Abstract
Background
Antibiotic resistance is increasing in many community settings. The purpose of this study was to determine the proportion of antibiotic-resistant community-associated bloodstream infections (CA-BSIs) present in hospital admissions to identify risk factors for acquiring resistant versus susceptible CA-BSIs and to describe the incidence of concurrent infections with CA-BSIs.
Methods
We conducted a retrospective cohort study of patients discharged from one community, one pediatric, and two tertiary/quaternary care hospitals within an academically affiliated network in the borough of Manhattan in New York, NY, from 2006–2008. The CA-BSIs present at hospital admission were defined as BSIs occurring within the first 48 hours of hospitalization. Infections and patient characteristics were identified using data available from patients’ electronic medical records and discharge records.
Results
In total, 1,677 CA-BSIs were identified. S. aureus had the largest proportion of resistance (41.2%), followed by enterococcal species (24.3%), P. aeruginosa (20.2%), S. pneumoniae (16.6%), A. baumannii (10.0%), and K. pneumoniae (9.9%). Significant predictors of resistance were prior residence in a skilled nursing facility (OR, 2.55; 95% CI, 1.39–4.70), advanced age (1.01; 1.002–1.02), presence of malignancy (0.58; 0.37–0.91), prior hospitalization (1.62; 1.17–2.23), a weighted Charlson score (1.09; 1.02–1.17) for S. aureus, presence of malignancy (1.82; 1.004–3.30), prior hospitalizations (2.03; 1.12–3.38) for enterococcal species, and younger age for S. pneumoniae (p=0.02). Urinary tract infections were the most common concurrent infection (n=45/87, 51.7%).
Conclusion
Over 27% of the CA-BSIs present on admission were antibiotic resistant. Understanding the prevalence and risk factors for CA-BSIs may help improve empiric antibiotic therapy and outcomes for patients with community-onset infections.
Keywords: Bloodstream infections, community-associated bloodstream infections, antibiotic resistance
1. INTRODUCTION
Community-associated bloodstream infections (CA-BSI) are a major cause of morbidity and mortality worldwide. While few population-based studies have characterized the epidemiology of CA-BSIs,[1] two recent estimates from Denmark and the United States suggest that the incidence is 78 and 83 per 100,000, respectively, accounting for approximately half of all bloodstream infections.[2, 3] In industrialized nations, hospitalization following onset of a CABSI is common, and case fatality rates are high at approximately 13–14%.[4, 5] Mortality can be even greater for patients with an antibiotic-resistant CA-BSI due to delayed administration of effective antibiotics.[6] Risk factors for antibiotic resistance among healthcare-associated infections have been well studied, with chronic conditions such as cancer, renal and liver failure, invasive procedures such as surgery, catheterization and intubation, antibiotic use, and prolonged contact with healthcare facilities exhibiting strong positive associations with resistance for many different organisms, including Staphylococcus aureus, enterococcus species, and gram-negative bacilli.[7] However, factors associated with resistance among community-associated infections may be different. Understanding the prevalence of antibiotic resistance in CA-BSIs, as well as which patients are at highest risk of acquiring a resistant CA-BSI, could help to improve empiric antibiotic therapy and, consequently, patient outcomes. Thus, the purpose of this study was to describe the prevalence of antibiotic resistance in patients hospitalized with a CA-BSI to determine patient characteristics associated with acquiring an antibiotic-resistant versus antibiotic-susceptible CA-BSI and to assess the incidence of concurrent infections in other body sites for patients with CA-BSIs.
2. METHODS
2.1. Sample and setting
Data were collected from all inpatients discharged from four hospitals within a large healthcare system in the New York metropolitan area between January 1, 2006, and December 31, 2008. The four facilities included a 220-bed community hospital, a 280-bed pediatric acute care hospital, and two tertiary/quaternary care hospitals with 650 and 850 beds, which serve a diverse spectrum of patients. This study was approved by the Institutional Review Board of Columbia University Medical Center.
