The relationship between multidrug resistance (MDR), inappropriate empiric therapy (IET), and mortality among patients with Acinetobacter baumannii (AB) remains unclear. We examined it using a large U.S. database.
Methods
We conducted a retrospective cohort study using the Premier Research database (2009–2013) of 175 U.S. hospitals. We included all adult patients admitted with pneumonia or sepsis as their principal diagnosis, or as a secondary diagnosis in the setting of respiratory failure, along with antibiotic administration within 2 days of admission. Only culture-confirmed infections were included. Resistance to at least three classes of antibiotics defined multidrug-resistant AB (MDR-AB). We used logistic regression to compute the adjusted relative risk ratio (RRR) of patients with MDR-AB receiving IET and IET’s impact on mortality.
Results
Among 1423 patients with AB infection, 1171 (82.3 %) had MDR-AB. Those with MDR-AB were older (63.7 ± 15.4 vs. 61.0 ± 16.9 years, p = 0.014). Although chronic disease burden did not differ between groups, the MDR-AB group had higher illness severity than those in the non-MDR-AB group (intensive care unit 68.0 % vs. 59.5 %, p < 0.001; mechanical ventilation 56.2 % vs. 42.1 %, p < 0.001). Patients with MDR-AB were more likely to receive IET than those in the non-MDR-AB group (76.2 % MDR-AB vs. 13.8 % non-MDR-AB, p < 0.001). In a regression model, MDR-AB strongly predicted receipt of IET (adjusted RRR 5.5, 95 % CI 4.0–7.7, p < 0.001). IET exposure was associated with higher hospital mortality (adjusted RRR 1.8, 95 % CI 1.4–2.3, p < 0.001).
Conclusions
In this large U.S. database, the prevalence of MDR-AB among patients with AB infection was > 80 %. Harboring MDR-AB increased the risk of receiving IET more than fivefold, and IET nearly doubled hospital mortality.
The Centers for Disease Control and Prevention considers Acinetobacter baumannii (AB) a “serious” threat [1]. AB’s resistance mechanisms target both first-line and salvage broad-spectrum agents, with approximate doubling in carbapenem and multidrug resistance (MDR) in the United States over the last decade [2, 3]. In addition to its public health implications, the rising tide of drug resistance presents a difficult clinical conundrum. In serious infections, appropriate initial therapy determines clinical outcomes. However, more extensive drug resistance makes it a challenge to select appropriate treatment [4‐13]. Carbapenem resistance among AB in severe sepsis and/or septic shock increases the risk of receiving inappropriate empiric therapy (IET) nearly threefold, raising the risk of death [14]. Unfortunately, using carbapenems as empiric therapy in hopes of minimizing IET drives increasing carbapenem resistance. Because of the limited data on this issue in AB, we conducted a multicenter, retrospective cohort study to explore the impact of MDR in IET and of IET on hospital mortality in AB.
Methods
We conducted a multicenter retrospective cohort study of patients admitted to the hospital with pneumonia and/or sepsis and included in the Premier Research database in the 2009–2013. We hypothesized that multidrug-resistant AB (MDR-AB) (primary exposure) increases the risk of receiving IET (primary outcome), and that IET increases hospital mortality. Because this study used already-existing, Health Insurance Portability and Accountability Act (“HIPAA”)-compliant, fully de-identified data, it was exempt from institutional review board (IRB) review.
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Patient population
Patients were included if they were adults (aged ≥ 18 years) hospitalized with pneumonia and/or sepsis. Pneumonia was identified by the principal diagnosis International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes 481–486, or by respiratory failure codes (518.81 or 518.84) with pneumonia as a secondary diagnosis. Sepsis was identified by the principal diagnosis codes 038, 038.9, 020.0, 790.7, 995.92, or 785.52, or by respiratory failure codes (518.81 or 518.84) with sepsis as a secondary diagnosis [15‐18]. Only patients with community-onset (present on admission) infection and antibiotic treatment beginning within the first 2 hospital days and continued for at least three consecutive days or until discharge were included [15‐17]. Patients were excluded if they had been transferred from another acute care facility, had cystic fibrosis, or had a hospital length of stay of 1 day or less. Those with both pneumonia and sepsis were included in the pneumonia group. Patients were followed until death in or discharge from the hospital. Only patients with a positive AB culture from a pulmonary or blood source who met the above criteria were included in the analysis.
