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01.12.2017 | Research article | Ausgabe 1/2017 Open Access

BMC Infectious Diseases 1/2017

Carbapenem resistance, inappropriate empiric treatment and outcomes among patients hospitalized with Enterobacteriaceae urinary tract infection, pneumonia and sepsis

Zeitschrift:
BMC Infectious Diseases > Ausgabe 1/2017
Autoren:
Marya D. Zilberberg, Brian H. Nathanson, Kate Sulham, Weihong Fan, Andrew F. Shorr
Abbreviations
CA
Community-acquired
CRE
Carbapenem-resistant Enterobaceriaceae
CSE
Carbapenem-sensitive Enterobacteriaceae
HCA
Healthcare-associated
ICU
Intensive care unit
IET
Inappropriate empiric therapy
LOS
Length of stay
UTI
Urinary tract infection

Background

Initial antibiotic therapy affects outcomes in severe infection. For empiric therapy to have a benefit on patient outcomes, it must not only be given in a timely manner but must also be active in vitro against the infecting pathogen. Many studies indicate that either delaying antibiotic therapy or selecting a treatment to which the infecting pathogen is non-susceptible increases the risk for death 2–5-fold [ 113]. Therefore, clinicians must be aware of the common pathogens in specific infectious syndromes and of local antimicrobial susceptibility patterns in order to make appropriate choices for antimicrobial therapies. Unfortunately, rapidly rising rates of resistance and shifting resistance patterns render ensuring appropriate empiric coverage a challenge [ 14].
Recently, the Centers for Disease Control and Prevention have identified carbapenem-resistance among Enterobacteriaceae as an urgent threat in the US [ 15]. Though Enterobacteriaceae are common pathogens in pneumonia, urinary tract infections and sepsis and thus are often treated in most empiric coverage recommendations, the escalating frequency of carbapenem resistance in these pathogens makes ensuring initially appropriate antimicrobial treatment in areas where carbapenem-resistant Enterobacteriaceae (CRE) are prevalent nearly impossible [ 13, 14, 1619]. Furthermore, administering broad-spectrum agents to all severely ill patients in order not to miss some individual with a rare highly resistant pathogen is not a sustainable practice, since the concerns for promoting further resistance may outweigh any potential benefit to patient-specific outcomes. In this way, the dilemma of CREs amplifies the tension between public (preservation of antimicrobial activity) and patient-level (optimizing clinical outcomes) health imperatives.
It remains unclear if the nexus between inappropriate therapy and outcomes seen with other pathogens exists in the case of infections due to CRE. Few analyses have specifically addressed this issue, while some that have attempted this lacked the ability to delineate the impact of inappropriate empiric therapy of CREs on attributable morbidity or on resources such as length of stay (LOS) [ 20, 21]. To understand better the relationship between carbapenem-resistance, choice of inappropriate empiric therapy (IET), and key hospital outcomes, we conducted a cohort study of patients admitted to the hospital with community-onset urinary tract infections (UTI), pneumonia and sepsis due to Enterobacteriaceae.

Methods

This was a multi-center retrospective cohort study of patients admitted to the hospital with pneumonia, sepsis and UTI (referred to from here on as “UTI”), or sepsis from another source in the Premier Research database in the years 2009–2013. We hypothesized that infection with a CRE phenotype increased the risk of receiving IET. In turn, we hypothesized that the receipt of IET is adversely associated with hospital mortality, LOS, and costs.
Because this study used already existing fully de-identified retrospective data, it was exempt from IRB review.
Since the data source was the same and methods utilized in this study were similar to those used in our previous study, please refer to that paper for details [ 22].

Patient population

Patients were included if they were adults (age ≥ 18 years) hospitalized with a UTI International Classification of Diseases, version 9, Clinical Modification (ICD-9-CM) codes (principal diagnosis 112.2, 590.1, 590.11, 590.2, 590.3, 590.8.590.81, 595, 597, 599 or 996.64, or principal sepsis diagnosis [see below] with UTI as a secondary diagnosis), pneumonia ICD-9-CM codes (principal diagnosis 481–486, or respiratory failure codes [518.81 or 518.84] with pneumonia as a secondary diagnosis) or sepsis codes from another source (principal diagnosis 038, 038.9, 020.0, 790.7, 995.92 or 785.52, or respiratory failure codes [518.81 or 518.84] with sepsis coded as a secondary diagnosis) [ 2327]. In order to eliminate confounding of the outcomes by pre-infection onset hospital events, only patients with infection present on admission, as evidenced by antibiotic treatment beginning within the first 2 days of hospitalization and continuing for at least 3 consecutive days, or until discharge, were included [ 2426]. Patients were excluded if they were transferred from another acute care facility, if they were diagnosed with cystic fibrosis, or if their hospital length of stay (LOS) was 1 day or less. Those who met criteria for both UTI and sepsis or pneumonia and sepsis were included in the UTI or pneumonia group, respectively. Those with both UTI and pneumonia were analyzed with the pneumonia group. Patients were followed until death in or discharge from the hospital.

