Abstract
Bacteremia caused by MRSA with reduced vancomycin susceptibility (MRSA-RVS) frequently resulted in treatment failure and mortality. The relation of bacterial factors and unfavorable outcomes remains controversial. We retrospectively reviewed clinical data of patients with bacteremia caused by MRSA with vancomycin MIC = 2 mg/L from 2009 to 2012. The significance of bacterial genotypes, agr function and heterogeneous vancomycin-intermediate S. aureus (hIVSA) phenotype in predicting outcomes were determined after clinical covariates adjustment with multivariate analysis. A total of 147 patients with mean age of 63.5 (±18.1) years were included. Seventy-nine (53.7%) patients failed treatment. Forty-seven (31.9%) patients died within 30 days of onset of MRSA bacteremia. The Charlson index, Pitt bacteremia score and definitive antibiotic regimen were independent factors significantly associated with either treatment failure or mortality. The hVISA phenotype was a potential risk factor predicting treatment failure (adjusted odds ratio 2.420, 95% confidence interval 0.946–6.191, P = 0.0652). No bacterial factors were significantly associated with 30-day mortality. In conclusion, the comorbidities, disease severity and antibiotic regimen remained the most relevant factors predicting treatment failure and 30-day mortality in patients with MRSA-RVS bacteremia. hIVSA phenotype was the only bacterial factor potentially associated with unfavorable outcome in this cohort.
Introduction
MRSA with reduced vancomycin susceptibility phenotype (MRSA-RVS) including heterogeneous vancomycin-intermediate S. aureus (hVISA) and vancomycin-susceptible S. aureus (VSSA) with elevated vancomycin MIC (MIC ≥ 1.5 mg/L) have been increasingly reported in the past decade1–5. Although the vancomycin MIC of 1.5–2 mg/L remains in the susceptible range, observational studies in the past few years have demonstrated that infections with MRSA-RVS were significantly associated with treatment failure and poor outcomes when treated with vancomycin5–8. A meta-analysis showed that high vancomycin MIC was significantly associated with treatment failure irrespective of the source of infection or MIC methodology9. Another systemic review further demonstrated that the hVISA phenotype was associated with increased incidence of treatment failure, though the 30-day mortality was similar for hVISA and VSSA infections10. These results suggested that the RVS phenotype is an independent factor associated with poor treatment response in patients with MRSA infections.
Intriguingly, it appears that the poor treatment response of patients could not simply be explained by inadequate treatment with vancomycin. Holmes et al. demonstrated that RVS phenotype can be significantly associated with increased mortality in patients with methicillin-susceptible S. aureus bacteremia who were treated with beta-lactams11. The underlying mechanism of the association between RVS and poor outcome is not clear and may involve a variety of bacterial genetic or phenotypic features other than drug susceptibility. Understanding of the mechanism will help develop strategies against this medically important pathogen. The accessory gene regulator (agr) is a well-studied global regulator of S. aureus, which controls the expression of a wide array of virulence factors and surface proteins. The agr locus was frequently mutated and its function is often compromised in S. aureus with incremental non-susceptibility to vancomycin12,13. The study was aimed to identify factors predicting treatment failure and adverse outcomes in patients with bacteremia caused by MRSA with a vancomycin MIC of 2 mg/L, with focus on a variety of bacterial factors including genotypes, hVISA phenotype and agr function.
Results
Patient features and outcomes
A total of 147 episodes of bacteremia caused by MRSA with vancomycin MIC of 2 mg/L were identified in 147 patients in this study. The mean ± standard deviation age was 63.5 ± 18.1 years and male gender accounted for 61.2% (90/147) of the cases. Among the147 episodes, 41 (27.9%) episodes were acquired in the community and only 9 (6.12%) episodes occurred in patients without any healthcare-associated risk. Catheter (38.1%) was the most common source of bacteremia. This was followed by pneumonia (23.1%) and skin/soft tissue infections (16.3%). Common comorbidities included renal insufficiency (47.6%), end-stage renal disease requiring dialysis (45.6%), and hypertension (44.9%). Empirical antibiotics with activity against MRSA were administered to 55 (37.4%) cases. Glycopeptides remained to be the most commonly used definitive antimicrobial agent (54.4%, 80/147). Daptomycin was used as the definitive agent in 55(37.4%) of the patients. Sixty-eight patients (46.3%) were treated successfully. The 30-day mortality was 32.0% (47/147) in this cohort. The demographics and clinical features of the 147 patients are summarized in Table 1.
Table 1.
