Antimicrobial Resistance Patterns and Microbial Epidemiology of Oral and Maxillofacial Space Infections: A Systematic Review and Meta-Analysis

Abstract

Oral and maxillofacial space infections (OMSIs) are potentially life-threatening polymicrobial conditions requiring timely and appropriate antimicrobial therapy. We conducted a systematic review and meta-analysis of 53 studies (12,408 cases) to evaluate microbial epidemiology and resistance patterns. Streptococcus was the predominant pathogen (45.51%), followed by Prevotella (15.89%) and Porphyromonas (15.61%). Methicillin-resistant Staphylococcus aureus (MRSA) accounted for 19.18% of S. aureus isolates. Diabetic patients exhibited a higher prevalence of Klebsiella (27.09% vs. 8.13% in nondiabetics). Antimicrobial resistance was lowest for levofloxacin (11.03%) and cephalosporins (16.83%), and highest for gentamicin (39.66%) and erythromycin (39.35%). The microbiological landscape and antimicrobial resistance patterns were influenced by multiple factors, including infection source, diabetic status, diagnostic methods, national income level, and temporal trends. Incorporating these variables with local antimicrobial surveillance data can support more targeted and effective empirical therapy. Well-designed, representative studies across diverse settings are urgently needed to optimize antimicrobial use amid rising resistance.

Keywords

antimicrobial resistance microbial epidemiology oral and maxillofacial space infections systematic review meta-analysis

Background

The superficial fascial spaces, anatomically adjacent to the oral cavity, constitute the primary sites of oral and maxillofacial space infections (OMSIs).^1,2 The pathogen can disseminate rapidly along interconnected fascial planes.³ OMSIs progress rapidly and can lead to life-threatening complications such as descending airway obstruction, necrotizing mediastinitis, craniocervical necrotizing fasciitis, and systemic inflammatory response syndrome, presenting significant challenges for clinical management.^4–6

Antimicrobial therapy represents a cornerstone of OMSIs management. The selection of appropriate antibiotic regimens presents clinical challenges due to the polymicrobial nature of OMSIs, which frequently involve mixed aerobic and anaerobic microorganisms derived from the complex oral microbiota. Culture-based analyses consistently show that Streptococci are the predominant pathogens in OMSIs. Staphylococcus and anaerobic organisms like Prevotella follow. Gram-negative bacilli (notably Klebsiella and Pseudomonas) have been detected in purulent specimens.^7–10 The isolation of methicillin-resistant Staphylococcus aureus (MRSA) is of significant clinical concern,^11,12 highlighting the complex etiological nature of OMSIs.

In clinical practice, there is a growing trend of combining vancomycin with broad-spectrum antibiotics (such as meropenem) as empirical therapy, due to the critical situation caused by OMSIs and the concerns related to gram-negative bacilli and MRSA infections.^12–14 However, antibiotic overuse accelerates the emergence of antimicrobial resistance, which poses a grave and multidimensional threat to global health security, affecting human health while undermining socioeconomic development.¹⁵ Current evidence is still scarce concerning the distribution of pathogens and associated antimicrobial resistance patterns in OMSIs. This systematic review and meta-analysis therefore aims to (1) characterize the microbial epidemiology of OMSIs and (2) analyze resistance patterns of OMSIs to optimize evidence-based therapeutic strategies.

Materials and Methods

Data sources and search strategy

A comprehensive literature search was conducted in the Web of Science, PubMed, EMBASE, Scopus, and the Cochrane Library from database inception until August 19, 2025. The following search terms were used: “maxillofacial space infections,” “maxillofacial infections,” “oral and maxillofacial space infections,” “Ludwig’s angina,” “angina, Ludwig’s,” “ludwig angina,” “ludwigs angina,” “submandibular space infection,” “submandibular space infections,” “culture*,” “microbio*,” “bacteria*,” “isolate*,” “pathogen*,” “sequenc*,” “swab*.” The detailed search strategy for each database is provided in Supplementary Data S1. The meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.¹⁶ This meta-analysis has been registered in PROSPERO (registration number: CRD420251124385).

Inclusion and exclusion criteria

Clinical studies were eligible for inclusion if they reported concurrent microbiological culture results, next-generation sequencing (NGS) data, or antimicrobial resistance rates in patients with OMSIs. We excluded reviews, case reports, conference abstracts, and other non-original articles. Studies were also excluded if they presented ambiguous microbiological data, were duplicate publications, or focused solely on a single specific bacterium. For multiple studies reporting on isolates with the same patient population, time period, and geographic origin, only the one involving the largest number of strains was included.

Literature screening and data extraction

The study selection was performed independently by two reviewers (Y.L. and L.Y.) using EndNote version X9. After deduplication, they screened the titles and abstracts of the remaining studies using the inclusion and exclusion criteria. Subsequently, the full texts of studies deemed potentially eligible were retrieved and assessed. Data extraction was performed independently by two investigators using Excel (Office 2019; Microsoft, Redmond, WA, USA). The extracted data included: (1) study characteristics: first author, publication year, country, and title; (2) participant demographics: sample size, sex, age, prevalence of smoking, and diabetes; (3) microbiological outcomes: number of isolated bacterial genera/species and proportion of resistance. Discrepancies during the process were resolved by consensus with a third reviewer (C.W.).

Assessment of literature quality

Two investigators independently evaluated the methodological quality of the included studies using the Joanna Briggs Institute checklist,¹⁷ scoring each item as “yes,” “no,” “unclear,” “not applicable”; any discrepancies were resolved through consensus discussion.

Statistical analysis

All statistical analyses were conducted using R version 4.2.3 with the “meta” package and employing the “metaprop” function. Before pooling, the proportions from individual studies were transformed using the Freeman–Tukey double arcsine transformation to stabilize variances and approximate normality. Random-effects meta-analyses were used to generate the pooled prevalence of the bacterial species and weighted pooled resistance rate estimates with 95% confidence intervals (CIs). Heterogeneity was evaluated through Cochran’s Q test and quantified using the I² statistic. Publication bias was quantitatively detected by funnel plots and the Egger’s test. Sensitivity analysis was conducted to examine the robustness of the model, including the potential influence of individual studies and other sources of bias.

