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

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.