Univariate analysis of clinical parameters associated with treatment failure and 30-day mortality in 147 episodes of bacteremia due to methicillin-resistant Staphylococcus aureus (MRSA) with vancomycin MIC of >1.5 mg/L in a teaching hospital in Taiwan, 2009–2011.
| Factor | Total (n = 147) |
Treatment response | 30-day outcome | ||||
|---|---|---|---|---|---|---|---|
| Failure (n = 79) |
Success (n = 68) |
P value | Died (n = 47) |
Survived (n = 100) |
P value | ||
| Demographics and medical history | |||||||
| Female gender (%) | 57 (38.8) | 30 (38.0) | 27 (39.7) | 0.8299 | 18 (38.3) | 39 (39.0) | 0.9351 |
| Age in years (SD) | 63.5 (18.1) | 65.7 (16.5) | 60.9 (19.5) | 0.1078 | 68.7 (16.2) | 61.0 (18.5) | 0.0161 |
| Previous S. aureus infections (%) | 68 (46.3) | 41 (51.9) | 27 (39.7) | 0.1393 | 20 (42.6) | 48 (48.0) | 0.5368 |
| glycopeptide exposure within 3 mo | 61 (41.5) | 38 (48.1) | 23 (33.8) | 0.0798 | 19 (40.4) | 42 (42.0) | 0.8566 |
| Community-acquired infections | 41 (27.9) | 16 (20.3) | 25 (36.8) | 0.0260 | 7 (14.9) | 34 (34.0) | 0.0160 |
| Source of bacteremia (concomitant infections) | |||||||
| Catheter-associated (%) | 56 (38.1) | 31 (39.2) | 25 (36.8) | 0.7579 | 20 (42.6) | 36 (36.0) | 0.4454 |
| Bone/joint infections (%) | 19 (12.9) | 9 (11.4) | 10 (14.7) | 0.5505 | 1 (2.13) | 18 (18.0) | 0.0075 |
| Cardiovascular infections (%) | 8 (5.44) | 5 (6.33) | 3 (4.41) | 0.7253* | 2 (4.26) | 6 (6.00) | 1.000* |
| Skin and soft tissue infections (%) | 24 (16.3) | 8 (10.1) | 16 (23.5) | 0.0284 | 5 (10.6) | 19 (19.0) | 0.2008 |
| Pneumonia (%) | 34 (23.1) | 24 (30.4) | 10 (14.7) | 0.0246 | 17 (36.2) | 17 (17.0) | 0.0101 |
| Intra-abdominal infections (%) | 1 (0.68) | 0 (0) | 1 (1.47) | 0.4626* | 0 (0) | 1 (1.00) | 1.000* |
| Urinary tract infections (%) | 3 (2.04) | 0 (0) | 3 (4.41) | 0.0966* | 0 (0) | 3 (3.00) | 0.5514* |
| Unknown (%) | 23 (15.7) | 13 (16.5) | 10 (14.7) | 0.7709 | 10 (21.3) | 13 (13.0) | 0.1977 |
| Comorbidities | |||||||
| Charlson index, median (range) | 5 (0–14) | 6 (0–14) | 4 (0–12) | 0.0027# | 6 (0–14) | 4 (0–12) | 0.0107# |
| DM without end organ damage (%) | 21 (14.3) | 10 (12.7) | 11 (16.2) | 0.5433 | 5 (10.6) | 16 (16.0) | 0.3863 |
| DM with end organ damage (%) | 37 (25.2) | 23 (29.1) | 14 (20.6) | 0.2350 | 10 (21.3) | 27 (27.0) | 0.4558 |
| Hypertension (%) | 66 (44.9) | 32 (40.5) | 34 (50.0) | 0.2486 | 17 (36.2) | 49 (49.0) | 0.1447 |
| Heart failure (%) | 26 (17.7) | 21 (26.6) | 5 (7.35) | 0.0023 | 9 (19.2) | 17 (17.0) | 0.7501 |
| History of myocardial infarction (%) | 6 (4.08) | 3 (3.80) | 3 (4.41) | 1.000* | 2 (4.26) | 4 (4.00) | 1.000* |
| Peripheral vascular disease (%) | 16 (10.9) | 10 (12.7) | 6 (8.82) | 0.4567 | 6 (12.8) | 10 (10.0) | 0.6155 |
| COPD (%) | 7 (4.76) | 5 (6.33) | 2 (2.94) | 0.4512* | 4 (8.51) | 3 (3.00) | 0.2105* |
| Mild hepatic dysfunction (%) | 9 (6.12) | 6 (7.59) | 3 (4.41) | 0.5056* | 1 (2.13) | 8 (8.00) | 0.2724* |
| Moderate/severe liver diseases (%) | 20 (13.6) | 13 (16.5) | 7 (10.3) | 0.2773 | 11 (23.4) | 9 (9.00) | 0.0175 |
| Renal insufficiency (%) | 70 (47.6) | 40 (50.6) | 30 (44.1) | 0.4303 | 21 (44.7) | 49 (49.0) | 0.6248 |
| On dialysis (%) | 67 (45.6) | 41 (51.9) | 26 (38.2) | 0.0972 | 23 (48.9) | 44 (44.0) | 0.5752 |