Data source
The Premier Research database, an electronic laboratory, pharmacy, and billing data repository for 2009–2013, contains approximately 15 % of all U.S. hospitalizations nationwide. In addition to patient age, sex, race and/or ethnicity, principal and secondary diagnoses, and procedures, the database contains a date-stamped log of all medications, laboratory tests, and diagnostic and therapeutic services charged to the patient or the patient’s insurer. We used data from 176 U.S. institutions that submit microbiological data into the database. Eligible time began only following the commencement of microbiological data submission by each institution.
Baseline variables
We classified infection (pneumonia or sepsis) as healthcare-associated (HCA) if one or more of the following were present: (1) prior hospitalization within 90 days of the index hospitalization, (2) hemodialysis, (3) admission from a long-term care facility, and/or (4) immune suppression. All other infections were considered community-acquired (CA). Patient-level factors included demographic variables and comorbid conditions. Charlson comorbidity index score was computed as a measure of the burden of chronic illness, while intensive care unit (ICU) admission, mechanical ventilation, and vasopressor use served as markers for disease severity. Hospital-level characteristics examined were geographic region, size, teaching status, and urbanicity.
Microbiological and treatment-related variables and definitions
Blood and respiratory cultures had to be obtained within the first 2 days of hospitalization. AB isolates were classified as S (susceptible), I (intermediate), or R (resistant). For the purposes of the present analyses, I and R were grouped together as nonsusceptible. MDR-AB was defined, per Magiorakos et al., as any AB resistant to at least one agent in at least three antimicrobial classes [19]. Similarly, extensively drug resistant AB (XDR-AB) was defined as an AB resistant to at least one agent in all but two or fewer classes listed above, and pandrug-resistant AB (PDR-AB) as an AB resistant to all antimicrobial agents listed above [19].
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IET was present if the antibiotic administered did not cover the organism or if coverage did not start within 2 days of obtaining the positive culture. Because the role of combination therapy in treating AB is not well defined, combination therapy was not included in the definition of IET [20]. IET was deemed “indeterminate” if the susceptibility of AB to the regimen received was not reported. These cases were excluded from the IET analysis. All microbiological testing was performed at the institutions contributing data to the database and conformed to the Clinical & Laboratory Standards Institute standards.
Statistical analyses
We compared characteristics of patients infected with MDR-AB with those of patients with non-MDR-AB infection, as well as characteristics of patients who received IET with those of patients treated with non-IET. Continuous variables were reported as means with SD when distributed normally or as medians with 25th and 75th percentiles when skewed. Differences between mean values were tested via Student’s t test, and differences between medians were assessed using the Mann-Whitney U test. Categorical data were summarized as proportions, and chi-square test or Fisher’s exact test (when cell counts were ≤4) was used to examine differences between groups.
We developed a generalized logistic regression model to explore the relationship between MDR-AB and the risk of IET. Covariates in the model included demographics (sex, age, whether the infection was HCA), Elixhauser comorbidities, and measures of illness severity by hospital day 2. We calculated the relative risk ratio with 95 % CI of receiving IET for MDR-AB vs. non-MDR-AB, based on Huber-White robust standard errors clustered at the hospital level [21]. To confirm our results, we created two other models: (1) a nonparse model that included all of the predictors in the generalized logistic regression model with a large number of additional treatments present or absent by hospital day 2, and (2) a propensity-matched model with propensity for MDR-AB derived from a logistic regression model using the nonparse model’s predictors, and MDR-AB matched to non-MDR-AB patients using a 5:1 Greedy algorithm [22, 23].
All tests were two-tailed, and a p value < 0.05 was deemed a priori to represent statistical significance. All analyses were performed in Stata/MP 13.1 for Windows software (StataCorp LP, College Station, TX, USA).
Results
Among the 229,028 enrolled patients with pneumonia or sepsis, 1423 (0.6 %) had a pulmonary or blood culture positive for AB, of which 1171 (82.3 %) were MDR, 239 (16.8 %) were XDR, and 0 (0.0 %) were PDR. Patients with MDR-AB were older (63.7 ± 15.4 vs. 61.0 ± 16.9 years, p = 0.014) than those with non-MDR-AB, while the racial distributions were comparable in both groups (Table 1). Although the distribution of some chronic conditions varied, there was no difference between the groups in the Charlson comorbidity index (Table 1). MDR-AB was more common than non-MDR-AB in the West and the Midwest, in urban hospitals, and in hospitals of medium size (200–499 beds). Both large hospitals (500+ beds) and those with an academic program were less likely to have MDR-AB than non-MDR-AB (Table 1).