Results

Study selection and characteristics

The systematic search initially identified 1,434 citations. Following deduplication (n = 618 excluded), 816 records underwent title/abstract screening, which excluded 679 studies; 8 additional studies were unavailable for retrieval. Of the remaining 128 full-text articles assessed, 53 were excluded due to incomplete bacterial infection data, and 22 lacked confirmed OMSIs. As illustrated in Figure 1, a total of 53 studies ultimately met the inclusion criteria.^{7–10,13,14,18–64}

FIG. 1.

Flowchart of the meta-analysis.

The 53 included studies were published between 1988 and 2025. The most frequent study locations were China (n = 14); India (n = 8); and the United States (n = 6), followed by Germany (n = 4); Lithuania (n = 3); Australia, Iran, Mexico, and South Africa (n = 2 each); and single reports from Poland, Iraq, Greece, France, Finland, Italy, Brazil, Nigeria, Ghana, and Korea (n = 1 each). These studies included 12,408 patients with confirmed OMSIs. The mean age was 41.31 ± 8.50 years, with 4,743 (38.23%) female participants. Odontogenic infections constituted the predominant etiology (89.69%; 95% CI: 88.94–90.41). Among 8,838 patients with documented medical histories, diabetes mellitus represented the most prevalent underlying comorbidity (19.95%; 95% CI: 12.10–29.10). Table 1 summarizes the characteristics of the included studies.

Table 1.

Characteristics of the Included Studies

Study	Author	Country	Patients	Females	Median age	Diabetes	Odontogenic	Sample
1	Zirk et al. (2016)⁶⁴	Germany	294	118	41.1	/	/	294
2	Zheng et al. (2012)⁶³	China	191	68	/	99	137	191
3	Yuvaraj et al. (2012)⁶²	India	88	38	33.0	/	/	80
4	Yang et al. (2023)⁶¹	China	190	62	/	51	102	190
5	Wei et al. (2014)⁶⁰	China	307	113	/	78	/	307
6	Wang et al. (2022)⁹	China	746	308	48.6	/	559	488
7	van der Merwe et al. (2023)⁵⁹	South Africa	63	48	38.6	/	55	102
8	Shi et al. (2022)⁵⁸	China	15	4	47.1	1	13	15
9	Shi et al. (2023)⁸	China	218	67	53.7	39	183	218
10	Shah et al. (2025)⁵⁷	India	141	84	/	16	141	160
11	Rowe et al. (2011)⁵⁶	Australia	30	13	/	/	/	23
12	Rasteniene et al. (2023)⁵⁵	Lithuania	428	180	/	7	123	181
13	Rasteniene et al. (2024)⁷	Lithuania	4,040	1,414	42.1	163	0	1,489
14	Rasteniene et al. (2015)⁵⁴	Lithuania	1,077	449	37.0	30	1,077	1,077
15	Rao et al. (2010)⁵³	India	111	/	44.9	31	111	111
16	Qian et al. (2021)⁵²	China	222	81	52.2	/	141	151
17	Prasanth et al. (2022)⁵¹	India	120	50	35.8	/	120	120
18	Pourdanesh et al. (2013)⁵⁰	Iran	310	133	/	/	310	46
19	Patel et al. (2009)⁴⁹	South Africa	15	2	37	/	15	15
20	Maharaj et al. (2019)⁴⁸	India	95	/	36.5	/	/	55
21	López-González et al. (2019)⁴⁷	Mexico	14	7	37.5	/	14	42
22	Lokes et al. (2024)⁴⁵	Germany	75	36	/	/	75	75
23	Lin et al. (2020)⁴⁶	China	29	8	53.4	10	21	29
24	Kumari et al. (2018)⁴⁴	India	40	15	27.3	/	40	40
25	Krishnan et al. (1993)⁴³	United States	50	18	25	/	43	50
26	Kinzer et al. (2009)⁴²	Germany	10	3	44.5	/	10	10
27	Kaminski et al. (2022)¹³	Poland	329	137	40.9	/	106	329
28	Kamat et al. (2015)⁴¹	Korean	188	80	50.3	61	188	188
29	Jundt et al. (2012)⁴⁰	United States	42	21	35.8	3	42	42
30	Juang et al. (1989)³⁹	China	14	2	/	1	12	14
31	Igoumenakis et al. (2015)³⁸	Greece	179	70	39.1	/	179	179
32	Han et al. (2016)³⁷	China	127	52	45.4	43	73	110
33	Guo et al. (2018)³⁵	China	348	140	51.1	54	138	357
34	Guo et al. (2021)³⁶	China	59	18	48	14	35	59
35	Grönholm et al. (2013)³⁴	Finland	60	24	45	6	60	44
36	Gómez-Arámbula et al. (2015)³²	Mexico	21	11	41.9	/	21	21
37	Gilmore et al. (1988)³³	United States	55	14	/	/	55	55
38	Ge et al. (2022)³¹	China	60	21	36.7	25	60	53
39	Gams et al. (2017)³⁰	United States	298	134	38.9	36	298	298
40	Flynn et al. (2006)²⁹	United States	37	14	34.9	2	37	37
41	Eick et al. (2000)²⁸	Germany	110	/	/	/	99	110
42	Delbet-Dupas et al. (2019)²⁷	France	653	267	37	630	653	535
43	de Paula et al. (2024)²⁶	Brazil	61	23	34.3	/	30	61
44	Das Jesabel et al. (2020)²⁵	India	141	84	36.8	/	/	35
45	Chen et al. (2021)²⁴	China	16	4	58.3	/	12	16
46	Canas et al. (2023)¹⁴	United States	73	31	52	13	/	73
47	Bridgeman et al. (1995)²³	Australia	107	47	23	/	80	81
48	Boscolo-Rizzo et al. (2009)²²	Italy	81	39	57	13	38	42
49	Bali et al. (2015)²¹	India	60	30	33.5	/	60	123
50	Aliabadi et al. (2021)²⁰	Iran	97	46	33.8	/	97	97
51	Akram et al. (2022)¹⁰	Iraq	222	95	50.8	/	222	222
52	Akinbami et al. (2010)¹⁹	Nigeria	17	7	34.7	3	17	17
53	Acevedo et al. (2018)¹⁸	Ghana	34	13	/	2	26	34

All 53 included studies were assessed using the JBI Critical Appraisal Checklist for Prevalence Studies. Overall, the majority demonstrated appropriate sampling frames, recruitment strategies, sample sizes, and detailed descriptions of study participants and settings. However, 33 studies provided only minimal information on microbiological methods—typically stating that samples were “cultured” without detailing culture conditions or identification procedures. Although this limits comparability, the omission was not deemed sufficient to justify exclusion (Supplementary Data S2).