| History of CVA (%) | 30 (20.4) | 21 (26.6) | 9 (13.2) | 0.0453 | 11 (23.4) | 19 (19.0) | 0.5366 |
| Hemiplegia (%) | 25 (17.0) | 16 (20.3) | 9 (13.2) | 0.2588 | 7 (14.9) | 18 (18.0) | 0.6401 |
| Dementia (%) | 21 (14.3) | 12 (15.2) | 9 (13.2) | 0.7356 | 7 (14.9) | 14 (14.0) | 0.8852 |
| Peptic ulcer (%) | 23 (15.7) | 15 (19.0) | 8 (11.8) | 0.2294 | 13 (27.7) | 10 (10.0) | 0.0060 |
| Immunosuppressive therapy (%) | 9 (6.12) | 8 (10.1) | 1 (1.47) | 0.0380* | 5 (10.6) | 4 (4.00) | 0.1453 |
| Organ transplant (%) | 1 (0.68) | 0 (0) | 1 (1.47) | 0.4626* | 0 (0) | 1 (1.00) | 1.000* |
| Connective tissue diseases (%) | 2 (1.36) | 2 (2.53) | 0 (0) | 0.4994* | 1 (2.13) | 1 (1.00) | 0.5387* |
| Malignant lymphoma/leukemia (%) | 4 (2.72) | 3 (3.80) | 1 (1.47) | 0.6241 | 2 (4.26) | 2 (2.00) | 0.5930* |
| Tumor without metastasis (%) | 15 (10.2) | 6 (7.59) | 9 (13.2) | 0.2600 | 5 (10.6) | 10 (10.0) | 1.000* |
| Metastatic solid tumor (%) | 20 (13.6) | 14 (17.7) | 6 (8.82) | 0.1167 | 12 (25.5) | 8 (8.00) | 0.0038 |
| Surgery requiring hospitalization in previous 30 days (%) | 16 (10.9) | 8 (10.1) | 8 (11.8) | 0.7505 | 4 (8.51) | 12 (12.0) | 0.5264 |
| Condition at disease onset | |||||||
| Bacteremia occurring in ICU | 35 (23.8) | 22 (27.9) | 13 (19.1) | 0.2153 | 20 (42.6) | 15 (15.0) | 0.0003 |
| Pitt bacteremia score, median (range) | 2 (0–12) | 4 (0–12) | 1 (0–7) | <0.0001# | 5 (0–12) | 1 (0–10) | <0.0001# |
| Laboratory data at bacteremia onset | |||||||
| Hemoglobin, g/dL (SD) | 9.60 (1.74) | 9.62 (1.83) | 9.58 (1.65) | 0.8680 | 9.55 (1.61) | 9.63 (1.81) | 0.7908 |
| WBC, /μl (SD) | 13,489 (8138) | 14,145 (8942) | 12,756 (7130) | 0.3079 | 15302 (8747) | 12665 (7197) | 0.1090 |
| Platelet, /μl (SD) | 184K (102K) | 169K (148K) | 201K (114K) | 0.0678 | 158K (101K) | 196K (101K) | 0.0346 |
| C-reactive protein, mg/L (SD) | 118 (82) | 125 (83) | 111 (114) | 0.4702 | 142 (117) | 106 (90) | 0.0127 |
| Managements | |||||||
| Use of permanent catheter | 37 (25.2) | 23 (29.1) | 14 (20.6) | 0.2350 | 12 (25.5) | 25 (25.0) | 0.9447 |
| Removal of catheter during bacteremia | 21 (14.3) | 13 (16.5) | 8 (11.8) | 0.4177 | 7 (14.9) | 14 (14.0) | 0.8852 |
| Empirical use of antibiotics with activity against MRSA | 55 (37.4) | 33 (41.8) | 22 (32.4) | 0.2393 | 19 (40.4) | 36 (36.0) | 0.6051 |
| Definite regimen | 0.2645 | 0.0149 | |||||
| Daptomycin | 55 (37.4) | 27 (34.2) | 28 (41.2) | 13 (27.7) | 42 (42.0) | ||
| Glycopeptide | 80 (54.4) | 43 (54.4) | 37 (54.4) | 26 (55.3) | 54 (54.0) | ||
| Others | 12 (8.16) | 9 (11.4) | 3 (4.41) | 8 (17.0) | 4 (4.00) | ||
*Fisher’s exact test.
#Wilcoxon test.
Abbreviations: CVA, cerebral vascular accident; hVISA, heterogeneous vancomycin-intermediate Staphylococcus aureus; VSSA, vancomycin-susceptible S. aureus; COPD, chronic obstructive pulmonary disease; ICU, intensive care unit;.
Strains characteristics
SCCmec type III and agr type 1 were the most common genotypes and identified in 105 (71.4%) isolates and 123 (83.7%) isolates, respectively. Community genotypes, including type IV or V SCCmec, were identified in 23 (15.6%) isolates. The hVISA phenotype was detected by GRD E-test method in 37.4% of the isolates. The agr function was intact in 106 (72.1%) isolates. The detailed characteristics of all isolates are shown in Table 2.
Table 2.