Table 1
Baseline characteristics
Non-MDR-AB (n = 252)
%
MDR-AB (n = 1171)
%
p Value
Mean age, years (SD)
61.0 (16.9)
63.7 (15.4)
0.014
Male sex
134
53.2 %
633
54.1 %
0.799
Race/ethnicity
White
134
53.2 %
633
54.1 %
< 0.001
Black
Hispanic
159
63.1 %
738
63.0 %
Other
55
21.8 %
276
23.6 %
Admission source
Non-healthcare facility (including from home)
167
66.3 %
573
48.9 %
< 0.001
Clinic
14
5.6 %
26
2.2 %
Transfer from ECF
13
5.2 %
280
23.9 %
Transfer from another non-acute care facility
3
1.2 %
45
3.8 %
Emergency department
54
21.4 %
236
20.2 %
Other
1
0.4 %
11
1.0 %
Elixhauser comorbidities
Congestive heart failure
61
24.2 %
353
30.1 %
0.060
Valvular disease
21
8.3 %
92
7.9 %
0.800
Pulmonary circulation disease
16
6.3 %
107
9.1 %
0.153
Peripheral vascular disease
33
13.1 %
145
12.4 %
0.756
Paralysis
32
12.7 %
292
24.9 %
<0.001
Other neurological disorders
44
17.5 %
300
25.6 %
0.006
Chronic pulmonary disease
108
42.9 %
507
43.3 %
0.898
Diabetes without chronic complications
65
25.8 %
390
33.3 %
0.020
Diabetes with chronic complications
21
8.3 %
96
8.2 %
0.943
Hypothyroidism
28
11.1 %
182
15.5 %
0.072
Renal failure
66
26.2 %
359
30.7 %
0.160
Liver disease
17
6.7 %
37
3.2 %
0.007
Peptic ulcer disease with bleeding
0
0.0 %
0
0.0 %
1.000
AIDS
0
0.0 %
0
0.0 %
1.000
Lymphoma
1
0.4 %
16
1.4 %
0.336
Metastatic cancer
20
7.9 %
30
2.6 %
< 0.001
Solid tumor without metastasis
17
6.7 %
31
2.6 %
0.001
Rheumatoid arthritis/collagen vascular
5
2.0 %
46
3.9 %
0.132
Coagulopathy
45
17.9 %
134
11.4 %
0.005
Obesity
41
16.3 %
191
16.3 %
0.987
Weight loss
49
19.4 %
392
33.5 %
< 0.001
Fluid and electrolyte disorders
145
57.5 %
628
53.6 %
0.258
Chronic blood loss anemia
5
2.0 %
16
1.4 %
0.461
Deficiency anemia
97
38.5 %
593
50.6 %
< 0.001
Alcohol abuse
22
8.7 %
35
3.0 %
< 0.001
Drug abuse
16
6.3 %
29
2.5 %
0.001
Psychosis
13
5.2 %
77
6.6 %
0.402
Depression
29
11.5 %
161
13.7 %
0.343
Hypertension
158
62.7 %
669
57.1 %
0.104
Charlson comorbidity index score
0
58
23.0 %
247
21.1 %
0.542
1
60
23.8 %
298
25.4 %
2
50
19.8 %
244
20.8 %
3
35
13.9 %
179
15.3 %
4
21
8.3 %
112
9.6 %
5+
28
11.1 %
91
7.8 %
Mean (SD)
2.2 (2.4)
2.0 (1.9)
0.096
Median [IQR]
2 [1–3]
2 [1–3]
0.873
Hospital characteristics
U.S. census region
Midwest
49
19.4 %
377
32.2 %
< 0.001
Northeast
54
21.4 %
164
14.0 %
South
122
48.4 %
436
37.2 %
West
27
10.7 %
194
16.6 %
Number of beds
< 200
26
10.3 %
140
12.0 %
0.007
200–299
49
19.4 %
272
23.2 %
300–499
84
33.3 %
454
38.8 %
500+
93
36.9 %
305
26.0 %
Teaching
137
54.4 %
537
45.9 %
0.014
Urban
233
92.5 %
1135
96.9 %
0.001
MDR-AB multidrug-resistant Acinetobacter baumannii, ECF extended care facility
In both groups (MDR-AB and non-MDR-AB), the majority (approximately three-fourths) of the patients had a diagnosis of sepsis, with the remaining one-fourth having pneumonia (Table 2). Patients harboring MDR-AB were more likely to have an HCA infection (64.9 % vs. 42.5 %, p < 0.001) along with higher illness severity by day 2 of admission (ICU 68.0 % vs. 59.5 %, p < 0.001; mechanical ventilation 56.2 % vs. 42.1 %, p < 0.001; vasopressors 15.5 % vs. 17.6 %, p = 0.420) than non-MDR-AB patients (Table 2). Although patients in the MDR-AB group had a higher prevalence of use of antipseudomonal carbapenems, aminoglycosides, and polymyxins than those in the non-MDR-AB group, they were also far more likely to receive IET (76.2 % MDR-AB vs. 13.8 % non-MDR-AB, p < 0.001), regardless of infection type (Fig. 1). Unadjusted hospital mortality among patients with MDR-AB was nearly double that in those with non-MDR-AB (23.7 % vs. 12.7 %, p < 0.001).