2.2. Data collection
Electronically stored data were extracted from the clinical data warehouse, which is the admission, discharge, transfer and billing system, and the electronic health record system shared by the four hospitals. A database was compiled using information from (1) laboratory reports, including microbiologic results from blood, urine, respiratory, and wound cultures; (2) International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes, including conditions present on admission, procedures performed, and discharge diagnoses; (3) clinical records documenting medication administration and catheter use; and (4) administrative records including age, race, admission and discharge dates, and previous hospitalizations within the system.[8
2.3. Measures
Using electronically available data, a team of clinicians and researchers developed and validated electronic algorithms to identify bloodstream, urinary tract, respiratory tract, and surgical site infections based on the Centers for Disease Control and Prevention National Healthcare Safety Network definitions (http://www.cdc.gov/nhsn/about.html). The algorithms, described previously,[8–10] utilized time-stamped microbiologic results and documented clinical symptoms, urine microscopy results, and ICD-9-CM billing codes to identify infections and antimicrobial susceptibilities for the following organisms of interest, which are known to cause antibiotic-resistant BSIs in the United States: Staphylococcus aureus, Streptococcus pneumoniae, Klebsiella pneumoniae, Pseudomonas aeruginosa, enterococcal species (E. faecalis and E. faecium), and Acinetobacter baumannii.[11, 12] Definitions of antimicrobial resistance for each organism are summarized in Table 1. Community-associated infections were defined as occurring on or before the patient’s second day of hospitalization. Patients who had been hospitalized in the network within the previous 30 days and patients who developed a BSI after being in the hospital for at least 48 hours were considered to have healthcare-associated infections and excluded from further analysis.[13
Table 1.
Antimicrobial resistance in the six organisms of interest
| Staphylococcus aureus | Oxacillin |
| Streptococcus pneumoniae | Penicillin |
| Pseudomonas aeruginosa | Levofloxacin |
| Klebsiella pneumoniae | Imipenem, Meropenem |
| Enteroccous faecalis, Enterococcus faecium | Vancomycin |
| Acinetobacter baumannii | Ampicillin-sulbactam |
Patients’ demographic characteristics, prior contact with healthcare facilities, and health status at the time of hospital admission were also collected. Demographic information included age (<5, 5–12, 13–18, 19–35, 36–50, 51–65, and >65 years) and race (white, black, Hispanic, other, or unidentified). Prior contact with healthcare facilities included ever having been hospitalized within the network (yes/no) and having a previously documented residence in a skilled nursing facility (yes/no). Health status at time of hospital admission was determined based on present-on-admission ICD-9-CM codes and included diagnoses of diabetes mellitus, dermatitis, renal failure, and malignancies (yes/no); history of trauma, dialysis, and transplant (yes/no); and weighted Charlson comorbidity index (continuous).
2.4. Statistical analyses
All patients with a CA-BSI were included in the analyses. The primary outcome of interest was the occurrence of antibiotic-resistant CA-BSIs versus antibiotic-sensitive bacteria of the same organism. First, the proportion of antibiotic-resistant CA-BSI was calculated for each organism. Bivariable analyses were performed to determine the association between each independent variable and the antibiotic-resistant versus antibiotic-sensitive CA-BSIs using Chi-square or Fisher’s exact test for the categorical variables and Wilcoxon signed-rank test for the Charlson comorbidity index. For each organism, independent variables associated with resistant CA-BSIs at the p<0.25 level were included in a multiple logistic regression model. Age was assessed as a continuous variable in the regression model. Finally, we calculated the proportion of patients with a CA-BSI who had concurrent urinary tract, surgical site, and respiratory infections with the same organism. All analyses were completed using SAS version 9.3 (SAS Institute Inc., Cary, NC).