Univariate analysis of microbiological parameters associated with treatment failure and 30-day mortality in 147 episodes of bacteremia due to MRSA with vancomycin MIC = 2 mg/L in a teaching hospital in Taiwan, 2009–2011.
| Factor | Total (n = 147) | Treatment response | 30-day outcome | ||||
|---|---|---|---|---|---|---|---|
| Failure (n = 79) |
Success (n = 68) |
P value | Died | Survived | P value | ||
| Resistance to | |||||||
| Daptomycin (%) | 11 (7.48) | 8 (10.1) | 3 (4.41) | 0.1892 | 4 (8.51) | 7 (7.00) | 0.7448 |
| Fusidic acid (%) | 11 (7.48) | 6 (7.59) | 5 (7.35) | 0.9577 | 3 (6.38) | 8 (8.00) | 1.000* |
| Linezolid (%) | 0 (0) | 0 (0) | 0 (0) | … | 0 (0) | 0 (0) | … |
| TMP-SXT (%) | 108 (73.5) | 62 (78.5) | 46 (67.7) | 0.1380 | 37 (78.7) | 71 (71.0) | 0.3226 |
| Teicoplanin (%) | 2 (1.36) | 0 (0) | 2 (2.94) | 0.2123 | 0 (0) | 2 (2.00) | 1.000* |
| Tigecycline (%) | 5 (3.40) | 4 (5.06) | 1 (1.47) | 0.3735 | 3 (6.38) | 2 (2.00) | 0.3276 |
| Rifampin (%) | 38 (25.9) | 23 (29.1) | 15 (22.1) | 0.3300 | 16 (34.0) | 22 (22.0) | 0.1199 |
| hVISA phenotype | 55 (37.4) | 38 (48.1) | 17 (25.0) | 0.0039 | 24 (51.1) | 31 (31.0) | 0.0191 |
| Delta-hemolysis | 106 (72.1) | 55 (69.6) | 51 (75.0) | 0.4683 | 31 (66.0) | 75 (75.0) | 0.2542 |
| Agr type | 0.2533 | 0.6534 | |||||
| Type 1 | 123 (83.7) | 69 (87.3) | 54 (79.4) | 41 (87.2) | 82 (82.0) | ||
| Type 2 | 21 (14.3) | 10 (12.7) | 11 (16.2) | 6 (12.8) | 15 (15.0) | ||
| Type 4 | 2 (1.36) | 0 (0) | 2 (2.94) | 0 (0) | 2 (2.00) | ||
| untypable | 1 (0.68) | 0 (0) | 1 (2.94) | 0 (0) | 1 (1.00) | ||
| SCCmec type | 0.0072 | 0.2801 | |||||
| II | 16 (10.9) | 10 (12.7) | 6 (8.82) | 6 (12.8) | 10 (10.0) | ||
| III | 105 (71.4) | 63 (79.8) | 42 (61.8) | 37 (78.7) | 68 (68.0) | ||
| IV | 19 (12.9) | 6 (7.59) | 13 (19.1) | 4 (8.51) | 15 (15.0) | ||
| V | 4 (2.72) | 0 (0) | 4 (5.88) | 0 (0) | 4 (4.00) | ||
| untypable | 3 (2.04) | 0 (0) | 3 (4.41) | 0 (0) | 3 (3.00) | ||
| PVL genes | 1 (0.68) | 0 (0) | 1 (1.47) | 0.4626* | 0 (0) | 1 (1.00) | 1.000* |
*Fisher’s exact test.
Factors associated with treatment failure
The bacterial factors including susceptibilities to various antibiotics, agr type and agr function were not associated with treatment response (Table 2). Univariate analysis disclosed that the hVISA phenotype was more commonly identified in patients with treatment failure compared to those who were treated successfully (48.1% versus 25.0%, P = 0.0039). The distributions of SCCmec types also differed in patients with different treatment responses (P = 0.0072, Table 2). After adjustment for clinical covariates, the only bacterial factor associated with increased incidence of treatment failure was the hVISA phenotype (adjusted odds ratio [aOR] 2.420, 95% confidence interval [CI] 0.946–6.191) though with marginal significance (P = 0.0652). Multivariate analysis revealed that the Charlson index (aOR 1.154, 95% CI 1.009–1.320, P = 0.0363) and Pitt bacteremia score (aOR 1.391, 95% CI 1.162–1.666, P = 0.0003) were independent clinical factors associated with increased incidence of treatment failure (Table 3).
Table 3.
