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Erschienen in: Ophthalmology and Therapy 2/2018

Open Access 09.08.2018 | Brief Report

Antibiotic Resistance Rates by Geographic Region Among Ocular Pathogens Collected During the ARMOR Surveillance Study

verfasst von: Penny A. Asbell, Rahul T. Pandit, Christine M. Sanfilippo

Erschienen in: Ophthalmology and Therapy | Ausgabe 2/2018

Abstract

Introduction

The Antibiotic Resistance Monitoring in Ocular micRoorganisms (ARMOR) study is an ongoing nationwide surveillance program that surveys in vitro antibiotic resistance rates and trends among ocular bacterial pathogens. We report resistance rates by geographic region for isolates collected from 2009 through 2016.

Methods

Staphylococcus aureus, coagulase-negative staphylococci (CoNS), Streptococcus pneumoniae, Haemophilus influenzae, and Pseudomonas aeruginosa isolates from ocular infections were collected at clinical centers across the US and categorized by geographic region based on state. Minimum inhibitory concentrations (MICs) for various antibiotics were determined at a central laboratory, and isolates were classified as susceptible or resistant based on established breakpoints. Geographic differences in methicillin resistance among staphylococci were evaluated by χ2 test with multiple comparisons, whereas geographic differences in mean percentage antibiotic resistance were evaluated by one-way analyses of variance and Tukey’s test.

Results

Overall, 4829 isolates (Midwest, 1886; West, 1167; Northeast, 1143; South, 633) were evaluated. Across all regions, azithromycin resistance was high among S. aureus (49.4–67.8%), CoNS (61.0–62.8%), and S. pneumoniae (22.3–48.7%), whereas fluoroquinolone resistance ranged from 26.1% to 47.8% among S. aureus and CoNS. Across all regions, all staphylococci were susceptible to vancomycin; besifloxacin MICs were similar to those of vancomycin. Geographic differences were observed for overall mean resistance among S. aureus, S. pneumoniae, and P. aeruginosa isolates (p ≤ 0.005); no regional differences were found among CoNS and H. influenzae isolates. Methicillin resistance in particular was higher among S. aureus isolates from the South and CoNS isolates from the Midwest (p ≤ 0.006).

Conclusion

This analysis of bacterial isolates from the ARMOR study demonstrated geographic variation in resistance rates among ocular isolates, with greater in vitro resistance apparent in the South and Midwest for some organisms. These data may inform clinicians in selecting appropriate treatment options for ocular infections.

Funding

Bausch & Lomb, Inc.
Hinweise

Enhanced digital features

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Introduction

Bacteria, including commensal species, can be associated with ocular infections including conjunctivitis, keratitis, blepharitis, uveitis, and endophthalmitis [1]. If left untreated, such infections may result in potentially serious consequences, including permanent loss of vision [24]. While antibiotics are commonly used to treat ocular infections, resistance to antibiotics is well known among ocular pathogens [1, 5, 6]. Infections due to antibiotic-resistant pathogens are difficult to treat, and understanding resistance and/or susceptibility patterns may guide the empirical treatment of ocular infections [79]. Microbial resistance or susceptibility can show geographic variation, highlighting the need to identify antibiotic resistance patterns by geographic region [4, 6, 10, 11].
Common ocular pathogens in the US include Staphylococcus aureus, coagulase-negative staphylococci (CoNS), Streptococcus pneumoniae, Pseudomonas aeruginosa, and Haemophilus influenzae [12]. The Antibiotic Resistance Monitoring in Ocular micRoorganisms (ARMOR) study is the only ongoing, prospective, multicenter, national surveillance study of antibiotic resistance patterns among bacterial isolates specific to ophthalmology in the US [9]. Each year since 2009, the ARMOR study has collected S. aureus, CoNS, S. pneumoniae, P. aeruginosa, and H. influenzae isolates from participating centers for antibiotic resistance monitoring. Overall 1-, 5-, and 7-year study outcomes have been reported [9, 13, 14].
The purpose of this analysis was to determine if the antibiotic susceptibility profiles of common ocular isolates vary by geography in the US. Here, we report antimicrobial resistance rates across the Northeast, Midwest, South, and West regions among isolates collected from 2009 through 2016 as part of the ARMOR study.