Data source

The data for the study derived from Premier Research database, an electronic laboratory, pharmacy and billing data repository, for years 2009 through 2013, which contains approximately 15% of all hospitalizations nationwide. For detailed description of the dataset, please, refer to citation #22.

Baseline variables

We classified each community-onset infection (UTI, pneumonia or sepsis) as healthcare-associated (HCA) if one or more of the following risk factors was present: 1) prior hospitalization within 90 days of the index hospitalization, 2) hemodialysis, 3) admission from a long-term care facility, 4) immune suppression [ 3, 6, 16, 2326]. All other infections were considered to be community-acquired (CA). For other patient factors and hospital-level variables, please see citation #22.

Microbiology and treatment variables and definitions

Urinary, blood and respiratory cultures had to be obtained within the first 2 days of hospitalization.
The following organisms were defined as Enterobacteriaceae of interest:
1.
Escherichia coli
 
2.
Klebsiella pneumoniae
 
3.
Klebsiella oxytoca
 
4.
Enterobacter cloacae
 
5.
Enterobacter aerogenes
 
6.
Proteus mirabilis
 
7.
Proteus spp.
 
8.
Serratia marcescens
 
9.
Citrobacter freundii
 
10.
Morganella morganii
 
11.
Providencia spp.
 
Premier database receives organism susceptibility reports from individual institutions’ laboratories as S (susceptible), I (intermediate) or R (resistant). Although no MIC data are available in the database, all microbiology testing was performed locally at the institutions contributing the data and conformed to the CLSI standards. Carbapenem-resistant Enterobacteriaceae were defined as one of the above organisms where susceptibility testing yielded an I or R result to at least one of the four carbapenems: imipenem, meropenem, ertapenem or doripenem.
IET was present if the antibiotic administered for the infection did not cover the organism or if appropriate coverage did not start within 2 days of the positive culture being obtained.

Statistical analyses

We compared characteristics of patients infected with CRE to those infected with carbapenem-susceptible Enterobacteriaceae (CSE) and those treated with IET to those treated with non-IET. All unadjusted comparisons were done using standard methods described in detail in citation #22.
We developed a generalized logistic regression model to explore the relationship between CRE and the risk of IET. Covariates in the model were identical to those in citation #22. We calculated the relative risk ratio with 95% confidence intervals of receiving IET for CRE vs. CSE based on Huber-White robust standard errors clustered at the hospital level [ 28]. Consistent with our prior study, we confirmed our results in a non-parse model and a propensity matched model with propensity for CRE derived from a logistic regression model using the non-parse model’s predictors [ 22]. To explore the impact of IET on hospital mortality, LOS and costs, we developed hierarchical regression models with hospitals as random effects along with confirmatory propensity-matched models.
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 (StataCorp LP, College Station, TX).