Multivariate analysis of factors associated with treatment failure and 30-day mortality in patients with bacteremia due to MRSA with vancomycin MIC of 2 mg/L.
| Factor | Treatment failure | 30-day mortality | ||
|---|---|---|---|---|
| aOR (95% CI) | P value | aOR (95% CI) | P value | |
| Age | — | — | 1.030 (0.999–1.062) | 0.0573 |
| Community-acquired infections | 0.762 (0.298–1.946) | 0.5694 | 2.324 (0.646–83.61) | 0.1969 |
| Bone/joint infections | – | — | 4.834 (0.393–59.438) | 0.2184 |
| Skin and soft tissue infections | 0.417 (0.136–1.280) | 0.1262 | — | — |
| Pneumonia | 1.260 (0.435–3.650) | 0.6700 | 1.436 (0.482–4.277) | 0.5159 |
| Charlson index | 1.154 (1.009–1.320) | 0.0363 | 1.108 (0.951–1.290) | 0.1873 |
| Bacteremia occurring in ICU | — | — | 2.227 (0.703–7.055) | 0.1736 |
| Pitt bacteremia score | 1.391 (1.162–1.666) | 0.0003 | 1.298 (1.087–1.549) | 0.0040 |
| Platelet | — | — | 0.996 (0.991–1.002) | 0.2162 |
| C-reactive protein | — | — | 1.007 (1.001–1.013) | 0.0195 |
| Definite regimen | ||||
| daptomycin | — | — | 0.128 (0.022–0.746) | 0.0286 |
| glycopeptide | — | — | 0.238 (0.046–1.246) | 0.4597 |
| others | — | — | Referent | — |
| SCCmec | ||||
| Type II | Referent | — | — | — |
| Type III | 1.065 (0.278–4.075) | 0.9412 | — | — |
| Type IV | 1.082 (0.199–5.879) | 0.9410 | — | — |
| Type V | — | 0.9676 | — | — |
| Non-typable | — | 0.9716 | — | — |
| hVISA phenotype | 2.420 (0.946–6.191) | 0.0652 | 1.698 (0.623–4.631) | 0.3011 |
Factors associated with 30-day mortality
Multivariate analysis disclosed that the Pitt bacteremia score and C-reactive protein value at disease onset were independent factors associated with increased incidence of 30-day mortality (aOR 1.298, P = 0.0040 and aOR 1.007, P = 0.0195, respectively). The use of daptomycin as definitive antimicrobial therapy was independently associated with decreased incidence of 30-day mortality (aOR 0.128, 95% CI 0.022–0.746, P = 0.0286). There was no association between bacterial factors and 30-day mortality in this cohort.
Discussion
The significance of hVISA phenotype on treatment response and patient outcomes had been addressed in previous studies involving bacteremic patients3,7,13–15. Data from these studies suggested that the patients with hVISA bacteremia might have higher treatment failure rates compared to those with VSSA bacteremia, The incidence of adverse outcomes were however similar for these infections. In one prospective study Horne et al. reported that there is no significant difference in the cure rates of hVISA and VISA infections compared to VSSA infections8. The authors concluded that the laboratory identification of the MRSA-RVS might be of limited value and might be reserved for isolates from patients who are failing appropriate anti-MRSA therapy. However, a meta-analysis by van Hal et al. found out that the hVISA phenotype was associated with a 2.37-fold increased incidence of treatment failure. There was no association between hVISA and 30-day mortality10. Consistent with the observation by van Hal et al., results from our study showed that hVISA phenotype was associated with 2.42 folds increased risk of treatment failure. Unfortunately, the difference was of borderline significance which was most likely owing to relative small number of cases in both groups. Together, the difference in patient populations, methods used in the identification of hVISA phenotype (i.e. PAP-AUC, macromethod E-test and GRD E-test), and heterogeneity between patients infected with hVISA and VSSA may be factors responsible for the conflicting results.
It has been shown that MRSA with high vancomycin MIC (i.e. MIC = 2 g/L) was a factor significantly associated with poor outcome in MRSA bacteremic patients16. In the current study, in addition to the hVISA phenotype, we failed to identify any bacterial parameters such as antibiotic susceptibilities, SCCmec types, agr type and agr function (δ-hemolysis) that can predict the treatment failure and 30-day mortality when the clinical covariates were adequately controlled. Our data indicated that the clinical condition of the affected patients, particularly the comorbidity and severity at disease onset, remained to be the most critical factor predicting the unfavorable outcomes. This findings supports the current MRSA guideline which stated that the patient’s clinical response should be the primary consideration, irrespective of the MIC17.
It was interesting to note that the use of daptomycin as the definitive regimen was associated with decreased incidence of 30-mortality in this MRSA bacteremia cohort. This observation was in agreement with the results in a recent MRSA bacteremia cohort study which demonstrated a decrease 30-day mortality and persistent bacteremia in patients on early use of daptomycin18. However, this observation should be interpreted with caution since our study was not designed to address the efficacy of different therapeutic regimen on MRSA bacteremia. The significance of the role of daptomycin in decreased 30-day mortality may be due to other factors that were not considered in this study. For instance, daptomycin was usually used as an alternative agent to glycopeptide in MRSA bacteremic patients. It was likely that those survived 30 days after disease onset might have greater chance of receiving daptomycin treatment. Nevertheless, given the high incidence of case fatality and the potential benefit of this agent in reducing mortality, the early use of antimicrobial agents alternative to glycopeptides should be seriously considered in those with multiple comorbidity and severe infections at disease onset (i.e. high Pitt bacteremia score and CRP).