Table 2
Infection characteristics and treatment
Non-MDR-AB (n = 252)
%
MDR-AB (n = 1171)
%
p Value
Infection characteristics
Sepsis
184
73.0 %
875
74.7 %
0.573
Pneumonia
68
27.0 %
296
25.3 %
HCA
107
42.5 %
760
64.9 %
< 0.001
Illness severity measures by day 2
ICU admission
150
59.5 %
796
68.0 %
0.010
Mechanical ventilation
106
42.1 %
658
56.2 %
< 0.001
Vasopressors
39
15.5 %
206
17.6 %
0.420
Antibiotics administered by day 2
Antipseudomonal penicillins with β-lactamase inhibitor
140
55.6 %
588
50.2 %
0.124
Extended-spectrum cephalosporins
100
39.7 %
373
31.9 %
0.017
Antipseudomonal fluoroquinolones
96
38.1 %
489
41.8 %
0.284
Antipseudomonal carbapenems
37
14.7 %
350
29.9 %
< 0.001
Aminoglycosides
25
9.9 %
204
17.4 %
0.003
Penicillins with β-lactamase inhibitors
4
1.6 %
19
1.6 %
1.000
Tetracyclines
3
1.2 %
6
0.5 %
0.203
Folate pathway inhibitors
3
1.2 %
11
0.9 %
0.724
Polymyxins
0
0.0 %
37
3.2 %
0.001
Empiric treatment appropriateness
Non-IET
162
64.3 %
217
18.5 %
< 0.001
IET
26
10.3 %
693
59.2 %
Indeterminate
64
25.4 %
261
22.3 %
Abbreviations: MDR-AB multidrug-resistant Acinetobacter baumannii, HCA healthcare-associated, ICU intensive care unit, IET inappropriate empiric therapy
Fig. 1
Inappropriate empiric therapy as a function of multidrug resistance (MDR). HCA healthcare-associated
×
When we compared the cohort of 1098 patients (77.2 % of all AB patients) with valid, known antimicrobial treatment data based on the receipt of IET, we found that only 379 (34.5 %) received appropriate therapy (Table 3). The rate of sepsis upon admission did not significantly differ between IET and non-IET patients (Table 3). Unadjusted hospital mortality was higher in patients receiving IET than non-IET (23.6 % vs. 16.6 %, p = 0.007) in all infection types (Fig. 2).
