10-fold cross-validation and grid search. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, precision, and F1 score. SHAP (SHapley Additive exPlanations) was used to interpret the optimal model.
Pathogen distribution in 280 clinical samples revealed sputum (65.36%) as themainsource, followed byblood (15.36%), urine (11.43%), and lavage fluid (5.00%). In all clinical specimens, Pseudomonas aeruginosa, Staphylococcus hominis, Escherichia coli, and Acinetobacter baumannii predominated in sputum, blood, urine, and lavage fluid, respectively. In terms of overall detection counts, the most frequently isolated strains were P. aeruginosa, Klebsiella pneumoniae, and A. baumannii. The drug resistance rate of P. aeruginosa to third-generation cephalosporins (such as ceftriaxone and cefotaxime) exceeds 89%, but it has relatively higher sensitivity to ceftazidime (71.7%) and cefepime (69.6%). Its drug resistance rates to imipenem and meropenem reach 45.7%. Although amikacin shows 100% sensitivity, combination with β-lactam antibiotics is recommended to reduce mortality. K. pneumoniae shows resistance rates of 53.3% to imipenem and 46.7% to meropenem, with over 50% resistance to levofloxacin and ciprofloxacin. Effective agents include sulfamethoxazole (68.9% susceptible), tigecycline (64.4%), chloramphenicol (62.2%), and amikacin (62.2%). Tigecycline combined with aminoglycosides has synergistic effects and inhibits resistant strains. A. baumannii was highly resistant to nearly all tested antibiotics, showing only partial susceptibility to minocycline (59.5%) and trimethoprim-sulfamethoxazole (38.1%). Among the seven ML models, the neural network (NN) achieved the best predictive performance. The SHAP method revealed the top 15 predictive variables by importance ranking, including length of stay (LOS), cholinesterase (CHE), age, albumin (ALB), etc.
由流感（Flu）和新冠病毒（COVID - 19）引起的病毒性呼吸道感染（VRTIs）给全球健康带来了重大挑战。医院获得性耐药病原体（DRPs）感染进一步加剧了临床后果。
对2018年至2024年在北京地坛医院住院的1051例VRTIs患者的数据进行回顾性分析。首先，从185例医院获得性DRPs感染患者中分离出280株耐药菌株进行扩展抗菌谱分析。其次，采用可解释机器学习（ML）来预测VRTIs患者医院获得性DRPs感染的危险因素。使用最优特征子集，开发了七个ML预测模型。通过10折交叉验证和网格搜索进行参数调整。使用曲线下面积（AUC）、敏感性、特异性、精度和F1分数评估模型性能。使用SHAP（Shapley值相加解释）来解释最优模型。
280份临床样本中的病原体分布显示，痰液（65.36%）是主要来源，其次是血液（15.36%）、尿液（11.43%）和灌洗液（5.00%）。在所有临床标本中，铜绿假单胞菌、人葡萄球菌、大肠杆菌和鲍曼不动杆菌分别在痰液、血液、尿液和灌洗液中占主导地位。就总体检测数量而言，最常分离出的菌株是铜绿假单胞菌、肺炎克雷伯菌和鲍曼不动杆菌。铜绿假单胞菌对第三代头孢菌素（如头孢曲松和头孢噻肟）的耐药率超过89%，但对头孢他啶（71.7%）和头孢吡肟（69.6%）相对敏感。其对亚胺培南和美罗培南的耐药率达到45.7%。虽然阿米卡星显示出100%的敏感性，但建议与β - 内酰胺类抗生素联合使用以降低死亡率。肺炎克雷伯菌对亚胺培南的耐药率为53.3%，对美罗培南的耐药率为46.7%，对左氧氟沙星和环丙沙星的耐药率超过50%。有效药物包括磺胺甲恶唑（68.9%敏感）、替加环素（64.4%）、氯霉素（62.2%）和阿米卡星（62.2%）。替加环素与氨基糖苷类联合具有协同作用并抑制耐药菌株。鲍曼不动杆菌对几乎所有测试抗生素都具有高度耐药性，仅对米诺环素（59.5%）和复方新诺明（38.1%）有部分敏感性。在七个ML模型中，神经网络（NN）实现了最佳预测性能。SHAP方法按重要性排名揭示了前15个预测变量，包括住院时间（LOS）、胆碱酯酶（CHE）、年龄、白蛋白（ALB）等。