There were limitations of the study. Firstly, we found out that there was a huge heterogeneity of clinical parameters between the two groups of patients. Heterogeneity of patient characteristics, thought having taken into consideration and adjusted by multivariate logistic regression method, might still exist and potentially skew our findings. A prospective randomized control study with sufficient number of participants will be the ultimate way to clarify the issue addressed in this study. Secondly, the sample size of the study was relative small which might not be able to precisely estimate the impact of certain clinical or bacterial parameters.
In conclusion, bacteremia due to MRSA with high vancomycin MIC is associated with extremely high incidences of treatment failure and adverse outcomes. Our data indicated that the underlying conditions, severity of infection at disease onset and antibiotic regimen remained the most critical factors predicting the patients’ outcomes. Our data also suggested that the hVISA phenotype was the only potential factor predicting treatment failure in this population. Based on these findings, we agree that detection of the hVISA phenotype might of clinical relevance and routine laboratory detection might be considered8,19.
Methods
Ethic statements
This study was approved by the institutional review boards (IRB) of Chang Gung Memorial Hospital at Linkou (GMH-Linkou). All experimental protocols were performed in accordance with the guidelines and regulations of the IRB of CGMH. A waiver of consent was granted given the retrospective nature of the project and anonymous analysis of the clinical information.
Study design
The study was conducted in a university-affiliated teaching hospital (CGMH-Linkou) in northern Taiwan from August 2009 to July 2012. The hospital is a 3,715-bed medical center and provided both primary and tertiary healthcare. It consists of 13 disease-oriented departments, including 26 intensive care units (ICUs) and 73 ordinary wards. A central microbiology laboratory was responsible for processing all clinical specimens. From August 01 2009, the microbiology laboratory began to report the vancomycin MICs values for invasive S. aureus isolates (from sterile sites, i.e. blood). The MICs were determined by the E-test method (BioMerieux, France). The S. aureus isolates were stored at −80 °C after their isolation and were accompanied by an electronic record containing the information of the isolates and the chart number of patients. We reviewed the clinical information of patients with MRSA bacteremia with a vancomycin MIC = 2 mg/L.
Demographics and anthropometric variables of subjects
Only the initial episodes of bacteremia during the study period was included for analysis to preserve the independence of observations. Positive blood cultures obtained from patients without consistent or persistent features of systemic infections were considered to have been either contaminated or transient bacteremia. Data for such patients were excluded from analysis.
A standardized data collection form was used to collect the medical information needed in this study (Table 1). We identified the possible sources of bacteremia (concomitant infections), comorbid illnesses, concurrent blood isolates of other bacterial species, clinical condition within 48 hours or on the day of onset of bacteremia, laboratory and image findings, ICU stay, empiric and definite antimicrobial regimens and a variety of unfavorable outcomes. Renal insufficiency was defined as a serum creatinine level ≥ 1.4 mg/dL without the requirement of hemodialysis. The sources of bacteremia were identified by reviewing the medical records, radiologic studies, surgical findings and microbiological records of the patients. The Charlson weighted index of comorbidity (WIC) and Pitt bacteremia score were calculated and used as parameters to control for comorbidity and severity of illness at the onset of bacteremia during the analysis of risks associated with treatment failure and 30-day mortality.
Appropriate definitive antimicrobial treatment was defined as antibiotics active against the offending pathogens started within 48 hours of onset of bacteremia and used for at least 3 days. Adjunctive therapy was defined as concurrent surgical intervention or palliative drainage. Treatment failure was defined as inadequate response to therapy, such as the development of resistance to glycopeptide; worsening, recurrent or new onset of signs and symptoms requiring a change of antibiotic regimen; or a positive blood culture for MRSA at the end of therapy. Mortality occurring within 7 days of S. aureus bacteremia was defined as bacteremia-attributed mortality if there was no other identified cause for death. The 30-day mortality was defined as any cause of death within 30 days of MRSA bacteremia onset.
Determination of hVISA phenotype
Etest GRD (bioMérieux, Marcy-l’Etoile, France) was used to determine the hVISA phenotype and performed according to manufacturer’s instructions20. Briefly, a bacterial suspension at a 0.5 McFarland standard was inoculated onto a MHA plate containing 5% sheep blood (BBL; Becton Dickinson, Cockeysville, MD) and incubated at 35 °C. The zone of inhibition was read at 24 and 48 h after incubation. An isolate was considered hVISA if the Etest GRD strip result was ≥8 mg/L for vancomycin or teicoplanin.
δ-Hemolysin production
Delta-hemolysin production is indicative of intact agr function. This was measured according to the procedure described elsewhere19. Briefly, the S. aureus isolates were streaked perpendicularly to RN4220 on Columbia agar plates (Oxoid, Cambridge, UK) with 5% sheep blood and incubated at 37 °C in 5% CO2 overnight. Synergistic hemolysis within the β-hemolysin zone produced by RN4220 was evaluated. The presence of synergistic hemolysis indicates the production of δ-hemolysin. ATCC 25923 and Mu50 were used as positive and negative controls, respectively, for the production of δ-hemolysin.