Methods

Participating centers across the US were invited to submit ocular isolates of S. aureus, CoNS, S. pneumoniae, H. influenzae, and P. aeruginosa cultured from 1 January 2009 through 31 December 2016 as part of the ongoing ARMOR study. As this was a laboratory study, patient informed consent and institutional review board approval were not required, and Health Insurance Portability and Accountability Act compliance did not apply because samples were taken as part of routine medical care, unrelated to the study, and no patient-identifying information was collected. The current study was not registered as a clinical trial since it does not contain any studies with human participants or animals performed by any of the authors.
Detailed ARMOR study methodology has been published previously [9, 13, 14]. Briefly, minimum inhibitory concentrations (MICs) of various antibiotics were determined by broth microdilution at a central laboratory, and MICs for 90% of isolates (MIC90s) were calculated. Systemic breakpoints, where available, were used to categorize isolates as resistant (includes intermediate and full resistance) or susceptible. Staphylococci were classified as methicillin-resistant (MR) or methicillin-susceptible (MS) based on oxacillin susceptibility.
For geographic analyses, isolates were categorized into Midwest, Northeast, South, and West regions based on the state of origin (Fig. 1). Differences in methicillin resistance among staphylococci by geography were determined by χ2 test followed by a multiple-comparisons test for proportions, using the p < 0.05 criterion for statistical significance. One-way analyses of variance (ANOVAs) were performed by geographic region using the means of the percentage of drug classes to which each isolate was resistant. In most cases a single surrogate antibiotic was chosen to determine sensitivity or resistance to a drug class. Drug classes analyzed (and their representative antibiotic) included fluoroquinolones (ciprofloxacin), macrolides (azithromycin), aminoglycosides (tobramycin), lincosamides (clindamycin), penicillins (oxacillin/penicillin), folate pathway inhibitors (trimethoprim), polypeptides (polymyxin B), phenicols (chloramphenicol), glycopeptides (vancomycin), and tetracyclines (tetracycline), where applicable by species. Tukey’s honestly significant difference test for pairwise differences (using the p < 0.1 criterion for statistical significance unless otherwise indicated) was performed when ANOVAs showed significance at the p < 0.05 level.

