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Ophthalmology and Therapy

, Volume 7, Issue 2, pp 417–429 | Cite as

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

  • Penny A. Asbell
  • Rahul T. Pandit
  • Christine M. Sanfilippo
Open Access
Brief Report

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.

Keywords

Antibiotic resistance ARMOR Besifloxacin Geographic region Ocular infections Ocular pathogens Surveillance study 

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 [2, 3, 4]. 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 [7, 8, 9]. 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.
Fig. 1

Distribution of ARMOR isolates by geographic region. Northeast: Connecticut, Delaware, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. Midwest: Illinois, Indiana, Iowa, Kansas, Kentucky, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. South: Alabama, Arkansas, Florida, Georgia, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia. West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming

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).
Fig. 2

Methicillin resistance by geographic region for A Staphylococcus aureus and B CoNS. Horizontal lines represent significant pairwise comparisons. CoNS coagulase-negative staphylococci

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).
Fig. 3

Mean percentage resistance by geographic region for A Staphylococcus aureus, B Streptococcus pneumoniae, C Pseudomonas aeruginosa, D CoNS, and E Haemophilus influenzae. *Tukey’s test performed using a p < 0.05 criterion for statistical significance; bars sharing the same letter (a, b, c) are not significantly different. ANOVA analysis of variance; CoNS coagulase-negative staphylococci; SEM standard error of the mean

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, 13, 14, 15, 18, 19, 20, 21, 22]. 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 [23, 24, 25]. 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.

Notes

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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Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Penny A. Asbell
    • 1
  • Rahul T. Pandit
    • 2
  • Christine M. Sanfilippo
    • 3
  1. 1.Cornea Service and Refractive Surgery CenterIcahn School of Medicine at Mount SinaiNew YorkUSA
  2. 2.Houston Methodist Eye AssociatesBlanton Eye InstituteHoustonUSA
  3. 3.Pharmaceutical Medical AffairsBausch + LombRochesterUSA

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