3. RESULTS
3.1. Characteristics of patients with CA-BSIs
A total of 319,959 discharges occurred during the study period, including 1,677 with CA-BSIs (5.2 per 1,000 discharges). The largest proportion was caused by S. aureus (695, 41.4%), followed by enterococcal species (333, 19.9%), K. pneumoniae (332, 19.8%), S. pneumoniae (163, 9.7%), P. aeruginosa (84, 5.0%), and A. baumannii (70, 4.2%; Table 2). The majority of patients with CA-BSIs had been previously hospitalized (1,072, 63.9%) and 6.7% (112) previously resided in a skilled nursing facility. Nearly half of CA-BSI patients had renal failure (728, 43.4%), nearly one-quarter had diabetes mellitus (410, 24.5%), and malignancies and dermatitis were also common (358, 21.3% and 240, 14.3%, respectively). Sixteen percent (279) were undergoing dialysis, 7% (117) had received an organ transplant, and only one patient had a history of trauma at the time of hospital admission. The largest proportion of infections occurred in the elderly >65 years (634, 37.8%), and 16.5% (276) occurred in pediatric patients ≤18 years.
Table 2.
Characteristics of patients with community-associated antimicrobial-resistant and antibiotic-susceptible bloodstream infections
| Staphylococcus aureus | Streptococcus pneuomoniae | Pseudomonas aeruginosa | Klebsiella pneumoniae | Enterococci species | Acinetobacter baumannii | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Resistant | Susceptible | P | Resistant | Susceptible | P | Resistant | Susceptible | P | Resistant | Susceptible | P | Resistant | Susceptible | P | Resistant | Susceptible | P | |
| % (N) | 286 | 409 | 37 | 126 | 17 | 67 | 33 | 299 | 81 | 252 | 7 | 63 | ||||||
| Male Gender | 64.0 (183) | 61.6 (252) | 0.52 | 54.1 (20) | 55.5 (70) | 0.87 | 64.7 (11) | 41.8 (28) | 0.09 | 54.5 (18) | 51.8 (155) | 0.77 | 49.4 (40) | 57.1 (144) | 0.22 | 28.6 (2) | 39.7 (25) | 0.70 |
| Race | 0.57 | 0.59 | -- | 0.89 | 0.70 | |||||||||||||
| White | 26.6 (76) | 25.7 (105) | 5.4 (2) | 13.5 (17) | 29.4 (5) | 19.4 (13) | 24.2 (8) | 29.8 (89) | 29.6 (24) | 31.3 (79) | 14.3 (1) | 22.2 (14) | ||||||
| Black | 20.3 (58) | 15.9 (65) | 32.4 (12) | 24.6 (31) | 5.9 (1) | 13.4 (9) | 18.2 (6) | 14.7 (44) | 11.1 (9) | 16.7 (42) | 14.3 (1) | 30.2 (19) | ||||||
| Hispanic | 21.3 (61) | 23.2 (95) | 27.0 (10) | 29.4 (37) | 11.8 (2) | 31.3 (21) | 30.3 (10) | 25.4 (76) | 23.5 (19) | 21.4 (54) | 28.6 (2) | 25.4 (16) | ||||||
| Unidentified | 27.6 (79) | 29.8 (122) | 32.4 (12) | 27.8 (35) | 52.9 (9) | 31.3 (21) | 21.2 (7) | 25.4 (76) | 30.9 (25) | 27.4 (69) | 42.9 (3) | 14.3 (9) | ||||||
| Other | 4.2 (12) | 5.4 (22) | 2.7 (1) | 4.8 (6) | 0.0 (0) | 4.5 (3) | 6.1 (2) | 4.7 (14) | 4.9 (4) | 3.2 (8) | 0.0 (0) | 7.9 (5) | ||||||
| Age | 0.002 | 0.03 | 0.09 | 0.37 | 0.21 | 0.07 | ||||||||||||