Results

A total of 4829 isolates were collected from 87 sites in 40 US states. Isolates included S. aureus (n = 1695), CoNS (n = 1475, including S. epidermidis [n = 1119]), S. pneumoniae (n = 474), H. influenzae (n = 586), and P. aeruginosa (n = 599). Of the isolates collected, 1886 (39.1%) originated from 32 sites in the Midwest, 1167 (24.2%) from 14 sites in the West, 1143 (23.7%) from 20 sites in the Northeast, and 633 (13.1%) from 21 sites in the South (Fig. 1). In vitro MIC90s and resistance profiles by geography are presented in Tables 1, 2, and 3.
Table 1
In vitro MIC90s (µg/ml) and resistance profiles for Staphylococcus aureus, MRSA, and MSSA
Organism
Antibiotic
Geographic region
West
Midwest
Northeast
South
n
%R
MIC90
n
%R
MIC90
n
%R
MIC90
n
%R
MIC90
S. aureus
Vancomycin
389
0.0
1
659
0.0
1
414
0.0
1
233
0.0
1
Besifloxacin
389
NA
0.5
659
NA
1
414
NA
1
233
NA
2
Moxifloxacin
389
26.5
4
659
33.7
4
414
33.8
8
233
45.1
8
Gatifloxacin
345
26.1
4
605
33.4
4
363
36.1
8
182
47.8
16
Ciprofloxacin
389
27.5
32
659
35.7
128
414
37.2
256
233
47.2
256
Levofloxacin
345
26.4
8
605
33.7
16
363
36.1
32
182
47.8
128
Ofloxacin
345
27.0
> 8
605
34.1
> 8
363
37.2
> 8
182
47.8
> 8
Clindamycin
389
15.7
> 2
659
17.2
> 2
414
13.8
> 2
233
12.9
> 2
Chloramphenicol
345
2.9
8
605
8.4
8
363
5.8
8
182
5.0
8
Azithromycin
389
49.4
> 512
659
62.5
> 512
414
64.0
> 512
233
67.8
> 512
Tobramycin
389
11.6
32
659
17.8
256
414
19.6
256
233
22.3
256
Tetracycline
148
2.0
0.5
188
6.9
0.5
74
2.7
0.5
9
0.0
Trimethoprim
345
2.6
2
605
3.6
4
363
4.1
2
182
10.4
16
MRSA
Vancomycin
95
0.0
1
264
0.0
1
149
0.0
1
113
0.0
1
Besifloxacin
95
NA
1
264
NA
1
149
NA
4
113
NA
2
Moxifloxacin
95
87.4
8
264
62.1
8
149
77.9
32
113
78.8
16
Gatifloxacin
81
85.2
8
241
62.2
8
135
79.3
64
86
83.7
16
Ciprofloxacin
95
88.4
256
264
65.5
256
149
80.5
256
113
81.4
256
Levofloxacin
81
86.4
32
241
62.7
32
135
80.0
256
86
83.7
128
Ofloxacin
81
86.4
64
241
63.5
> 8
135
80.0
> 8
86
83.7
> 8
Clindamycin
95
37.9
> 16
264
33.0
> 2
149
29.5
> 2
113
18.6
> 2
Chloramphenicol
81
6.2
8
241
13.3
16
135
11.1
16
86
5.8
8
Azithromycin
95
87.4
> 512
264
93.6
> 512
149
95.3
> 512
113
92.0
> 512
Tobramycin
95
35.8
> 256
264
38.6
256
149
49.0
> 256
113
40.7
256
Tetracycline
30
6.7
0.5
57
12.3
16
15
6.7
4
1
0.0
Trimethoprim
81
0.0
2
241
2.9
2
135
8.2
4
86
16.3
> 128
MSSA
Vancomycin
294
0.0
1
395
0.0
1
265
0.0
1
120
0.0
1
Besifloxacin
294
NA
0.06
395
NA
0.25
265
NA
0.25
120
NA
0.25
Moxifloxacin
294
6.8
0.12
395
14.7
2
265
9.1
0.5
120
13.3
1
Gatifloxacin
264
8.0
0.25
364
14.3
2
228
10.5
1
96
15.6
2
Ciprofloxacin
294
7.8
1
395
15.7
8
265
12.8
4
120
15.0
8
Levofloxacin
264
8.0
0.5
364
14.6
4
228
10.1
2
96
15.6
8
Ofloxacin
264
8.7
1
364
14.6
8
228
11.9
8
96
15.6
> 8
Clindamycin
294
8.5
0.25
395
6.6
0.25
265
4.9
0.12
120
7.5
0.25
Chloramphenicol
264
1.9
8
364
5.2
8
228
2.6
8
96
4.2
8
Azithromycin
294
37.1
> 512
395
41.8
> 512
265
46.4
> 512
120
45.0
> 512
Tobramycin
294
3.7
0.5
395
3.8
0.5
265
3.0
1
120
5.0
1
Tetracycline
118
0.9
0.5
131
4.6
0.5
59
1.7
0.5
8
0.0
Trimethoprim
264
3.4
2
364
4.1
4
228
1.8
2
96
5.2
2
< 10 isolates, %R percentage resistance (refers to all non-susceptible isolates), MIC90 minimum inhibitory concentration at which 90% of the isolates were inhibited, MRSA methicillin-resistant S. aureus, MSSA methicillin-susceptible S. aureus, NA interpretive breakpoints not available/not applicable
Table 2
In vitro MIC90s (µg/ml) and resistance profiles for CoNS, MRCoNS, and MSCoNS
Organism
Antibiotic
Geographic region
West
Midwest
Northeast
South
n
%R
MIC90
n
%R
MIC90
n
%R
MIC90
n
%R
MIC90
CoNS
Vancomycin
397
0.0
2
548
0.0
2
350
0.0
2
180
0.0
2
Besifloxacin
397
NA
2
548
NA
2
350
NA
2
180
NA
2
Moxifloxacin
397
27.2
8
548
30.8
16
350
31.4
16
180
40.0
16
Gatifloxacin
371
30.2
16
505
33.7
16
311
32.8
16
144
38.9
32
Ciprofloxacin
397
31.7
64
548
34.7
64
350
34.6
64
180
43.3