Synthesis of results

Microbiological testing protocols of OMSIs utilized two principal methodologies: purulent exudate cultures (48 studies)^{7,9,10,13,14,19–23,25–64} and nucleic acid amplification techniques (3 studies).^8,24,58 The microbial profile revealed Streptococcus as the predominant pathogen (45.51%; 95% CI: 38.34–52.77), followed by anaerobic organisms including Prevotella (15.89%; 95% CI: 7.57–26.38) and Porphyromonas (15.61%; 95% CI: 1.69–38.33). Among aerobic isolates, Streptococcus viridans (29.65%; 95% CI: 20.90–39.19) was the most prevalent. Other isolates from OMSIs included S. aureus (9.03%; 95% CI: 5.77–12.86), coagulase-negative staphylococci (10.63%; 95% CI: 6.41–15.71), and gram-negative bacilli such as Klebsiella (7.56%; 95% CI: 4.73–10.90), Escherichia coli (4.14%; 95% CI: 1.09–8.54), and Pseudomonas (1.77%; 95% CI: 0.77–3.06). The forest plot in Figure 2 illustrates the prevalence data, with detailed information provided in Supplementary Data S3. Among 1,082 cultured samples, 34 S. aureus isolates were identified, including 8 MRSA isolates, corresponding to a prevalence of 0.23% (95% CI: 0.00–1.49) among all specimens. Among the S. aureus isolates, MRSA accounted for 19.18% (95% CI: 0.00–59.56).^{14,19,21,23,30,34,40,42,52,59}

FIG. 2.

Prevalence of microorganisms in patients with oral and maxillofacial space infections. CI, confidence interval; MRSA, methicillin-resistant Staphylococcus aureus.

Nineteen included studies provided antimicrobial susceptibility profiles of pathogens isolated from OMSIs, with weighted pooled resistance rates across 11 antibiotics, ranging from 11.03% to 39.66% (Fig. 3, Supplementary Data S4). Levofloxacin demonstrated the lowest resistance rate (11.03%; 95% CI: 0.00–39.73), whereas β-lactam exhibited intermediate resistance: cephalosporin (16.83%; 95% CI: 3.44–37.01), ampicillin/amoxicillin (18.50%; 95% CI: 7.43–32.73), and amoxicillin/clavulanate (19.66%; 95% CI: 5.12–40.21). The highest resistance rates were observed for gentamicin (39.66%; 95% CI: 9.85–74.51), erythromycin (39.35%; 95% CI: 18.65–62.22), penicillin (26.72%; 95% CI: 16.69–38.05), metronidazole (28.29%; 95% CI: 13.68–45.65), and tetracycline (28.22%; 95% CI: 4.40–61.69).

FIG. 3.

Forest plot for the antimicrobial resistance profiles of clinical isolates.

Subgroup analysis of microorganisms

Methods used for the identification of microorganisms

Studies employing NGS revealed that anaerobic bacteria including Prevotella (72.26%; 95% CI: 50.23–90.07), Porphyromonas (60.89%; 95% CI: 48.76–72.43), and Peptostreptococcus (54.05%; 95% CI: 20.34–85.94) were major pathogens in OMSIs. In contrast, culture-based methods reported substantially lower detection rates for Prevotella (9.30%; 95% CI: 5.63–13.70) and Peptostreptococcus (7.01%; 95% CI: 4.38–10.11) (Table 2).

Table 2.

Subgroup Analysis of Microorganisms in Patients with Oral and Maxillofacial Space Infections