| <5 | 4.2 (12) | 4.4 (18) | 10.8 (4) | 4.8 (6) | 0.0 (0) | 10.4 (7) | 15.2 (5) | 21.1 (63) | 9.9 (8) | 10.3 (26) | 0.0 (0) | 22.2 (14) | ||||||
| 5 to 12 | 1.4 (4) | 4.6 (19) | 2.7 (1) | 0.7 (1) | 5.9 (1) | 9.0 (6) | 3.0 (1) | 6.0(18) | 8.6 (7) | 5.6 (11) | 0.0 (0) | 19.0 (12) | ||||||
| 13 to 18 | 0.3 (1) | 3.2 (13) | 2.7 (1) | 0.7 (1) | 0.0 (0) | 3.0 (2) | 3.0 (1) | 2.3 (7) | 1.2 (1) | 1.6 (4) | 14.3 (1) | 0.0 (0) | ||||||
| 19 to 35 | 6.6 (19) | 8.8 (36) | 10.8 (4) | 4.8 (6) | 5.9 (1) | 4.5 (3) | 9.0 (3) | 4.7 (14) | 3.7 (3) | 4.8 (12) | 0.0 (0) | 11.1 (7) | ||||||
| 36 to 50 | 19.6 (56) | 19.6 (80) | 13.5 (5) | 22.2 (28) | 11.8 (2) | 14.9 (10) | 15.2 (5) | 12.4 (37) | 14.8 (12) | 9.5 (24) | 14.3 (1) | 7.9 (5) | ||||||
| 51 to 65 | 26.6 (76) | 28.1 (115) | 24.3 (9) | 24.6 (31) | 29.4 (5) | 14.9 (10) | 21.2 (7) | 20.1 (60) | 23.5 (19) | 20.6 (52) | 28.6 (2) | 17.5 (11) | ||||||
| 65+ | 41.3 (118) | 31.3 (128) | 32.4 (12) | 42.0 (53) | 47.1 (8) | 40.3 (27) | 39.4 (13) | 33.8 (101) | 39.5 (32) | 49.6 (125) | 42.9 (3) | 22.2 (14) | ||||||
| Prior Skilled Nursing Facility stay |
11.9 (34) | 2.4 (18) | 0.0002 | 0.0 (0) | 4.8 (6) | -- | 11.8 (2) | 3.0 (2) | 0.18 | 9.0 (3) | 4.0 (12) | 0.18 | 7.4 (6) | 10.3 (26) | 0.44 | 0.0 (0) | 4.8 (3) | |
| Diabetes | 31.8 (91) | 27.1 (111) | 0.18 | 18.9 (7) | 15.9 (20) | 0.66 | 23.5 (4) | 16.4 (11) | 0.49 | 24.2 (8) | 21.7 (65) | 0.74 | 18.5 (15) | 27.8 (70) | 0.10 | 14.3 (1) | 11.1 (7) | 1.00 |
| Transplant History |
2.8 (8) | 6.4 (26) | 0.03 | 5.4 (2) | 2.4 (3) | 0.32 | 5.9 (1) | 16.4 (11) | 0.44 | 6.1 (2) | 10.7 (32) | 0.55 | 11.1 (9) | 5.6 (14) | 0.09 | 14.3 (1) | 12.7 (8) | 1.00 |
| Dialysis | 28.7 (82) | 20.0 (82) | 0.008 | 16.2 (6) | 6.3 (8) | 0.10 | 11.8 (2) | 11.9 (8) | 1.00 | 15.2 (5) | 8.7 (26) | 0.22 | 14.8 (12) | 14.7 (37) | 0.99 | 28.6 (2) | 14.3 (9) | 0.30 |
| Trauma | 0.0 (0) | 0.2 (1) | 0.0 (0) | 0.0 (0) | 0.0 (0) | 0.0 (0) | 0.0 (0) | 0.0 (0) | 0.0 (0) | 0.0 (0) | 0.0 (0) | 0.0 (0) | ||||||
| Renal Failure | 55.2 (158) | 46.7 (191) | 0.03 | 37.8 (14) | 35.7 (45) | 0.81 | 35.3 (6) | 40.3 (27) | 0.71 | 51.5 (17) | 36.1 (108) | 0.08 | 39.5 (32) | 40.9 (103) | 0.83 | 57.1 (4) | 36.5 (23) | 0.42 |
| Malignancy | 14.0 (40) | 18.3 (75) | 0.13 | 13.5 (5) | 23.0 (29) | 0.21 | 35.3 (6) | 38.8 (26) | 0.79 | 18.2 (6) | 28.4 (85) | 0.21 | 27.2 (22) | 17.1 (43) | 0.05 | 42.9 (3) | 28.6 (18) | 0.41 |
| Dermatitis | 22.0 (63) | 22.7 (93) | 0.83 | 5.4 (2) | 7.1 (9) | 5.9 (1) | 7.5 (5) | 3.0 (1) | 6.0 (18) | 0.71 | 13.6 (11) | 11.1 (28) | 0.55 | 0.0 (0) | 14.3 (9) | |||
| Prior Hospitalizations |
67.1 (192) | 56.0 (229) | 0.003 | 37.8 (14) | 39.7 (50) | 0.84 | 70.6 (12) | 80.6 (54) | 0.51 | 69.7 (23) | 72.2 (216) | 0.76 | 79.0 (64) | 65.1 (164) | 0.02 | 57.1 (4) | 79.4 (50) | 0.54 |
| Weighted Charlson Score (median, [SE]) |
