Epidemiological Investigation and Clinical Characteristics Study of Orthopedic Infections Caused by Aeromonas hydrophila in the Southeast Coastal Region from 2018 to 2024

Objective To investigate the clinical-epidemiological characteristics of trauma-related orthopedic Aeromonas infections and develop a risk prediction model to guide early diagnosis and clinical management. Methods The data were derived from traumatic Aeromonas infection in Fuzhou Second General Hospital from May 2018 to May 2024. The culture results and drug sensitivity data of inpatients were collected, and a retrospective analysis was performed on the distribution and drug resistance of Aeromonas. Statistical analysis of the data was conducted using single factors and logistic regression. Results A total of 111 patients with trauma-related Aeromonas infection were collected with an average age of 49.9 ± 14.3 years. The average length of hospital stay was 36.2 ± 21.1 days, and the average number of surgeries was 2.7 ± 1.6. The outcomes included debridement in 51(45.95%) cases, skin grafting in 17(15.32%) cases, flaps in 27(24.32%) cases, amputation in 14(12.61%) cases, and death in 2(1.80%) cases. The drug sensitivity test results showed that Aeromonas extracted from the wound secretions were resistant to ampicillin and amoxicillin and were sensitive to amikacin, levofloxacin and cefepime. Our research has listed 14 predictive indicators significantly associated with unfavourable prognosis mainly including body mass index (BMI), visible fracture, defect of skin, postoperative alanine aminotransferase (ALT), perioperative blood transfusion, etc. Conclusion The Aeromonas infection predominantly affected middle-aged individuals, with prolonged hospital stays and multiple surgical interventions. Highly adverse outcomes included amputation (12.61%) and even death (1.80%). Antimicrobial susceptibility testing revealed high resistance rates to common antibiotics such as ampicillin but maintained sensitivity to amikacin and levofloxacin. Our predictive model incorporating 14 prognostic indicators enables risk-stratified management. These findings provide critical insights for the early identification of severe infections and optimisation of clinical decision-making in diagnosis and treatment.