Outcomes	Studies	Cases	Samples	Proportion (%)	95% CI	Tau	I²	p
Methods used for the identification of microorganisms
Cluture
Klebsiella	31	250	4,141	6.9	[4.22–10.01]	0.1363	90.9%	<0.0001
Peptostreptococcus	16	171	3,750	7.0	[4.38–10.11]	0.9400	88.4%	<0.0001
Prevotella	16	564	5,250	9.3	[5.63–13.70]	0.1286	95.1%	<0.0001
Streptococcus	48	3,461	8,192	43.4	[36.44–50.52]	0.2357	96.0%	<0.0001
Staphylococcus	46	1,534	8,141	14.8	[10.89–19.14]	0.1815	96.0%	<0.0001
Next-generation sequencing
Klebsiella	3	24	247	19.7	[0.61–51.66]	0.2698	89.1%	0.0001
Peptostreptococcus	3	149	247	54.0	[20.34–85.94]	0.2841	85.9%	0.0008
Prevotella	3	145	247	72.3	[50.23–90.07]	0.1666	79.9%	0.007
Streptococcus	3	152	247	80.5	[47.41–99.69]	0.2808	90.2%	<0.0001
Porphyromonas	3	141	247	60.9	[48.76–72.43]	0.0662	24.7%	0.2648
Infection origin
Non-odontogenic
Streptococcus	2	726	1776	35.0	[6.49–61.84]	0.1006	87.3%	0.005
Staphylococcus	2	702	1776	49.0	[27.70–70.26]	0.0234	94.1%	<0.0001
Enterobacter	2	54	1776	2.8	[2.06–3.68]	0.0000	90.0%	0.6791
Prevotella	2	69	1776	3.0	[1.11–5.77]	0.0369	48.0%	0.1655
Odontogenic
Streptococcus	25	1,588	3,691	37.0	[28.11–46.35]	0.2275	96.5%	<0.0001
Staphylococcus	25	488	3,599	12.0	[7.62–17.15]	0.1670	91.6%	<0.0001
Pseudomonas	4	20	306	5.5	[2.93–8.71]	0.0000	0%	0.6697
Prevotella	11	270	2321	10.5	[7.13–14.30]	0.0790	73.8%	<0.0001
Klebsiella	14	76	1,728	5.2	[2.11–9.32]	0.1324	86.8%	<0.0001
Enterococcus	9	74	1,001	7.9	[2.40–15.86]	0.1761	91.2%	<0.0001
Peptostreptococcus	10	104	1,278	9.2	[5.49–13.76]	0.0949	81.0%	<0.0001
Haemophilus	5	21	1,021	1.9	[1.06–2.85]	0.0202	6.2%	0.3717
Fusobacterium	10	43	1,356	3.0	[1.33–5.25]	0.0624	71.6%	0.0002
Escherichia coli	2	30	160	13.3	[0.03–40.74]	0.2175	93.1%	0.0001
Bacteroides	11	198	2,357	2.5	[2.45–8.94]	0.1017	88.2%	<0.0001
Actinomyces	9	34	1,204	2.3	[1.18–3.35]	0.0475	51.9%	0.0341
Acinetobacter	5	28	647	3.3	[1.87–5.10]	0.0000	0%	0.849
Subgroup of complication
Diabetes
Klebsiella	4	36	131	27.1	[19.57–35.29]	0.0000	0%	0.4557
Enterococcus	2	9	65	13.4	[5.69–23.32]	0.0000	0%	0.9384
Pseudomonas	2	4	65	5.1	[0.53–12.66]	0.0781	36.7%	0.2089
Streptococcus	4	40	131	32.1	[18.34–47.40]	0.1250	66.0%	0.0316
Staphylococcus	4	24	131	18.1	[11.66–25.45]	0.0000	0%	0.9709
Anaerobe	2	9	66	11.3	[0.46–30.67]	0.1432	72.4%	0.0569
Nondiabetes
Klebsiella	4	23	279	8.1	[5.08–11.77]	0.0000	0%	0.8658
Enterococcus	2	17	145	11.6	[6.78–17.52]	0.0000	0%	0.8881
Pseudomonas	3	13	199	5.8	[2.05–11.02]	0.0498	37.3%	0.203
Streptococcus	4	147	279	53.0	[34.17–71.42]	0.1817	91.4%	<0.0001
Staphylococcus	3	42	233	15.9	[1.23–41.05]	0.2410	94.6%	<0.0001
Anaerobe	2	12	134	9.0	[0.39–25.22]	0.1424	84.0%	0.0123
Subgroup of age
Infant
Streptococcus	3	5	6	90.3	[41.24-1.00]	0.0000	0%	0.6813
Adult
Klebsiella	4	89	380	22.4	[18.06–26.97]	0.0000	0%	0.6891
Streptococcus	11	234	633	41.4	[29.08–54.24]	0.1714	84.6%	<0.0001
Staphylococcus	9	113	542	14.9	[7.94–23.22]	0.1052	70.2%	0.0008
Pseudomonas	3	18	362	3.4	[0.00–11.45]	0.0967	77.1%	0.0127
Enterobacter	4	15	210	8.3	[0.63–20.94]	0.1215	58.7%	0.0641
Anaerobe	7	76	367	30.6	[6.49–61.84]	0.3983	93.4%	<0.0001
Lower middle income
Streptococcus	13	301	857	34.7	[18.40–53.03]	0.3196	96.2%	<0.0001
Staphylococcus	13	134	857	17.9	[9.94–27.41]	0.1836	83.4%	<0.0001
Pseudomonas	6	16	440	3.0	[0.65–6.41]	0.0550	35.8%	0.1680
Prevotella	2	44	293	11.6	[2.40–25.89]	0.1239	86.9%	0.0057
Peptostreptococcus	3	46	393	13.3	[6.32–22.20]	0.0904	80.9%	0.0053
Klebsiella	10	56	656	10.2	[5.56–15.93]	0.1047	64.9%	0.0024
Enterococcus	5	40	529	9.7	[2.58–20.25]	0.1562	87.7%	<0.0001
Candida	2	6	243	2.2	[0.05–6.52]	0.0597	63.5%	0.0978
Upper middle income
Streptococcus	19	945	1,834	57.4	[44.41–69.89]	0.2694	93.8%	<0.0001
Staphylococcus	14	188	1,635	11.2	[6.42–16.91]	0.1381	90.4%	<0.0001
Pseudomonas	10	30	987	2.4	[0.86–4.39]	0.0563	56.9%	0.0132
Prevotella	7	266	990	30.4	[5.98–62.58]	0.4263	97.8%	<0.0001
Klebsiella	14	172	1,601	10.9	[5.74–17.25]	0.1447	89.4%	<0.0001
Peptostreptococcus	5	155	346	30.6	[5.32–64.19]	0.3665	96.8%	<0.0001
Candida	7	21	776	2.4	[1.36–3.75]	0.0000	0.0%	0.6990
High income								<0.0001
Streptococcus	21	2,367	5,878	35.8	[26.82–45.25]	0.2145	97.3%	<0.0001
Staphylococcus	19	1,212	5,649	15.6	[9.10–23.43]	0.2071	97.8%	<0.0001
Pseudomonas	6	34	3,455	1.1	[0.15–9.18]	0.0660	84.5%	<0.0001
Prevotella	10	399	4,214	9.9	[5.54–15.16]	0.1167	95.2%	<0.0001
Klebsiella	9	44	1,966	2.6	[0.51–5.88]	0.1077	87.7%	<0.0001
Enterococcus	6	65	2,667	5.3	[0.37–14.46]	0.1874	95.4%	<0.0001
Peptostreptococcus	11	119	3,258	5.6	[2.91–8.99]	0.0901	87.4%	<0.0001
Haemophilus	6	50	3,014	2.0	[0.86–3.63]	0.0481	82.5%	<0.0001
Fusobacterium	9	76	3,267	2.7	[0.83–5.37]	0.0839	92.6%	<0.0001
Enterobacter	5	62	3,088	0.9	[0.05–2.28]	0.0485	85.7%	<0.0001
Bacteroides	13	236	4,093	6.1	[3.03–9.98]	0.1124	93.7%	<0.0001
Candida	4	297	2,800	9.6	[0.00–34.13]	0.3138	99.5%	<0.0001
Recent studies (2015–2025)
Streptococcus	35	2,993	6,694	44.6	[34.40–54.96]	0.2990	96.9%	<0.0001
Staphylococcus	30	1,365	6,303	17.8	[12.50–23.70]	0.1872	96.1%	<0.0001
Pseudomonas	16	66	3,930	1.8	[0.60–3.36]	0.0691	79.4%	<0.0001
Prevotella	14	552	4,675	17.1	[6.00–31.94]	0.3209	97.6%	<0.0001
Klebsiella	22	184	3,069	7.4	[4.16–11.30]	0.1372	90.0%	<0.0001
Enterococcus	11	90	3,007	6.4	[2.05–12.46]	0.1592	92.9%	<0.0001
Acinetobacter	7	21	918	2.4	[0.26–5.76]	0.0759	76.2%	0.0003
Peptostreptococcus	10	257	2,934	16.9	[4.90–33.51]	0.2974	98.3%	<0.0001
Fusobacterium	7	100	2,574	7.3	[0.33–20.20]	0.2442	98.0%	<0.0001
Enterobacter	6	66	2,804	1.7	[0.71–2.98]	0.0351	65.5%	0.0127
Bacteroides	8	155	3,627	2.9	[1.19–5.21]	0.0683	93.2%	<0.0001
Candida	10	257	2,934	16.9	[4.90–33.51]	0.2974	98.3%	<0.0001
Earlier studies (before 2015)
Streptococcus	18	620	1,875	40.1	[29.08–51.61]	0.2308	94.0%	<0.0001
Staphylococcus	16	169	1,838	9.8	[5.13–15.52]	0.1556	91.5%	<0.0001
Pseudomonas	6	14	952	1.9	[0.22–4.72]	0.0793	75.8%	<0.0001
Prevotella	5	157	822	14.0	[6.42–23.73]	0.1249	87.6%	<0.0001
Klebsiella	12	90	1,319	8.0	[2.82–15.03]	0.1705	92.5%	<0.0001
Peptostreptococcus	9	63	1,063	7.2	[3.67–11.67]	0.0930	81.5%	<0.0001
Fusobacterium	8	72	1,224	4.7	[2.96–6.84]	0.0366	40.6%	<0.0001
Enterobacter	5	21	861	2.8	[0.35–6.90]	0.0866	86.2%	<0.0001
Bacteroides	10	88	821	7.6	[3.11–13.38]	0.1236	84.6%	<0.0001
Candida	2	4	277	1.4	[0.24–3.28]	0.0000	0.0%	0.6759