2.00 [0.15] | 2.00 [0.12] | <0.0001 | 1.00 [0.47] | 2.00 [0.23] | <0.0001 | 2.00 [0.54] | 2.00 [0.29] | <0.0001 | 2.00 [0.24] | 1.00 [0.16] | <0.0001 | 2.00 [0.15] | 2.00 [0.28] | <0.0001 | 2.00 [1.00] | 2.00 [0.28] | <0.0001 |
3.2. Prevalence and risk factors for antibiotic resistance among CA-BSIs
S. aureus had the largest proportion of antibiotic resistance (41.2%), followed by enterococcal species (24.3%), P. aeruginosa (20.2%), S. pneumoniae (16.6%), A. baumannii (10.0%), and K. pneumoniae (9.9%). In the bivariable analyses, there were no significant associations between resistance and gender or race for any organism (Table 2). Age was significantly associated with resistance for S. aureus, where age >65 was positively associated with resistance (p=0.002), and S. pneumoniae, where age >65 was negatively associated with resistance (p=0.03). Prior stay in a skilled nursing facility (p=0.0002) and renal failure (p=0.03) were significantly positively associated with methicillin resistance for S. aureus, but no significant differences were observed for other organisms, and the direction of these associations was not consistent across organisms. Transplant history was significantly negatively associated with resistance for S. aureus (p=0.03), but this trend was also inconsistent across organisms with no other significant associations detected. Prior hospitalization was significantly positively associated with resistance for S. aureus (p=0.003) and enterococcal species (p=0.02). Diabetes was positively associated with resistance for all organisms except for enterococci species, although these differences were not statistically significant. Malignancies were not significantly associated with resistance for any organism, and the direction of the association between malignancy and resistance was not consistent across organisms. The weighted Charlson comorbidity index was significantly associated with resistance for all organisms (all p<0.001).
In the multivariable analyses (Table 3) for S. aureus, significant predictors of resistance included previous residence in a skilled nursing facility (OR, 2.55; 95% CI, 1.39–4.70), older age (1.01, 1.002–1.02), prior hospitalizations (1.62, 1.17–2.23), and weighted Charlson comorbidity index (1.09, 1.02–1.17), while malignancies were protective against resistance (0.58, 0.37–0.91). However, for enterococcal species, malignancies were positively associated with resistance (1.82, 1.004–3.30), along with prior hospitalizations (2.03, 1.12–3.38). The only significant predictor of resistance for S. pneumoniae was younger age, with 41.7% of isolates from patients <35 years versus 20% of those from patients >35 years (Chi square, 5.40; p=0.02). Multivariable analyses were not performed for P. aeruginosa, A. baumannii, or K. pneumoniae due to insufficient power.