64
Levofloxacin
371
30.7
128
505
33.7
128
311
33.8
128
144
38.9
128
Ofloxacin
371
31.3
> 8
505
34.3
> 8
311
33.8
> 8
144
38.9
> 8
Clindamycin
397
25.4
> 2
548
27.6
8
350
26.9
> 2
180
21.7
> 2
Chloramphenicol
371
1.9
4
505
1.2
8
311
0.6
4
144
0.7
8
Azithromycin
397
61.0
> 512
548
61.0
> 512
350
61.1
> 512
180
62.8
> 512
Tobramycin
397
17.1
8
548
17.7
16
350
15.7
8
180
16.7
8
Tetracycline
154
17.5
> 16
157
10.2
8
68
14.7
8
2
0.0
Trimethoprim
371
27.5
256
505
26.1
> 128
311
29.3
> 128
144
32.6
> 128
MRCoNS
Vancomycin
175
0.0
2
295
0.0
2
155
0.0
2
92
0.0
2
Besifloxacin
175
NA
4
295
NA
4
155
NA
4
92
NA
4
Moxifloxacin
175
46.9
32
295
49.8
32
155
55.5
32
92
58.7
32
Gatifloxacin
162
53.1
32
269
55.0
32
135
58.5
64
75
60.0
32
Ciprofloxacin
175
55.4
64
295
56.3
64
155
60.7
64
92
64.1
64
Levofloxacin
162
54.3
256
269
55.4
128
135
60.0
256
75
60.0
128
Ofloxacin
162
54.3
16
269
56.1
32
135
60.0
> 8
75
60.0
> 8
Clindamycin
175
39.4
> 16
295
35.3
> 16
155
37.4
> 16
92
30.4
> 2
Chloramphenicol
162
1.2
8
269
1.9
8
135
1.5
8
75
1.3
8
Azithromycin
175
78.3
> 512
295
77.6
> 512
155
78.7
> 512
92
79.4
> 512
Tobramycin
175
28.0
16
295
28.5
32
155
28.4
32
92
23.9
16
Tetracycline
65
23.1
> 16
87
16.1
> 16
23
8.7
2
1
0.0
Trimethoprim
162
40.1
> 256
269
40.5
> 256
135
45.2
> 128
75
42.7
> 128
MSCoNS
Vancomycin
222
0.0
2
253
0.0
2
195
0.0
2
88
0.0
2
Besifloxacin
222
NA
0.25
253
NA
0.12
195
NA
0.25
88
NA
0.5
Moxifloxacin
222
11.7
1
253
8.7
0.25
195
12.3
1
88
20.5
2
Gatifloxacin
209
12.4
1
236
9.3
0.25
176
13.1
2
69
15.9
2
Ciprofloxacin
222
13.1
4
253
9.5
1
195
13.9
8
88
21.6
64
Levofloxacin
209
12.4
4
236
8.9
0.5
176
13.6
4
69
15.9
8
Ofloxacin
209
13.4
8
236
9.3
1
176
13.6
8
69
15.9
> 8
Clindamycin
222
14.4
1
253
18.6
> 2
195
18.5
2
88
12.5
1
Chloramphenicol
209
2.4
4
236
0.4
4
176
0.0
4
69
0.0
4
Azithromycin
222
47.3
> 512
253
41.5
> 512
195
47.2
> 512
88
45.5
> 512
Tobramycin
222
8.6
4
253
5.1
2
195
5.6
4
88
9.1
4
Tetracycline
89
13.5
> 16
70
2.9
1
45
17.8
8
1
0.0
Trimethoprim
209
17.7
256
236
9.8
8
176
17.1
64
69
21.7
> 128
< 10 isolates,  %R percentage resistance (refers to all non-susceptible isolates), CoNS coagulase-negative staphylococci, MIC90 minimum inhibitory concentration at which 90% of the isolates were inhibited, MRCoNS methicillin-resistant CoNS, MSCoNS methicillin-susceptible CoNS, NA interpretive breakpoints not available/not applicable
Table 3
In vitro MIC90s (µg/ml) and resistance profiles for Streptococcus pneumoniae, Pseudomonas aeruginosa, and Haemophilus influenzae
Organism
Antibiotic
Geographic region
West
Midwest
Northeast
South
n
%R
MIC90
n
%R
MIC90
n
%R
MIC90
n
%R
MIC90
S. pneumoniae
Besifloxacin
121
NA
0.06
191
NA
0.06
103
NA
0.12
59
NA
0.06
Moxifloxacin
121
0.0
0.12
191
0.0
0.25
103
1.0
0.25
59
0.0
0.12
Gatifloxacin
105
1.0
0.25
171
0.0
0.25
86
0.0
0.25
37
0.0
0.25
Ciprofloxacin
121
NA
1
191
NA
1
103
NA
2
59
NA
1
Levofloxacin
105
0.0
1
171
0.0
1
86
0.0
1
37
0.0
1
Ofloxacin
105
0.0
2
171
0.0
2
86
1.2
2
37
2.7
2
Chloramphenicol
121
4.1
4
191
2.1
4
103
2.9
4
59
0.0
2
Penicillina
121
22.3
0.25
191
41.9
1
103
30.1
1
59
33.9
2
Azithromycin
121
22.3
16
191
48.7
256
103
29.1
256
59
33.9
32
Tobramycin
121
NA
32
191
NA
32
103
NA
32
59
NA
32
Tetracycline
28
3.6
0.25
45
17.8
> 4
18
5.6
0.25
1
0.0
Trimethoprim
105
NA
128
171
NA
128
86
NA
64
37
NA
32
P. aeruginosa
Vancomycin
120
NA
> 16
186
NA
> 16
133
NA
> 16
60
NA
> 16
Besifloxacin
138
NA
2
215
NA
4
154
NA
4
92
NA
4
Moxifloxacin
138
NA
4
215
NA
4
154
NA
4
92
NA
4
Gatifloxacin
120
3.3
1
186
7.5
2
133
4.5
2
60
8.3
2
Ciprofloxacin
138
4.3
0.5
215
8.8
1
154
3.3
0.25
92
6.5
0.5
Levofloxacin
120
2.5
1
186
7.5
1
133
3.0
1
60
8.3
1
Ofloxacin
120
3.3
1
186
9.7
2
133
6.8
2
60
10.0
2
Azithromycin
138
NA
512
215
NA
512
154
NA
512
92
NA
512
Chloramphenicol
138
NA
128
215
NA
128
154
NA
128
92
NA
128
Polymyxin B
120
1.7
2
186
12.4
4
133
12.0
4
60
3.3
2