Genotyping of MRSA strains
Staphylococcal cassette chromosome mec element (SCCmec) typing of MRSA isolates was performed using a multiplex PCR strategy described in a previous study14. The control strains for SCCmec types I, II, III, IVa, and VT were S. aureus NCTC10442, N315, 85/2082, JCSC4744 and TSGH-17, respectively. SCCmec typing was determined by using a particular primer described elsewhere21. The presence of Panton-Valentine leukocidin (PVL) genes was determined in all S. aureus isolates by a PCR technique described by Lina et al.22. The agr type was determined as previously described23. The agr sequences were amplified with the following primers, Pan (5′-ATGCACATGGTGCACATGC-3′), agr1 (5′-GTCACAAGTACTATAAGCTGCGAT-3′), agr2 (5′-TATTAC TAATTGAAA AGTGGCCATAGC-3′), agr3 (5′-GTAATGTAATAGCTT GTATAATAATA CCCAG-3′) and agr4 (5′-CGATAATGCCGT AATACCCG-3′). These primers allowed amplification of a 441-bp DNA fragment from agr group I, 575-bp fragment from agr group II, 323-bp fragment from agr group III, and 659-bp DNA fragment from agr group IV strains.
Statistical analysis
Comparison of categorical variables between study groups was performed with a chi-square test or with the Fisher exact test where appropriate, whereas differences among the numerical variables were analyzed by two-sample t-test. Multiple logistic regression analysis was applied to explore factors associated with treatment failure and 30-day mortality. Statistic significance was defined as a P value of <0.05 in the tests. The statistics were performed using an SAS 9.3 for windows (SAS Institute, Inc., Cary, NC).
Acknowledgements
This work was supported by grants from Chang Gung Memorial Hospital (CMRPG3C1881, 1882 and 1883) and partly supported from Ministry of Science and Technology of Taiwan (103-2314-B-182A-058). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank Dr. K. Hiramatsu and Dr. Chih-Chie Wang for providing the control strains for SCCmec typing.
Author Contributions
All authors contributed sufficiently to this work. C.J.C. developed the concept and designed the study. C.C.Y. and C.L.S. collected and interpreted the data and wrote the paper. Y.C.H., S.S.S., J.C.S., P.H.H., and C.H.H. provided technical support and conceptual advice. All authors reviewed the manuscript.
Competing Interests
The authors declare no competing interests.
Footnotes
Chien-Chang Yang and Cheng-Len Sy contributed equally to this work.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Matsuo M, Cui L, Kim J, Hiramatsu K. Comprehensive identification of mutations responsible for heterogeneous vancomycin-intermediate Staphylococcus aureus (hVISA)-to-VISA conversion in laboratory-generated VISA strains derived from hVISA clinical strain Mu3. Antimicrob Agents Chemother. 2013;57:5843–5853. doi: 10.1128/AAC.00425-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Rybak MJ, et al. Characterization of vancomycin-heteroresistant Staphylococcus aureus from the metropolitan area of Detroit, Michigan, over a 22-year period (1986 to 2007) J Clin Microbiol. 2008;46:2950–2954. doi: 10.1128/JCM.00582-08. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Charles PGP, Ward PB, Johnson PDR, Howden BP, Grayson ML. Clinical features associated with bacteremia due to heterogeneous vancomycin-intermediate Staphylococcus aureus. Clin Infect Dis. 2004;38:448–451. doi: 10.1086/381093. [DOI] [PubMed] [Google Scholar]
- 4.Yeh Y-C, Chiu S-K, Yang Y-S, Wang Y-C, Lin J-C. Impact of vancomycin MIC creep on patients with methicillin-resistant Staphylococcus aureus bacteremia. J Microbiol Immunol Infect. 2012;45:214–220. doi: 10.1016/j.jmii.2011.11.006. [DOI] [PubMed] [Google Scholar]
- 5.Gould IM. Clinical relevance of increasing glycopeptide MICs against Staphylococcus aureus. Int J Antimicrob Agents. 2008;31(Suppl 2):1–9. doi: 10.1016/S0924-8579(08)70002-5. [DOI] [PubMed] [Google Scholar]
- 6.Fong RKC, Low J, Koh TH, Kurup A. Clinical features and treatment outcomes of vancomycin-intermediate Staphylococcus aureus (VISA) and heteroresistant vancomycin-intermediate Staphylococcus aureus (hVISA) in a tertiary care institution in Singapore. Eur J Clin Microbiol Infect Dis. 2009;28:983–987. doi: 10.1007/s10096-009-0741-5. [DOI] [PubMed] [Google Scholar]
- 7.Park K-H, et al. Comparison of the clinical features, bacterial genotypes and outcomes of patients with bacteraemia due to heteroresistant vancomycin-intermediate Staphylococcus aureus and vancomycin-susceptible S. aureus. J Antimicrob Chemother. 2012;67:1843–1849. doi: 10.1093/jac/dks131. [DOI] [PubMed] [Google Scholar]