Table 3.
Factors associated with antibiotic-resistant versus antibiotic-susceptible community-associated bloodstream infection
| Odds ratios (95% Confidence Intervals) | |||
|---|---|---|---|
|
Staphylococcus aureus (n=695) |
Enterococcus sp. (n=333) |
Streptococcus pneumoniae (n=163) |
|
| Prior stay in skilled nursing facility |
2.55 (1.39–4.70) | --- | --- |
| Age (years) | 1.01 (1.002–1.02) | --- | 0.98 (0.97–0.999) |
| Malignancy | 0.58 (0.37–0.91) | 1.82 (1.004–3.30) | --- |
| Prior Hospitalizations | 1.62 (1.17–2.23) | 2.03 (1.12–3.38) | --- |
| Weighted Charlson Comorbidity Index |
1.09 (1.02–1.17) | --- | --- |
Results of multiple logistic regression analysis.
3.3. Concurrent infections present among patients admitted with CA-BSIs
Eighty-seven concurrent infections were detected among the 1,667 CA-BSI cases (Table 4). Urinary tract infections were the most common concurrent infection (n=45, 51.7%), followed by respiratory tract (n=30, 34.5%) and surgical site (n=12, 13.8%). Concurrent infections were most common among patients with S. aureus BSI (n=62), followed by K. pneumoniae (n=12), enterococcal species (n=7), P. aeruginosa (n=3), A. baumannii (n=2), and S. pneumoniae (n=1).
Table 4.
Concurrent infections present among patients admitted to hospital with community-associated bloodstream infections
| Bloodstream infection organism (N) | Urinary tract infection N=45 |
Pneumonia N=30 |
Surgical site infection N=12 |
None | |
|---|---|---|---|---|---|
| Staphylococcus aureus | Sensitive (409) | 20 (4.9) | 12 (2.9) | 8 (2.0) | 369 (90.2) |
| Resistant (286) | 6 (2.1) | 13 (4.5) | 3 (1.1) | 264 (92.3) | |
| Streptococcus pneumoniae | Sensitive (126) | 0 (0) | 1 (0.8) | 0 (0) | 125 (99.2) |
| Resistant (37) | 0 (0) | 0 (0) | 0 (0) | 37 (100) | |
| Pseudomonas aeruginosa | Sensitive (67) | 1 (1.5) | 1 (1.5) | 0 (0) | 65 (97.0) |
| Resistant (17) | 0 (0) | 1 (5.9) | 0 (0) | 16 (94.1) | |
| Klebsiella pneumoniae | Sensitive (299) | 11 (5.9) | 0 (0) | 0 (0) | 288 (96.3) |
| Resistant (33) | 1 (3.0) | 0 (0) | 0 (0) | 32 (97.0) | |
| Enterococci species | Sensitive (252) | 5 (2.0) | 0 (0) | 1 (0.4) | 246 (97.6) |
| Resistant (81) | 1 (1.2) | 0 (0) | 0 (0) | 80 (98.8) | |
| Acinetobacter baumannii | Sensitive (63) | 0 (0) | 1 (1.6) | 0 (0) | 62 (98.4) |
| Resistant (7) | 0 (0) | 1 (14.3) | 0 (0) | 6 (85.7) |
Data are n(%).