Tetracycline
50
NA
> 16
57
NA
16
36
NA
16
2
NA
Tobramycin
138
1.5
1
215
4.2
1
154
2.0
1
92
1.1
1
H. influenzae
Besifloxacin
122
NA
0.03
273
NA
0.03
122
NA
0.03
69
NA
0.03
Moxifloxacin
122
0.8
0.03
273
0.0
0.06
122
0.0
0.06
69
0.0
0.03
Gatifloxacin
108
0.9
0.015
252
0.0
0.015
108
0.0
0.015
45
0.0
0.015
Ciprofloxacin
122
0.8
0.015
273
0.0
0.015
122
0.0
0.015
69
0.0
0.015
Levofloxacin
108
0.9
0.03
252
0.0
0.03
108
0.0
0.03
45
0.0
0.015
Ofloxacin
108
0.9
0.06
252
0.0
0.03
108
0.0
0.06
45
0.0
0.03
Azithromycin
122
0.0
2
273
1.1
2
122
0.0
2
69
0.0
2
Chloramphenicol
122
0.0
0.5
273
0.7
0.5
122
0.8
0.5
69
0.0
1
Penicillin
122
NA
> 4
273
NA
> 4
122
NA
> 4
69
NA
> 4
Polymyxin B
108
NA
1
252
NA
2
108
NA
2
45
NA
2
Tetracycline
53
0.0
0. 5
89
2.3
0.5
11
18.2
8
7
0.0
Tobramycin
122
NA
2
273
NA
2
122
NA
2
69
NA
4
< 10 isolates,  %R percentage resistance (refers to all non-susceptible isolates), MIC90 minimum inhibitory concentration at which 90% of the isolates were inhibited, NA interpretive breakpoints not available/not applicable
aOral penicillin breakpoints applied
Compared with other antibiotics, S. aureus and CoNS isolates, especially the respective MR subsets, showed notable in vitro resistance to azithromycin and the fluoroquinolones (Tables 1 and 2). Among S. pneumoniae isolates, resistance was observed for azithromycin and penicillin, whereas resistance was low overall among P. aeruginosa isolates and negligible among H. influenzae isolates. Of the fluoroquinolones tested, besifloxacin, a chlorofluoroquinolone for which susceptibility breakpoints are not available, had the lowest MIC90 against staphylococcal (including MR isolates) and streptococcal isolates. Newer fluoroquinolones (besifloxacin, moxifloxacin, and gatifloxacin) generally had lower MIC90s compared with older fluoroquinolones (ofloxacin, ciprofloxacin, and levofloxacin). Ciprofloxacin had the lowest MIC90 against P. aeruginosa and, along with gatifloxacin, the lowest MIC90 against H. influenzae.
Among S. aureus and CoNS, 621 and 717 isolates were MR (MRSA and MRCoNS), whereas 1074 and 758 isolates were MS (MSSA and MSCoNS), respectively. Resistance to methicillin varied by geographic region among both S. aureus and CoNS isolates (p ≤ 0.006; Fig. 2). Among S. aureus isolates, the proportions of MRSA isolates were 48.5, 40.1%, 36.0%, and 24.4% in the South, Midwest, Northeast, and West, respectively, with pairwise differences observed between the South and Northeast and between the West and all other regions (Fig. 2A). The proportions of MRCoNS isolates were 53.8% in the Midwest, 51.1% in the South, 44.3% in the Northeast, and 44.1% in the West, with significant pairwise differences found between the Midwest and both the Northeast and West (Fig. 2B).
Analysis of the overall mean percentage of resistance showed variations based on the geographic region for S. aureus (p < 0.001), S. pneumoniae (p < 0.001), and P. aeruginosa (p = 0.005), despite low overall resistance for P. aeruginosa (Fig. 3). Among S. aureus isolates, mean [standard error (SE)] percentage of resistance was highest in the South [28.1% (1.5%)] and lowest in the West [16.8% (1.1%); Fig. 3A]. Among S. pneumoniae isolates, mean (SE) percentage of resistance was 14.5% (1.0%), 11.9% (1.8%), 9.9% (1.4%), and 7.6% (1.3%) in the Midwest, South, Northeast, and West, respectively, with pairwise differences observed between the Midwest and both the Northeast and West (Fig. 3B). For P. aeruginosa isolates, the mean (SE) percentage of resistance was 8.5% (1.1%), 5.4% (1.3%), 3.6% (1.6%), and 2.9% (1.4%) in the Midwest, Northeast, South, and West, with pairwise differences observed between the Midwest and both the South and West (Fig. 3C). No regional differences in overall mean resistance rates were observed among CoNS (Fig. 3D) or H. influenzae isolates (both p > 0.05; Fig. 3E).

