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. 2025 Aug 7:15:1619640.
doi: 10.3389/fcimb.2025.1619640. eCollection 2025.

The clinical impact of 16S ribosomal RNA PCR and sequencing in the identification of bacterial infections: a 7-year report from a Lebanese tertiary care center

Affiliations

The clinical impact of 16S ribosomal RNA PCR and sequencing in the identification of bacterial infections: a 7-year report from a Lebanese tertiary care center

Nour Youssef et al. Front Cell Infect Microbiol. .

Abstract

Introduction: The identification of bacterial pathogens in the clinical setting is essential for providing optimal care and improving outcomes. The primary objective of this study was to assess the performance of the 16S test in bacterial identification from different samples and determine its impact on clinical outcomes.

Methods: This was a retrospective study of patient samples collected from all age-groups at the American University of Beirut Medical Center (AUBMC), from May 2016 to December 2022. Descriptive statistics were conducted to calculate the 16S test positivity rate and to describe the different types of organisms. Univariate analyses were performed to study the clinical impact of the 16S test and its comparison to the conventional bacterial culture among different characteristics. A p ≤ 0.05 was considered statistically significant.

Results: A total of 1489 specimens were submitted for the 16S test during the study period. Of the submitted tests, 395 (26%) had bacteria identified by the 16S test and/or culture. Out of the culture negative/16S positive group, the majority were from specimens collected from the skin and soft tissue system (24 out of 92, 26.1%). This was followed by musculoskeletal specimens (15 out of 92, 16.3%), and central nervous system specimens (14 out of 92, 15.2%). Pus samples had a positivity rate of 66.3% with 5 times higher odds of being positive compared to non-pus samples (25%). Overall, there were 260 identified organisms by 16S test of which the most detected organisms were Staphylococcus spp, Streptococcus spp. and Enterobacterales. The results revealed that 16S testing impacted management in 45.9% of the cases (83/181) showing a change in management. Antibiotic escalation was applied in 31.3% of cases (26/83). Antibiotic de-escalation occurred in 41% of cases (34/83). A change in the treating diagnosis was noted in 26.5% of cases (22/83).

Conclusion: Identification of pathogens using the 16S test in combination with conventional cultures is essential in clinical diagnostics and management of infectious diseases to provide targeted therapy and improve antimicrobial stewardship. Shorter turnaround time, improved patient management, and cost-effectiveness are key factors to consider when advocating for the broader adoption of 16S testing.

Keywords: 16S rRNA; Lebanon; antimicrobial management; bacterial infection; clinical impact; clinical specimens; conventional culture; diagnostic yield.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
The positivity rate of 16S test across the years (N=1489). 16S rRNA PCR, 16S ribosomal Ribonucleic Acid Polymerase Chain Reaction. Binomial logistic regression analysis was used to compare the 16S rRNA PCR positivity rate across years. The year 2016 was the reference category. *P-value= 0.003, OR= 0.402, 95% CI= [0.222-0.726]. The x-axis represents the years of 16S testing from 2016 to 2022. The left y-axis is the number of 16S test negative (blue bar) and 16S test positive (red bar) specimens per year and the right y-axis is the 16S test positivity rate per year (red line).
Figure 2
Figure 2
16S test positivity rate per sample source. CNS, Central Nervous System; GU, Genito-Urinary; MSK, Musculoskeletal; SST, Skin and Soft Tissue; GI, Gastrointestinal. Binomial logistic regression analysis was used to compare the 16S rRNA PCR positivity rate among sample sources by system. The CNS was the reference category. Sample sources: GI tract: peritoneal fluid, liver, spleen, abdominal wall, bile tree; SST: leg tissues, neck abscess, lymph nodes, back abscess, thigh tissues and fluids, toe swab, perianal abscess; Cardiovascular: pericardial fluid, valve, pericardial tissue, mediastinal fluid, aortic tissue, pus surrounding pacemaker; MSK: knee tissues and fluids, bone tissues, joint fluids, synovial fluids, shoulder fluids. Respiratory tract: BAL, Pleural fluid, DTA, lung tissues; Ocular: eye discharge, eye swab; GU tract: urine, urethral discharge, vaginal swab, cervical tissue; CNS: CSF, brain abscess, brain tissue; Hematology: blood, bone marrow aspirate. The x-axis represents the 16S test positivity rate. The y-axis represents the rate in each sample source system.
Figure 3
Figure 3
Bacterial culture positivity rate per sample source. CNS, Central Nervous System; GU, Genito-Urinary; MSK, Musculoskeletal; SST, Skin and Soft Tissue; GI, Gastrointestinal. Binomial logistic regression analysis was used to compare the bacterial culture positivity rate among sample sources by system. The CNS was the reference category. Sample sources: GI tract: peritoneal fluid, liver, spleen, abdominal wall, bile tree; SST: leg tissues, neck abscess, lymph nodes, back abscess, thigh tissues and fluids, toe swab, perianal abscess; Cardiovascular: pericardial fluid, valve, pericardial tissue, mediastinal fluid, aortic tissue, pus surrounding pacemaker; MSK: knee tissues and fluids, bone tissues, joint fluids, synovial fluids, shoulder fluids. Respiratory tract: BAL, Pleural fluid, DTA, lung tissues; Ocular: eye discharge, eye swab; GU tract: urine, urethral discharge, vaginal swab, cervical tissue; CNS: CSF, brain abscess, brain tissue; Hematology: blood, bone marrow aspirate. The x-axis represents the bacterial culture positivity rate. The y-axis represents the rate in each sample source system.
Figure 4
Figure 4
The proportion of different types of organisms identified by 16S testing from 77 culture negative samples.
Figure 5
Figure 5
Clinical impact on management of positive tested samples. Multinomial logistic regression analysis was used to compare the clinical impact on management of the different categories of the positive tested samples. The category “16S positive/culture positive” was the reference category. *P-value=0.001, OR=2.859, 95%CI= [1.552-5.268]. The x-axis represents the positive tested samples. The y-axis represents the percentage of positive cases with a change in management (orange bar) versus the percentage of cases without a change in management (blue bar).

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