Table 3
Characteristics of the cohort, based on receipt of inappropriate empiric therapy
Non-IET (n = 379)
%
IET (n = 719)
%
p Value
Baseline characteristics
Mean age, years (SD)
62.4 (15.6)
62.7 (15.9)
0.767
Male sex
202
53.3 %
373
51.9 %
0.654
Race/ethnicity
White
236
62.3 %
464
64.5 %
0.055
Black
103
27.2 %
159
22.1 %
Hispanic
7
1.8 %
32
4.5 %
Other
33
8.7 %
64
8.9 %
Admission source
Non-healthcare facility (including from home)
223
58.8 %
357
49.7 %
0.022
Clinic
14
3.7 %
15
2.1 %
Transfer from ECF
69
18.2 %
173
24.1 %
Transfer from another non-acute care facility
8
2.1 %
20
2.8 %
Emergency department
63
16.6 %
148
20.6 %
Other
2
0.5 %
6
0.9 %
Elixhauser comorbidities
Congestive heart failure
97
21.5 %
209
30.4 %
0.222
Valvular disease
30
6.6 %
53
7.7 %
0.746
Pulmonary circulation disease
26
5.8 %
62
9.0 %
0.306
Peripheral vascular disease
51
11.3 %
82
11.9 %
0.322
Paralysis
85
18.8 %
176
25.6 %
0.448
Other neurological disorders
82
18.1 %
185
26.9 %
0.133
Chronic pulmonary disease
164
36.3 %
305
44.4 %
0.786
Diabetes without chronic complications
105
23.2 %
241
35.1 %
0.049
Diabetes with chronic complications
38
8.4 %
56
8.2 %
0.208
Hypothyroidism
49
10.8 %
119
17.3 %
0.113
Renal failure
107
23.7 %
217
31.6 %
0.501
Liver disease
17
3.8 %
27
3.9 %
0.557
Peptic ulcer disease with bleeding
0
0.0 %
0
0.0 %
1.000
AIDS
0
0.0 %
0
0.0 %
1.000
Lymphoma
2
0.4 %
10
1.5 %
0.236
Metastatic cancer
24
5.3 %
17
2.5 %
0.001
Solid tumor without metastasis
18
4.0 %
19
2.8 %
0.066
Rheumatoid arthritis/collagen vascular
11
2.4 %
29
4.2 %
0.342
Coagulopathy
58
12.8 %
83
12.1 %
0.077
Obesity
48
10.6 %
128
18.6 %
0.027
Weight loss
94
20.8 %
250
36.4 %
0.001
Fluid and electrolyte disorders
219
48.5 %
393
57.2 %
0.322
Chronic blood loss anemia
5
1.1 %
9
1.3 %
0.924
Deficiency anemia
173
38.3 %
363
52.8 %
0.127
Alcohol abuse
18
4.0 %
24
3.5 %
0.246
Drug abuse
16
3.5 %
19
2.8 %
0.157
Psychosis
22
4.9 %
45
6.6 %
0.765
Depression
50
11.1 %
96
14.0 %
0.941
Hypertension
215
47.6 %
413
60.1 %
0.821
Charlson comorbidity score
0
72
19.0 %
164
22.8 %
0.152
1
108
28.5 %
177
24.6 %
2
64
16.9 %
151
21.0 %
3
62
16.4 %
108
15.0 %
4
34
9.0 %
66
9.2 %
5+
39
10.3 %
53
7.4 %
Mean (SD)
2.2 (2.2)
2.0 (1.9)
0.043
Median [IQR]
2 [1–3]
2 [1–3]
0.202
Infection characteristics and treatments
Infection characteristics
Sepsis
296
78.1 %
525
73.0 %
0.065
Pneumonia
83
21.9 %
194
27.0 %
HCA
222
58.6 %
464
64.5 %
0.053
MDR-AB
217
57.3 %
693
96.4 %
< 0.001
Illness severity
ICU admission
249
65.7 %
482
67.0 %
0.655
Mechanical ventilation
206
54.4 %
390
54.2 %
0.972
Vasopressors
64
16.9 %
121
16.8 %
0.981
Antibiotics administered
Antipseudomonal penicillins with β-lactamase inhibitor
91
24.0 %
123
17.1 %
0.006
Antipseudomonal fluoroquinolones
97
25.6 %
209
29.1 %
0.222
Extended-spectrum cephalosporins
177
46.7 %
339
47.1 %
0.888
Antipseudomonal carbapenems
190
50.1 %
350
48.7 %
0.647
Aminoglycosides
140
36.9 %
269
37.4 %
0.877
Penicillins with β-lactamase inhibitors
5
1.3 %
9
1.3 %
0.924
Polymyxins
12
3.2 %
9
1.3 %
0.028
Folate pathway inhibitors
7
1.8 %
23
3.2 %
0.191
Tetracyclines
1
0.3 %
4
0.6 %
0.665
Hospital characteristics
U.S. region
Midwest
118
31.1 %
234
32.5 %
< 0.001
Northeast
60
15.8 %
91
12.7 %
South
167
44.1 %
254
35.3 %
West
34
9.0 %
140
19.5 %
Number of beds
< 200
41
10.8 %
78
10.8 %
0.011
200–299
65
17.2 %
165
22.9 %
300–499
143
37.7 %
292
40.6 %
500+
130
34.3 %
184
25.6 %
Teaching
212
55.9 %
292
40.6 %
< 0.001
Urban
357
94.2 %
696
96.8 %
0.038
Hospital mortality
63
16.6 %
170
23.6 %
0.007
Abbreviations: IET inappropriate empiric therapy, ECF extended care facility, HCA healthcare-associated, MDR-AB multidrug-resistant Acinetobacter baumannii, ICU intensive care unit
Fig. 2
Mortality and inappropriate empiric therapy. HCA healthcare-associated
×
In a regression model designed to explore the impact of MDR on the risk of IET exposure, MDR-AB was the single strongest predictor of receiving IET (adjusted relative risk ratio 5.5, 95 % CI 4.0–7.7, p < 0.001) (Table 4). The confirmatory analyses produced similar risk ratios (Table 4).