Discussion

The ARMOR study continues to provide important insights on in vitro antibiotic resistance among ocular pathogens in the US. The current analysis provides information on antibiotic resistance trends by geographic region among ARMOR pathogens isolated from ocular infections and expands upon the findings reported previously for the 5-year cumulative ARMOR data set through inclusion of an additional 1600 isolates collected in the 3 ensuing years from 15 additional clinical sites.
Overall, and consistent with previous reporting, analysis of the current cumulative data set highlights relatively high in vitro antibiotic resistance among staphylococci to methicillin, azithromycin, and fluoroquinolones across the various geographies [9, 13, 14]. Methicillin-resistant staphylococcal isolates showed the highest resistance rates, a finding that has been corroborated in other studies [6, 15, 16]. In contrast, but as expected based on the previous analysis, in vitro resistance among S. pneumoniae isolates appeared lower and largely limited to azithromycin and penicillin, and there was low-to-minimal in vitro resistance among P. aeruginosa and H. influenzae isolates. Specific analysis by geography showed that resistance to methicillin varied by region, with the highest resistance among S. aureus isolates in the South and CoNS isolates in both the Midwest and South. The findings for S. aureus isolates are consistent with those reported by Blanco et al., who observed higher methicillin resistance among S. aureus isolates from the South [17]. While the geographic trend for resistance among S. aureus isolates is consistent with the 5-year ARMOR results, methicillin resistance was slightly lower in S. aureus in the current analyses (36.6%) than in the 5-year analysis (42.2%) [14]. This decrease is not unexpected given that a decrease in methicillin resistance over time was observed in the 7-year ARMOR results [9]. Further differences by geography were found for overall mean percentage of resistance among S. aureus, S. pneumoniae, and P. aeruginosa isolates, with the highest rates in the South for S. aureus and the Midwest for both S. pneumoniae and P. aeruginosa. General geographic trends observed with S. pneumoniae and P. aeruginosa showed high resistance rates in the Midwest, similar to that reported in the 5-year findings [14].
Comparisons of cumulative MIC90s showed wide variations among fluoroquinolones, particularly against staphylococci, with newer fluoroquinolones having lower MIC90s than older fluoroquinolones and besifloxacin having an MIC90 most comparable to that of vancomycin. Although not analyzed, MIC90s did not appear to differ by region and were consistent (within few dilutions) with the previous reports of ARMOR, other single-study reports of ocular isolates, and national systemic surveys [9, 1315, 1822]. Besifloxacin, a chlorofluoroquinolone for which interpretive breakpoints are not available, was approved by the US Food and Drug Administration for use in 2009 [19], and in vitro MIC90s have not varied substantially since its introduction [9, 13, 14, 19]. Compared with other fluoroquinolones, besifloxacin has more balanced targeting of DNA gyrase and topoisomerase IV; this, in turn, results in the need for multistep mutations and reduces the possibility of spontaneous resistance [2325]. Furthermore, besifloxacin may have a lower incidence of resistance development due to its use being limited to topical ophthalmic infections only, although cross-resistance from other fluoroquinolones is possible [26].
Although the literature contains antibiotic resistance data by geography for systemic infections [11, 27, 28], very few studies are available that describe geographic differences in antibiotic resistance rates among ocular pathogens [14, 16]. A prospective cohort study of systemic MRSA infections from 20 sites across the US suggested that meteorologic factors and geographic location play a role in MRSA colonization [17]. The study results indicated a negative association between latitude and colonization (p = 0.001), with MRSA colonization being higher in the South than in the North [17]. It follows that these factors may be associated with colonization of other microorganisms as well. Overuse and inappropriate prescribing have been associated with the crisis of antibiotic resistance [29]. Variations in the prescribing patterns of antibiotics may be associated with the differences in antibiotic resistance rates across geographies.
Limitations of this study include potential sampling bias owing to the practice of infrequent culturing of bacteria involved in ocular infections. In the absence of specific breakpoints for ocular isolates, systemic criteria were used to interpret MIC data, which may be of limited value given expected differences in antibiotic concentrations achieved following topical versus systemic administration. Moreover, not all topical ophthalmic antibiotics could be included, and one may debate the choice of antibiotics tested. Identification of the reasons for underlying geographic variability in resistance rates was outside the scope of this study. A limitation specific to this analysis is the subjective delineation of the geographic regions, implemented for comparison with previously published data [14]. Alternate regional divisions were possible with more evenly matched numbers of participating sites, further lessening potential sampling bias.