CI, confidence interval.

Odontogenic versus non-odontogenic OMSIs

In odontogenic cases, Streptococcus predominated (37.00%; 95% CI: 28.11–46.35), followed by Staphylococcus (12.01%; 95% CI: 7.62–17.15), Prevotella (10.46%; 95% CI: 7.13–14.30), Peptostreptococcus (9.24%; 95% CI: 5.49–13.76), Enterococcus (7.90%; 95% CI: 2.40–15.86), Pseudomonas (5.53%; 95% CI: 2.93–8.71), and Klebsiella (5.19%; 95% CI: 2.11–9.32). Non-odontogenic infections were primarily associated with Staphylococcus (48.88%; 95% CI: 27.70–70.26) and Streptococcus (34.97%; 95% CI: 6.49–61.84), and Prevotella was detected in a relatively limited proportion of isolates (3.04%; 95% CI: 1.11–5.77) (Table 2).

Diabetic versus nondiabetic patients

To evaluate the impact of diabetes, we performed a subgroup analysis comparing diabetic and nondiabetic patients. The results demonstrated a significantly higher prevalence of Klebsiella in OMSIs among diabetic patients (27.09%; 95% CI: 19.57–35.29) than in nondiabetic individuals (8.13%; 95% CI: 5.08–11.77) (Table 2).

National income level

Klebsiella was more frequently reported in lower-middle-income (10.22%; 95% CI: 5.56–15.93) and upper-middle-income countries (10.90%; 95% CI: 5.74–17.25) than in high-income countries (2.60%; 95% CI: 0.51–5.88). In contrast, Candida was predominantly identified in high-income countries (9.55%; 95% CI: 0.00–34.13), with lower pooled prevalences in lower-middle-income (2.22%; 95% CI: 0.05–6.52) and upper-middle-income settings (2.44%; 95% CI: 1.36–3.75) (Table 2).

Time period

Subgroup analysis by publication period (before 2015 vs. 2015–2025) revealed notable temporal trends in the microbial etiology of OMSIs (Table 2). The pooled prevalence of Staphylococcus nearly doubled in recent studies (17.77%; 95% CI: 12.50–23.70) compared to earlier reports (9.76%; 95% CI: 5.13–15.52). Similarly, certain anaerobic bacteria showed higher detection rates in the recent period, including Peptostreptococcus (16.85%; 95% CI: 4.90–33.51 vs. 7.21%; 95% CI: 3.67–11.67) and Prevotella (17.06%; 95% CI: 6.00–31.94 vs. 14.01%; 95% CI: 6.42–23.73). Candida was markedly more prevalent in recent studies (16.85%; 95% CI: 4.90–33.51) than in studies published before 2015 (1.41%; 95% CI: 0.24–3.28).

Subgroup analysis of antimicrobial susceptibility

Antimicrobial susceptibility testing according to Clinical and Laboratory Standards Institute guidelines

Based on the subgroup analysis restricted to studies that adhered to Clinical and Laboratory Standards Institute (CLSI) guidelines for antimicrobial susceptibility testing, the highest pooled resistance was observed for erythromycin (53.30%; 95% CI: 27.45–78.25), followed by clindamycin (28.35%; 95% CI: 12.64–47.28) and penicillin (22.65%; 95% CI: 8.00–41.78). Resistance to ampicillin/amoxicillin was similarly high at 22.60% (95% CI: 8.10–41.48), whereas cephalosporins showed a lower resistance rate of 15.52% (95% CI: 2.47–36.06) (Table 3).

Table 3.

