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Background <p>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).</p> Methods <p>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.</p> Results <p>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, <i>Pseudomonas aeruginosa</i>,&#xa0;<i>Staphylococcus hominis</i>, <i>Escherichia coli</i>, and <i>Acinetobacter baumannii&#xa0;</i>predominated in sputum, blood, urine, and lavage fluid, respectively. In terms of overall detection counts, the most frequently isolated strains were&#xa0;<i>P. aeruginosa</i>,&#xa0;<i>Klebsiella pneumoniae</i>, and <i>A. baumannii</i>. The drug resistance rate of <i>P. aeruginosa</i>&#xa0;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%.&#xa0;Although amikacin shows 100% sensitivity, combination with β-lactam antibiotics is recommended to reduce mortality. <i>K. pneumoniae</i> 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. <i>A. baumannii</i> 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.</p>

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Hospital acquired drug resistant pathogens infections in patients with viral respiratory tract infections: a retrospective study

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  • 发表日期：

    2025年8月25日

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  • Zibo Fan,
  • Xinmin Xu,
  • Qun Li,
  • Tong Zhou,
  • Aibin Wang,
  • Chengjie Ma,
  • Zhihai Chen,
  • Lianhe Lu,
  • Yuanyuan Zhang,
  • Yajie Wang,
  • Wei Zhang

摘要

Background

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).

Methods

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.

Results

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.