Conclusions

Findings from the ARMOR study suggest that in vitro antibiotic resistance rates among ocular S. aureus, S. pneumoniae, and P. aeruginosa isolates vary across different regions of the US, with the South and Midwest identified as regions of potential resistance concerns. Data related to geographic distribution of resistant ocular microorganisms may be useful during empirical prescription of antibiotics.

Acknowledgements

Funding

The ARMOR study is funded by Bausch & Lomb, Inc., which was responsible for the design and conduct of the study; data collection, management, analysis, and interpretation; preparation, review, and approval of the manuscript; the decision to submit the manuscript for publication; and funding the journal’s article processing charges. All authors had full access to all of the data in this study and take complete responsibility for the integrity of the data and accuracy of the data analysis.

Medical Writing

Medical writing support was provided by Lakshya Untwal of Cactus Communications Pvt. Ltd. (Mumbai, India) and was funded by Bausch & Lomb.

Authorship

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Disclosures

Penny A. Asbell is a consultant for Perrigo and Kurobe, has participated in advisory boards for Bausch & Lomb, and is a speaker on continuing medical education topics at professional meetings for Vindico. Rahul T. Pandit is a consultant for Shire and is a speaker for Bausch & Lomb, Carl Zeiss Meditec, and Johnson & Johnson Vision. Christine M. Sanfilippo is an employee of Bausch & Lomb.

Compliance with Ethics Guidelines

This article does not contain any studies with human participants or animals performed by any of the authors.

Data Availability

The data sets from the current study are available from the corresponding author on reasonable request.

Open Access

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://​creativecommons.​org/​licenses/​by-nc/​4.​0/​), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Metadaten
Titel
Antibiotic Resistance Rates by Geographic Region Among Ocular Pathogens Collected During the ARMOR Surveillance Study
verfasst von
Penny A. Asbell
Rahul T. Pandit
Christine M. Sanfilippo
Publikationsdatum
09.08.2018
Verlag
Springer Healthcare
Erschienen in
Ophthalmology and Therapy / Ausgabe 2/2018
Print ISSN: 2193-8245
Elektronische ISSN: 2193-6528
DOI
https://doi.org/10.1007/s40123-018-0141-y

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