Subgroup Analysis of the Antimicrobial Resistance Profiles

Outcomes	Studies	Cases	Samples	Proportion (%)	95% CI	Tau	I²	p
Antimicrobial susceptibility testing according to CLSI guidelines
Penicillin	6	231	941	22.65	[8.00–41.78]	0.2485	98.2%	<0.0001
Clindamycin	8	322	1,088	28.35	[12.64–47.28]	0.2742	98.2%	<0.0001
Cephalosporin	6	112	689	15.52	[2.47–36.06]	0.2875	96.9%	<0.0001
Ampicillin/Amoxicillin	5	176	601	22.60	[8.10–41.48]	0.2218	97.4%	<0.0001
Erythromycin	3	281	476	53.30	[27.45–78.25]	0.2271	96.6%	<0.0001
Ceftriaxone	4	90	346	17.94	[3.61–39.13]	0.2275	91.9%	<0.0001
Levofloxacin	3	90	476	11.03	[0.00–39.73]	0.2937	98.6%	<0.0001
Type of microorganism
Streptococcus
Penicillin	9	231	1,768	16.3	[4.59–32.72]	0.2797	96.1%	<0.0001
Clindamycin	6	255	1,046	30.2	[6.64–61.09]	0.3800	99.0%	<0.0001
Cephalosporin	5	93	916	8.0	[0.49–21.50]	0.2062	94.4%	<0.0001
Ampicillin/Amoxicillin	7	46	390	12.4	[0.70–31.84]	0.2754	94.1%	<0.0001
Erythromycin	4	198	285	50.5	[15.92–84.83]	0.3686	97.6%	<0.0001
Staphylococcus
Penicillin	11	421	964	64.2	[46.73–80.20]	0.2336	84.8%	<0.0001
Clindamycin	6	62	757	36.4	[6.05–73.28]	0.4008	93.7%	<0.0001
Cephalosporin	3	26	203	16.6	[1.24–41.77]	0.2223	85.9%	0.0008
Ampicillin/Amoxicillin	5	31	69	52.8	[11.48–92.30]	0.3642	90.1%	<0.0001
Erythromycin	4	36	67	67.2	[25.97–97.78]	0.3730	89.9%	<0.0001
Gram-negative bacteria
Penicillin	4	198	285	50.5	[15.92–84.83]	0.3686	97.6%	<0.0001
Cephalosporin	4	7	46	13.3	[3.77–26.06]	0.0297	28.1%	0.2437
Ampicillin/Amoxicillin	5	33	51	67.6	[51.07–82.63]	0.0000	0.0%	0.4623
Recent years (2015–2025)
Penicillin	9	857	3,028	27.83	[15.26–42.34]	0.2143	96.9%	<0.0001
Clindamycin	10	499	2,511	29.67	[17.48–43.50]	0.2226	98.3%	<0.0001
Cephalosporin	7	211	1,410	13.66	[2.43–31.22]	0.2770	96.3%	<0.0001
Ampicillin/Amoxicillin	5	38	464	10.68	[3.37–20.72]	0.1262	76.3%	<0.0001
Erythromycin	5	308	599	39.35	[18.65–62.22]	0.2529	96.7%	<0.0001
Gentamicin	4	320	1,156	39.66	[9.85–74.51]	0.3602	99.0%	<0.0001
Levofloxacin	3	90	476	11.03	[0.00–39.73]	0.2937	98.6%	<0.0001
Metronidazole	5	623	2,744	23.32	[11.34–37.99]	0.1781	98.1%	<0.0001
Tetracycline	3	168	420	28.22	[4.40–61.69]	0.2947	98.0%	<0.0001
Ceftriaxone	4	90	346	17.94	[3.61–39.13]	0.2275	91.9%	<0.0001
Earlier studies (before 2015)
Penicillin	4	76	489	24.40	[8.31–45.18]	0.2083	93.4%	<0.0001
Clindamycin	4	48	551	10.15	[2.96–20.53]	0.1322	88.7%	<0.0001
Stratified by national income level
Middle income
Penicillin	6	185	602	23.98	[7.55–45.32]	0.2585	97.8%	<0.0001
Clindamycin	6	282	655	35.03	[15.40–57.66]	0.2750	97.5%	<0.0001
Cephalosporin	6	108	622	15.04	[2.01–36.01]	0.2932	96.8%	<0.0001
Metronidazole	3	120	323	35.50	[6.67–71.70]	0.3135	96.3%	<0.0001
Ceftriaxone	4	90	346	17.94	[3.61–39.13]	0.2275	91.9%	<0.0001
Erythromycin	4	302	555	46.42	[24.30–69.30]	0.2296	96.6%	<0.0001
Ampicillin/Amoxicillin	8	195	774	19.73	[8.97–33.11]	0.1991	96.2%	<0.0001
Levofloxacin	3	90	476	11.03	[0.00–39.73]	0.2937	98.6%	<0.0001
High income
Penicillin	7	748	2,915	28.87	[17.79–41.33]	0.1627	94.8%	<0.0001
Clindamycin	8	265	2,407	15.98	[9.20–24.12]	0.1350	93.5%	<0.0001
Cephalosporin	3	112	959	6.49	[1.46–14.36]	0.1028	87.8%	0.0003
Metronidazole	3	517	2,443	22.66	[11.39–36.40]	0.1310	98.6%	<0.0001

CLSI, Clinical and Laboratory Standards Institute.

Antibiotic resistance based on bacterial species

Subgroup analysis of antibiotic resistance patterns revealed species-specific profiles. Streptococcus exhibited pronounced resistance to erythromycin (50.51%; 95% CI: 15.92–84.83) and clindamycin (30.19%; 95% CI: 6.64–61.09), but remained susceptible to β-lactams, including penicillin (16.28%; 95% CI: 4.59–32.72), ampicillin/amoxicillin (12.42%; 95% CI: 0.70–31.84), and cephalosporins (7.95%; 95% CI: 0.49–21.50). Non-MRSA Staphylococcus showed high resistance rates to penicillin (64.22%; 95% CI: 46.73–80.20) and erythromycin (67.15%; 95% CI: 25.97–97.78), followed by ampicillin/amoxicillin (52.77%; 95% CI: 11.48–92.30) and clindamycin (36.43%; 95% CI: 6.05–73.28), while demonstrating greater susceptibility to cephalosporins (16.60%; 95% CI: 1.24–41.77). Gram-negative bacteria exhibited relatively low resistance to cephalosporins (13.29%; 95% CI: 3.77–26.06) (Table 3).