- 8.Horne KC, et al. Prospective comparison of the clinical impacts of heterogeneous vancomycin-intermediate methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-susceptible MRSA. Antimicrob Agents Chemother. 2009;53:3447–3452. doi: 10.1128/AAC.01365-08. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.van Hal SJ, Lodise TP, Paterson DL. The clinical significance of vancomycin minimum inhibitory concentration in Staphylococcus aureus infections: a systematic review and meta-analysis. Clin Infect Dis. 2012;54:755–771. doi: 10.1093/cid/cir935. [DOI] [PubMed] [Google Scholar]
- 10.van Hal SJ, Paterson DL. Systematic review and meta-analysis of the significance of heterogeneous vancomycin-intermediate Staphylococcus aureus isolates. Antimicrob Agents Chemother. 2010;55:405–410. doi: 10.1128/AAC.01133-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Holmes NE, et al. Antibiotic choice may not explain poorer outcomes in patients with Staphylococcus aureus bacteremia and high vancomycin minimum inhibitory concentrations. J Infect Dis. 2011;204:340–347. doi: 10.1093/infdis/jir270. [DOI] [PubMed] [Google Scholar]
- 12.Chen C-J, Lin M-H, Shu J-C, Lu J-J. Reduced susceptibility to vancomycin in isogenic Staphylococcus aureus strains of sequence type 59: tracking evolution and identifying mutations by whole-genome sequencing. J Antimicrob Chemother. 2014;69:349–354. doi: 10.1093/jac/dkt395. [DOI] [PubMed] [Google Scholar]
- 13.Howden BP, Davies JK, Johnson PDR, Stinear TP, Grayson ML. Reduced vancomycin susceptibility in Staphylococcus aureus, including vancomycin-intermediate and heterogeneous vancomycin-intermediate strains: resistance mechanisms, laboratory detection, and clinical implications. Clin Microbiol Rev. 2010;23:139. doi: 10.1128/CMR.00042-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Khatib R, et al. Relevance of vancomycin-intermediate susceptibility and heteroresistance in methicillin-resistant Staphylococcus aureus bacteraemia. J Antimicrob Chemother. 2011;66:1594–1599. doi: 10.1093/jac/dkr169. [DOI] [PubMed] [Google Scholar]
- 15.Maor Y, et al. Clinical features of heteroresistant vancomycin-intermediate Staphylococcus aureus bacteremia versus those of methicillin-resistant S. aureus bacteremia. J Infect Dis. 2009;199:619–624. doi: 10.1086/596629. [DOI] [PubMed] [Google Scholar]
- 16.Wang J-L, Wang J-T, Sheng W-H, Chen Y-C, Chang S-C. Nosocomial methicillin-resistant Staphylococcus aureus (MRSA) bacteremia in Taiwan: mortality analyses and the impact of vancomycin, MIC = 2 mg/L, by the broth microdilution method. BMC Infect Dis. 2010;10:159. doi: 10.1186/1471-2334-10-159. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Liu C, et al. Clinical practice guidelines by the Infectious Diseases Society of America for the treatment of methicillin-resistant Staphylococcus aureus infections in adults and children. Clin Infect Dis. 2011;52:e18–55. doi: 10.1093/cid/ciq146. [DOI] [PubMed] [Google Scholar]
- 18.Murray KP, et al. Early use of daptomycin versus vancomycin for methicillin-resistant Staphylococcus aureus bacteremia with vancomycin minimum inhibitory concentration 1 mg/L: a matched cohort study. Clin Infect Dis. 2013;56:1562–1569. doi: 10.1093/cid/cit112. [DOI] [PubMed] [Google Scholar]
- 19.Traber KE, et al. agr function in clinical Staphylococcus aureus isolates. Microbiology. 2008;154:2265–2274. doi: 10.1099/mic.0.2007/011874-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Leonard SN, Rossi KL, Newton KL, Rybak MJ. Evaluation of the Etest GRD for the detection of Staphylococcus aureus with reduced susceptibility to glycopeptides. J Antimicrob Chemother. 2009;63:489–492. doi: 10.1093/jac/dkn520. [DOI] [PubMed] [Google Scholar]
- 21.Huang Y-C, Su L-H, Wu T-L, Lin T-Y. Changing molecular epidemiology of methicillin-resistant Staphylococcus aureus bloodstream isolates from a teaching hospital in Northern Taiwan. J Clin Microbiol. 2006;44:2268–2270. doi: 10.1128/JCM.00776-06. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Lina G, et al. Involvement of Panton-Valentine leukocidin-producing Staphylococcus aureus in primary skin infections and pneumonia. Clin Infect Dis. 1999;29:1128–1132. doi: 10.1086/313461. [DOI] [PubMed] [Google Scholar]
- 23.Gilot P, Lina G, Cochard T, Poutrel B. Analysis of the genetic variability of genes encoding the RNA III-activating components Agr and TRAP in a population of Staphylococcus aureus strains isolated from cows with mastitis. J Clin Microbiol. 2002;40:4060–4067. doi: 10.1128/JCM.40.11.4060-4067.2002. [DOI] [PMC free article] [PubMed] [Google Scholar]