Table 4
Adjusted risk of inappropriate empiric therapy and hospital mortality
Risk of IET in the setting of MDR-AB
Marginal effect, IET in non-MDR-AB
Marginal effect, IET in MDR-AB
Adjusted relative risk ratio (95 % CI)
p Value
Method
Parse model
13.8 %
76.2 %
5.5 (4.0–7.7)
< 0.001
Propensity score (based on 204 matched pairs; 81.0 % matched)
13.4 %
73.9 %
5.5 (3.6–8.4)
< 0.001
Nonparse model
14.4 %
75.6 %
5.3 (3.7–7.4)
< 0.001
Risk of death in the setting of IET
Marginal effect, mortality in non-IET
Marginal effect, mortality in IET
Adjusted relative risk ratio (95 % CI)
p Value
Method
Parse model
15.9 %
24.3 %
1.53 (1.21–1.93)
< 0.001
Propensity score (based on 226 matched pairs; 59.6 % matched)
15.0 %
27.8 %
1.85 (1.35–2.54)
< 0.001
Nonparse model
14.5 %
25.6 %
1.76 (1.36–2.28)
< 0.001
IET inappropriate empiric therapy, MDR-AB multidrug-resistant Acinetobacter baumannii
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In a nonparse generalized regression model adjusting for all confounders (demographics, comorbidities, severity of illness measures, hospital characteristics), IET was associated with an increased risk of in-hospital mortality (adjusted relative risk ratio 1.76; 95 % CI 1.36–2.28, p < 0.001 (Table 4). The parse model and propensity-matched analysis produced similar risk ratios.
Discussion
In this large, multicenter cohort study, we have demonstrated that CA and HCA pneumonia and sepsis are rarely caused by AB. However, when AB is present, it is most often MDR. Moreover, harboring MDR puts patients at a fivefold increased risk of receiving IET, which is in turn associated with increased hospital mortality.
Multiple investigators have documented the exceedingly high and rising rate of AB resistance. In a multicenter microbiology database study in the United States, we noted a rise in MDR-AB from 21.4 % between 2003 and 2005 to 35.2 % in the 2009–2012 period [3]. Similarly, the Center for Disease Dynamics, Economics & Policy (CDDEP) reported an MDR-AB increase from 32.1 % in 2009 to 51.0 % in 2010 [2]. The discrepancy between the two studies reflects the populations evaluated and the definitions of MDR applied. While our prior investigation was limited to only patients with severe sepsis and septic shock, the CDDEP surveillance included all infection sources. Additionally, we limited drug definitions to those where clinical efficacy data were available, while CDDEP included all pertinent drug categories.
Our present study, though not longitudinal, confirms the high probability of MDR-AB, though the rate is higher than that in either of the surveillance studies. Although the our examined population is more similar to that in our previous surveillance study than to the CDDEP surveillance, the IET definition is more in line with that of the CDDEP [19]. Because our data represent years 2009–2013, the high prevalence may simply be consistent with continued growth of this resistant pathogen beyond the time frame examined in either of the previous surveillance efforts.
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We confirm that antimicrobial resistance confers a high risk for IET. A previous single-center study reported that having severe sepsis or septic shock caused by carbapenem-resistant AB doubled the risk of IET [14]. This is the case for any gram-negative pathogen of severe sepsis or septic shock [24]. In the present study, the effect size was even greater, with a more than fivefold increase in the relative risk of receiving IET compared with non-MDR-AB. This suggests that clinicians should consider broad empiric coverage when AB is either suspected or identified by rapid testing.