Antibiotic resistance patterns by publication period

Subgroup analysis by publication period revealed a significant increase in clindamycin resistance among OMSIs-associated pathogens in recent years (29.67%; 95% CI: 17.48–43.50) compared to the pre-2015 period (10.15%; 95% CI: 2.96–20.53). In contrast, penicillin resistance remained persistently high across both eras (27.83%; 95% CI: 15.26–42.34 in 2015–2025 vs. 24.40%; 95% CI: 8.31–45.18 before 2015). Resistance to erythromycin, gentamicin, and metronidazole was also substantial in recent studies; however, insufficient data from earlier publications precluded meaningful comparison with the pre-2015 era.

Antimicrobial resistance patterns by national income level

Subgroup analysis by national income level showed that resistance to clindamycin (35.03%; 95% CI: 15.40–57.66 vs. 15.98%; 95% CI: 9.20–24.12), metronidazole (35.50%; 95% CI: 6.67–71.70 vs. 22.66%; 95% CI: 11.39–36.40), and cephalosporins (15.04%; 95% CI: 2.01–36.01 vs. 6.49%; 95% CI: 1.46–14.36) was consistently higher in middle-income than in high-income countries. Notably, resistance to the third-generation cephalosporin ceftriaxone was reported exclusively in middle-income countries (17.94%; 95% CI: 3.61–39.13).

Risk of bias

Funnel plots (Supplementary Data S5) and Egger’s test (Supplementary Data S6) did not indicate significant publication bias for several microorganisms, including Streptococcus, Staphylococcus, Prevotella, S. aureus, coagulase-negative staphylococci, S. milleri, Enterobacter, Fusobacterium, Bacteroides, and Candida. In contrast, significant funnel plot asymmetry was observed for Haemophilus (p = 0.0447), Enterococcus (p = 0.0020), Actinomyces (p = 0.0334), E. coli (p = 0.0372), Pseudomonas (p = 0.0005), Klebsiella (p = 0.0014), S. viridans (p = 0.0072), Peptostreptococcus (p = 0.0343), and MRSA (p = 0.0400), suggesting a potential risk of publication bias for these nine microorganisms. Among antibiotics, no significant publication bias was detected for penicillin and clindamycin. For the remaining nine antibiotics, publication bias was not assessed due to an insufficient number of included studies (k < 10). To assess the influence of individual studies on the pooled estimates, we performed a leave-one-out sensitivity analysis. The results demonstrated that the effect estimates remained stable upon the sequential exclusion of each study (Supplementary Data S7 and S8), suggesting that our findings are not overly dependent on any single literature source.

Discussion

OMSIs may rapidly progress to life-threatening complications when polymicrobial pathogens disseminate through interconnected fascial planes. In the context of escalating global antimicrobial resistance, empirical therapy often relies on broad-spectrum antibiotics. In this systematic review and meta-analysis, we delineate the microbial epidemiology of OMSIs and characterize their associated antimicrobial resistance patterns, providing a foundation for more targeted and evidence-based antimicrobial strategies.

Our analysis reveals that OMSIs are typically characterized by polymicrobial infections featuring synergistic interactions between aerobic and anaerobic bacteria. Streptococcus—particularly the viridans groups—emerged as predominant aerobic pathogens, with Prevotella and Staphylococcus frequently isolated as co-pathogens. These findings are consistent with global surveillance data.⁶⁵Our study shows that Streptococcus predominates in odontogenic OMSIs, whereas Staphylococcus are the primary pathogen in non-odontogenic infections, with a markedly lower prevalence of Prevotella in the latter. This pattern aligns with the established understanding of oral microbial ecology and the etiological specificity of infection sources.^66,67 The detection rate of Klebsiella in diabetic patients was more than threefold higher than in nondiabetic patients, consistent with previous studies reporting an increased risk of Klebsiella infection in diabetic hosts.^68,69 This finding suggests that empirical antimicrobial therapy in diabetic patients with OMSIs may warrant enhanced coverage against gram-negative organisms. However, it should be noted that few studies reporting on non-odontogenic OMSIs and diabetes were included in our analysis. This limited evidence base may affect the robustness and generalizability of findings for this subgroup, underscoring the need for additional high-quality studies to confirm these observations. Klebsiella was more frequently isolated in OMSIs in middle-income countries than in high-income countries, consistent with prior reports on regional disparities in the epidemiology of Klebsiella.⁷⁰ However, conventional culture-based techniques are prone to missing fastidious or unculturable anaerobes, which may lead to an underestimation of their true prevalence. It may therefore be beneficial for future research to explore the use of NGS to enable a more comprehensive and accurate characterization of the microbial landscape in OMSIs.

This study systematically evaluated the overall antimicrobial resistance profiles of 11 commonly used antibiotics based on data from 19 studies reporting antimicrobial susceptibility testing of pathogens isolated from OMSIs. The pooled resistance rate was lowest for levofloxacin (11.03%), whereas β-lactam antibiotics—including cephalosporins, ampicillin/amoxicillin, and amoxicillin/clavulanate—exhibited moderate resistance rates (16.83–19.66%). In contrast, substantially higher resistance was observed for gentamicin (39.66%), erythromycin (39.35%), metronidazole (28.29%), tetracycline (28.22%), and penicillin (26.72%). However, these findings should be interpreted with caution. Analyses for certain antibiotics—such as levofloxacin and tetracycline—were based on only three studies each. Moreover, heterogeneity exists across real-world settings due to variations in susceptibility testing methodologies, pathogen composition, and regional antibiotic prescribing practices, all of which contribute to uncertainty in the estimates. For example, non-MRSA Staphylococcus exhibited a markedly high penicillin resistance rate (64.2%), compared with only 16.3% in Streptococcus, a pattern highly consistent with the well-documented capacity of staphylococci to produce β-lactamase.⁷¹ Additionally, clindamycin resistance increased significantly in the post-2015 period (29.67%) compared with the pre-2015 era (10.15%), further underscoring the dynamic evolution of antimicrobial resistance.^72,73 Notably, metronidazole resistance was considerably higher in middle-income countries (35.50%) than in high-income countries (22.66%), likely reflecting prolonged and often unregulated use of this agent in resource-limited settings.⁷⁴ Antimicrobial therapy for OMSIs is facing increasingly complex resistance challenges. There is an urgent need to strengthen microbiological diagnostics, promote standardized susceptibility testing, and tailor empirical regimens according to infection origin, comorbidities, and national health care capacity.

