Microbiological monitoring of major pathogens in infected long bone fractures treated with external osteosynthesis

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BACKGROUND: The development of fracture-related infection disrupts osteoreparative processes at the fracture site, which may result in the need for repeated surgical interventions. It is well known that the etiology of fracture-related infection involves microorganisms, whose spectrum may vary considerably across healthcare facilities. Staphylococcus aureus and coagulase-negative staphylococci are the predominant pathogens in this condition, whereas Gram-negative bacteria, anaerobes, and fungi are less common.

AIM: The work is aimed to determine the spectrum and changes of major pathogens in patients with long bone fracture-related infection and post-traumatic chronic osteomyelitis treated with external osteosynthesis from 2019 to 2024.

METHODS: A single-center retrospective analysis was conducted based on microbiological findings in 247 patients with long bone fracture-related infection and chronic osteomyelitis as its sequela, all treated with external osteosynthesis. The spectrum of major pathogens and their changes over time were examined. The statistical analysis was performed using Pearson’s chi-square(χ2) test.

RESULTS: Positive cultures were obtained in 70.4% of cases, whereas 29.6% were negative. A total of 230 microorganisms were identified: 158 (68.7%) Gram-positive, 71 (30.9%) Gram-negative, and 1 (0.4%) fungal isolate. Monomicrobial infections were revealed in 76.4% of cases, whereas in 23.6% of cases, the infection was polymicrobial. A microbial shift was observed in 18.4% of patients: in 15.5% during treatment and in 2.9% upon recurrence.

DISCUSSION: The major causative pathogens of fracture-related infection were S. aureus (36.9%), S. epidermidis (10%), K. pneumoniae (9.1%), E. faecalis (7.8%), A. baumannii (6.1%), P. aeruginosa (4.3%), E. cloacae and Corynebacterium (3.5% each). Between 2019 and 2024, the incidence of MRSE and E. faecalis increased from 0.6% to 5.7% and 8.2%, respectively; Corynebacterium from 0% to 3.2%; K. pneumoniae from 2.8% to 12.7%; and E. cloacae from 1.4% to 9.9%. A microbial shift during treatment was observed in patients with open fractures and extensive soft tissue defects. Microbial composition showed unpredictable variation. In cases of recurrence, the primary isolate was often replaced by MSSA, E. faecalis, or Corynebacterium.

CONCLUSION: Despite an increasing proportion of MRSE, E. faecalis, Corynebacterium, K. pneumoniae, and E. cloacae in the etiological structure of fracture-related infection, S. aureus remains the predominant pathogen.

作者简介

Archil Tsiskarashvili

Priorov Central Research Institute of Traumatology and Orthopedics

编辑信件的主要联系方式.
Email: armed05@mail.ru
ORCID iD: 0000-0003-1721-282X
SPIN 代码: 2312-1002

MD, Cand. Sci. (Medicine)

俄罗斯联邦, 10 Priorova st, Moscow, 127299

Regina Melikova

Priorov Central Research Institute of Traumatology and Orthopedics

Email: regina-melikova@mail.ru
ORCID iD: 0000-0002-5283-7078
SPIN 代码: 8288-0256

MD, Cand. Sci. (Medicine)

俄罗斯联邦, 10 Priorova st, Moscow, 127299

Anton Nazarenko

Priorov Central Research Institute of Traumatology and Orthopedics

Email: nazarenkoag@cito-priorov.ru
ORCID iD: 0000-0003-1314-2887
SPIN 代码: 1402-5186

Corresponding Member of the Russian Academy of Sciences, MD, Dr. Sci. (Medicine), Professor of RAS

俄罗斯联邦, 10 Priorova st, Moscow, 127299

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2. Fig. 1. Distribution of patients by group, depending on the timing of infection onset and type of fracture.

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3. Fig. 2. Distribution of patients by group, depending on the duration of infection.

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4. Fig. 3. Microbiological spectrum of pathogens in fracture-related infection and chronic post-traumatic osteomyelitis of long bones.

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5. Fig. 4. Species-specific resistance of the most common pathogens throughout the microbial spectrum during the study period.

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6. Fig. 5. Changes in identification frequency of leading Gram-positive pathogens across the study periods.

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7. Fig. 6. Changes in identification frequency of leading Gram-negative pathogens across the study periods.

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8. Fig. 7. Changes in monomicrobial infection detection rates across the study periods, based on Gram classification of verified microorganisms (n=133).

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9. Fig. 8. Changes in polymicrobial infection detection rates across the study periods, based on Gram classification of identified microorganisms (n=41).

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10. Fig. 9. Species composition of microorganisms identified during microbial shift in the course of treatment and in cases of infection recurrence.

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