In sepsis and pneumonia, it has been shown repeatedly that IET increases hospital mortality two- to fourfold and that escalation of treatment in response to culture results fails to alter this outcome [4‐13]. Specific to AB sepsis and septic shock patients, Shorr et al. recently reported a significantly elevated risk of mortality associated with IET (risk ratio 1.42, 95 % CI 1.10–1.58, p = 0.015) [14]. We confirm this observation in a cohort of patients with AB pneumonia or sepsis. However, this association has not always been found in studies of AB infection. While researchers in two additional cohort studies reported a two- to sixfold rise in hospital mortality in association with IET for AB, six other study groups failed to detect such an association [25‐32]. Though it is not clear why such a well-recognized relationship would not exist specifically in the setting of AB, there are a number of potential reasons for this divergence. Some of the previous studies suffer from several methodological issues, such as small sample size, incomplete adjustment for or unmeasured confounders, and overadjusting for some factors that may be collinear.
Our study has a number of strengths and limitations. It included a large multicenter cohort representative of U.S. institutions and thus has broad generalizability. Though largely representative of U.S. institutions overall, the southern portion of the United States is overrepresented in the database. Although this made the study susceptible to bias, particularly selection bias, we dealt with it by setting a priori enrollment criteria and definitions for the main exposures and outcomes. Though some misclassification is possible, the main exposures (MDR-AB, IET) and outcomes (IET, hospital mortality) are minimally susceptible to misclassification. At the same time, in at least some of the identified cases, AB might have represented colonization rather than true infection. Additionally, the fact that fully one-third of all MDR-AB were isolates from cases defined as CA suggests that some misclassification may exist in this group; that is, it is possible that we were unable to identify these patients’ exposure to the healthcare system with the variables available in the current database. Although confounding is a potential issue in observational studies, we attempted to eliminate this through regression analyses using a large number of potentially confounding variables. Nevertheless, the possibility of residual confounding remains.
Conclusions
In this largest representative multicenter study to date, although AB was a rare pathogen in CA or HCA pneumonia or sepsis, over 80 % of the AB isolates exhibited MDR. MDR increased the risk of receiving IET fivefold. In turn, IET was associated with increased risk of in-hospital mortality.
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Key messages
AB is a rare pathogen in community-acquired or healthcare-associated pneumonia or sepsis.
Eighty percent of all AB in this population is MDR.
MDR raises the risk of receiving inappropriate empiric therapy fivefold.
Inappropriate empiric therapy increases the risk of hospital mortality.
Abbreviations
AB, Acinetobacter baumannii; CA, community-acquired; CDDEP, Center for Disease Dynamics, Economics & Policy; ECF, extended care facility; HCA, healthcare-associated; I, intermediate; ICU, intensive care unit; IET, inappropriate empiric therapy; IRB, institutional review board; MDR, multidrug resistance; PDR, pandrug-resistant; R, resistant; RRR, relative risk ratio; S, susceptible; XDR, extensively drug resistant
Funding
This study was supported by a grant from The Medicines Company, Parsippany, NJ, USA.
Authors’ contributions
MDZ, KS, WF, and AFS contributed substantially to the study design, the data interpretation, and the writing of the manuscript. BHN had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. BHN contributed substantially to the study design, the data analysis, and the writing of the manuscript. All authors read and approved the final manuscript.
Competing interests
MDZ, an employee of EviMed Research Group, LLC, has served as a consultant to and/or received research funding from The Medicines Company, Pfizer, Astellas, Tetraphase, Theravance, and Merck. BHN in an employee of OptiStatim, which has received funding from EviMed Research Group, LLC, to conduct this study. KS is an employee and stockholder of The Medicines Company. WF is an employee and stockholder of The Medicines Company. Although KS and WF are employees of the sponsor and participated in the study as coinvestigators, the larger sponsor had no role in the study design, the data analysis or interpretation, or publication decisions. AFS has served as a consultant to, received research support from, or been a speaker for Abbott, Actavis, Alios BioPharma, Astellas Pharma, AstraZeneca, Bayer, Bristol-Myers Squibb, Cardeas Pharma, The Medicines Company, Merck, Pfizer, Roche, Tetraphase Pharmaceuticals, Theravance Biopharma, and Wockhardt Pharma.
Consent for publication
All authors have reviewed and approved the manuscript for publication.
Ethical approval and consent to participate
Because this study used already-existing, HIPAA-compliant, fully de-identified data, it was exempt from IRB review.
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