Despite extensive subgroup analyses, high heterogeneity persisted across most subgroups—a common feature in prevalence-based meta-analyses with deep epidemiological and statistical roots.⁷⁵ First, real-world variability inherently drives heterogeneity: pathogen prevalence and resistance are shaped by time, geography, and population characteristics, further amplified by (1) evolving antimicrobial susceptibility testing methods and interpretive criteria over decades; (2) advances in microbiological diagnostics—from aerobic-only culture to anaerobic techniques and molecular methods (e.g., 16S ribosomal RNA sequencing)—which alter detection sensitivity and spectrum; (3) regional differences in antibiotic use that selectively enrich or suppress specific pathogens; (4) variations in patient demographics, comorbidities, and health care access; and (5) inconsistent sample collection protocols. Second, statistical properties of proportion data contribute to inflated I²: non-comparative prevalence estimates naturally yield high apparent heterogeneity even with modest absolute differences, unlike comparative effect measures.⁷⁵ Third, low I² values in a few subgroups (e.g., Klebsiella prevalence in diabetic vs. non-diabetic patients, both I² = 0%) should be interpreted cautiously—they stem from limited study numbers (k ≤ 5), where I² lacks power to detect heterogeneity.⁷⁶ Notably, despite statistical homogeneity, the pooled prevalence differed more than threefold (27.1% vs. 8.1%), revealing underlying epidemiological divergence. In summary, the high heterogeneity observed is characteristic of prevalence-based meta-analyses. Future high-quality primary studies with standardized methods and stratified reporting by clinical and demographic factors are essential to improve the precision and generalizability of meta-analyses on prevalence.

In this study, publication bias concerning less common pathogens in OMSIs—such as Enterococcus, E. coli, Pseudomonas, Klebsiella, Haemophilus, and Actinomyces—likely stems from two interrelated factors. First, the majority of studies report these organisms as “not detected” and such negative findings are seldom published. Second, high-throughput sequencing can detect microbial DNA at extremely low abundances but cannot reliably distinguish between true infection, colonization, and contamination. This limitation may lead to overestimation of pathogen prevalence in the literature. The pooled prevalence of Peptostreptococcus was initially estimated at 11.87%. After the trim-and-fill analysis imputed 10 potentially missing studies, the adjusted estimate decreased to 2.26%. Similarly, the pooled prevalence of S. viridans was initially 29.0% and fell to 13.04% following the imputation of 13 studies. However, this apparent “overestimation” likely reflects the fact that our meta-analysis only included studies that explicitly identified and reported these organisms at the species level. In practice, many studies that detected such bacteria may have reported them under broader categories—such as “anaerobic gram-positive cocci” or “streptococci”—or used updated taxonomic names (e.g., Parvimonas or S. anginosus). Consequently, these cases would be difficult to capture with our search and extraction strategy. Therefore, the true burden of these common oral microbes in OMSIs is likely underestimated in the existing literature.

Although MRSA remains a major global health threat,^77,78 our analysis reveals a low burden of MRSA in OMSIs. Among 1,082 cultured specimens, only 8 MRSA isolates were identified (0.23%; 95% CI: 0.00–1.49),^{14,19,21,23,30,34,40,42,52,59} a finding consistent with the recommendations in references^79,80 that routine empirical anti-MRSA therapy is generally unnecessary for most OMSI patients in the absence of specific risk factors. Nevertheless, when restricted to S. aureus isolates, the MRSA proportion was 19.18% (95% CI: 0.00–59.56). Thus, in cases of Ludwig’s angina where Gram staining shows gram-positive cocci in clusters, adding vancomycin—as suggested by the Sanford Guide to Antimicrobial Therapy—remains a reasonable precautionary measure.⁸¹ It should be noted that MRSA epidemiology exhibits heterogeneity across regions and settings, and clinical decisions should also be guided by local antimicrobial resistance patterns and institutional surveillance data.

Conclusion

This systematic review suggests that OMSIs are generally polymicrobial, with Streptococcus, Staphylococcus, and Prevotella among the pathogens most frequently identified. Levofloxacin and cephalosporins appear to have the lowest resistance rates among the antimicrobial agents evaluated, whereas penicillin, erythromycin, and metronidazole tend to show higher resistance rates. MRSA remains uncommon in OMSIs, consistent with current guidance advising against routine anti-MRSA therapy. A higher prevalence of Klebsiella has been observed in diabetic patients, which may indicate a potential need for broader gram-negative coverage in this group; however, this finding is based on a limited number of included studies, and further high-quality prospective research across different geographic regions would be helpful to better understand this association. In light of geographic variation in resistance patterns, limited susceptibility data for antibiotics, and heterogeneity across studies, empirical antibiotic choices might best be informed by antibiograms, the likely source of infection, and relevant comorbidities. Conventional culture-based methods may underestimate fastidious or unculturable anaerobes; therefore, future high-quality, population-based studies should prioritize integrating NGS with traditional methods to enable a more comprehensive understanding of the polymicrobial epidemiology of OMSIs and to inform evidence-based empirical antibiotic strategies in diverse clinical settings.

Authors’ Contributions

Y.L. conceived and designed the study, analyzed the data, wrote the article, and reviewed the article. L.Y. and C.W. analyzed the data and reviewed the article. All authors read and approved the final article.

Footnotes

Disclosure Statement

The authors have no relevant financial or nonfinancial interests to disclose.

Funding Information

The authors declare that no funds, grants, or other support were received during the preparation of this article.

Supplemental Material

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