4. DISCUSSION
Only a few previous studies have investigated the factors associated with antibiotic resistance among patients with a CA-BSI. Hattemer et al. [14] examined differences between patients with carbapenem-resistant versus carbapenem-sensitive P. aeruginosa CA-BSI and reported no significant differences by age, sex, or Charlson comorbidity index but did find that prior antibiotic therapy was highly predictive of resistance—a factor that we were not able to assess in this analysis. We did find a positive, though not statistically significant, association between malignancy and antibiotic resistance among patients with enterococcal infections, but this finding was not consistent across all of the organisms we evaluated. In fact, we found that among patients with a CA-BSI caused by S. aureus, those with a history of malignancy were significantly less likely to have a methicillin-resistant strain after controlling for other factors such as prior hospitalization, skilled nursing facility stay, severity of illness, and age. This finding is surprising but not inconsistent with a previous study on enterococcal bloodstream infections, which found that high-level gentamicin resistance was not associated with all cancers, only hematologic malignancies.[15] Methicillin resistance is increasingly common among community members without chronic illness, prolonged contact with healthcare institutions, or other traditional risk factors, so the changing epidemiology of MRSA might explain this finding.[16] While our study found no differences in risk of resistance by race, Sattler et al. [17] found that among children with an S. aureus CA-BSI, African Americans were significantly more likely and white and Hispanic children were significantly less likely to have resistant isolates; race was the only significant demographic or clinical predictor of resistance identified by these authors. Younger age was a risk factor for resistance to a CA-BSI caused by S. pneumoniae in our patient population. This finding is not unexpected because pneumococcus frequently colonizes the respiratory tract and is particularly common in children, for whom the tract is frequently exposed to antimicrobial agents.[18
This study benefits from being one of the largest to investigate the factors associated with antibiotic resistance among patients hospitalized with a CA-BSI, though the methods have certain limitations. First, the use of electronic health records limited our ability to obtain data on patient symptoms, so the case definitions were primarily based on laboratory results, although the infection-defining algorithms were validated by a team of clinicians.[8–10] Similarly, the factors examined for possible association with resistant CA-BSI were restricted to those that could be collected from patients’ electronic medical and billing records, which are not designed or validated for the purposes of research.[19] For example, previous antibiotic use is likely to be an important risk factor for resistance, but these data were not systematically available in the electronic medical records. Prior contact with healthcare facilities was also not entirely captured by the available data because the billing records only indicated previous admissions within the hospital’s network. A second limitation of this analysis was insufficient statistical power to detect differences between patients with resistant versus susceptible CA-BSIs for some organisms. We also did not account for possible polymicrobial BSIs.
The ability to identify which patients are most likely to have an infection with an antibiotic-resistant organism is critical, as delays in effective antibiotic therapy can drastically increase mortality rates for patients with a CA-BSI. One study of patients with A. baumannii bacteremia described a 25% reduction in mortality when effective antimicrobials were administered within 48 hours of an initial blood culture.[20] Similarly, Apisarnthanarak et al. [21] found that patients with extended-spectrum β-lactamase-producing E. coli and K. pneumoniae CA-BSI had significantly higher odds of mortality if they did not receive empiric antimicrobial therapy that included carbapenem or β-lactam/β-lactamase inhibitors. Creating local or national risk scores, similar to what has been done for patients presenting with community-acquired pneumonia,[22] could help guide physicians toward prescribing the most appropriate empiric antibiotics. The results of this study add to a growing body of research describing profiles of patients at highest risk for an antibiotic-resistant CA-BSI.
ACKNOWLEDGEMENTS
The authors thank data manager Jianfang Liu, PhD, for her assistance with data procurement and cleaning. This study was conducted as part of a larger grant “Distribution of the Costs of Antimicrobial Resistant Infections,” funded by the National Institute of Nursing Research, National Institutes of Health (R01 NR010822).
Footnotes
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Contributor Information
Caitlin M. Wolfe, Email: cmw2188@columbia.edu.
Bevin Cohen, Email: bac2116@columbia.edu.
Elaine Larson, Email: ell23@columbia.edu.
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