Results

Among 230,086 patients presenting to the hospital with a UTI, pneumonia or sepsis, 40,137 (17.4%) met the inclusion criteria for Enterobacteriaceae of which the majority were UTI (54.2%), with the remainder either pneumonia (13.1%) or sepsis (32.7%). Among all patients with Enterobacteriaceae, 1227 (3.1%) had 1938 CRE organisms (Table 1). The prevalence of CRE among the Enterobacteriaceae ranged from 2.9% in UTI to 3.6% in pneumonia. Notably, over 85% of patients in both the CRE and CSE groups had a sepsis diagnosis code at some point during the hospitalization.
Table 1
Individual CRE organisms and their frequencies
CRE organism name
CRE organism Count
% of Total CRE
% of the Total patients a
( N = 1938)
( N = 1938)
( N = 1227)
Klebsiella pneumoniae
724
37.4%
59.0%
Proteus mirabilis
370
19.1%
30.2%
Escherishia coli
294
15.2%
24.0%
Enterobacter cloacae
128
6.6%
10.4%
Providencia spp
94
4.9%
7.7%
Serratia marcescens
87
4.5%
7.1%
Morganella morganii
87
4.5%
7.1%
Enterobacter aerogenes
40
2.1%
3.3%
Proteus spp.
27
1.4%
2.2%
Citrobacter freundii
27
1.4%
2.2%
Klebsiella oxytoca
22
1.1%
1.8%
Enterobacter other
13
0.7%
1.1%
Citrobacter other
14
0.7%
1.1%
Serratia other
6
0.3%
0.5%
Klebsiella other
5
0.3%
0.4%
aSum adds up to >100%, as some patients had >1 CRE organism
Those with CRE were younger (66.6+/−15.3 vs. 69.1+/−15.9 years, p < 0.001), and more likely to be African-American (19.7% vs. 14.0%, p < 0.001) than those with CSE. Many of the individual chronic conditions were more prevalent in the CRE than CSE group, and the mean Charlson comorbidity index reflected this (2.0+/−2.0 vs. 1.9+/−2.1, p = 0.009) (Table 2). CRE was more common than CSE in the West and the Northeast, in urban hospitals, in those of medium size (200–499 beds) and in teaching hospitals ( p < 0.001 for each comparison) (Table 2). Large hospitals (500+ beds) were less likely to have CRE than CSE (Table 2).
Table 2
Baseline characteristics
 
CSE
%
CRE
%
P-value
N = 38,910
N = 1227
Mean age, years (SD)
69.1 (15.9)
 
66.6 (15.3)
 
<0.001
Gender: male
16,273
41.8%
642
52.3%
<0.001
Race
 White
28,295
72.7%
821
66.9%
<0.001
 Black
5464
14.0%
242
19.7%
 Hispanic
1069
2.7%
32
2.6%
 Other
4082
10.5%
132
10.8%
Admission Source
 Non-healthcare facility (including from home)
25,559
65.7%
776
63.2%
<0.001
 Clinic
1285
3.3%
27
2.2%
 Transfer from ECF
3697
9.5%
266
21.7%
 Transfer from another non-acute care facility
473
1.2%
22
1.8%
 Emergency Department
7766
20.0%
132
10.8%
 Other
130
0.3%
4
0.3%
Elixhauser Comorbidities
 Congestive heart failure
9623
24.7%
329
26.8%
0.096
 Valvular disease
3112
8.0%
96
7.8%
0.825
 Pulmonary circulation disease
2323
6.0%
93
7.6%
0.020
 Peripheral vascular disease
4285
11.0%
169
13.8%
0.002
 Paralysis
4085
10.5%
271
22.1%
<0.001
 Other neurological disorders
8668
22.3%
348
28.4%
<0.001
 Chronic pulmonary disease
11,035
28.4%
371
30.2%
0.151
 Diabetes without chronic complications
11,616
29.9%
420
34.2%
0.001
 Diabetes with chronic complications
3809
9.8%
141
11.5%
0.049
 Hypothyroidism
6764
17.4%
224
18.3%
0.428
 Renal failure
10,810
27.8%
446
36.3%
<0.001
 Liver disease
2084
5.4%
65
5.3%
0.929
 Peptic ulcer disease with bleeding
17
0.0%
1
0.1%
0.428
 AIDS
12
0.0%
0
0.0%
1.000
 Lymphoma
604
1.6%
21
1.7%
0.657
 Metastatic cancer
1787
4.6%
40
3.3%
0.027
 Solid tumor without metastasis
1569
4.0%
34
2.8%
0.026
 Rheumatoid arthritis/collagen vascular
1721
4.4%
45
3.7%
0.204
 Coagulopathy
5350
13.7%
139
11.3%
0.015
 Obesity
6095
15.7%
191
15.6%
0.926
 Weight loss
6855
17.6%
340
27.7%
<0.001
 Fluid and electrolyte disorders
21,332
54.8%
378
30.8%
0.764
 Chronic blood loss anemia
545
1.4%
24
2.0%
0.105
 Deficiency anemia
15,154
38.9%
598
48.7%
<0.001
 Alcohol abuse
1367
3.5%
33
2.7%
0.122
 Drug abuse
923
2.4%
35
2.9%
0.278
 Psychosis
2358
6.1%
81
6.6%
0.435
 Depression
5854
15.0%
174
14.2%
0.404
 Hypertension
24,938
64.1%
781
63.7%
0.752
Charlson Comoribidity Score
 0
12,010
30.9%
334
27.2%
<0.001
 1
7855
20.2%
230
18.7%
 2
7902
20.3%
244
19.9%
 3
5118
13.2%
180
14.7%
 4
2897
7.4%
146
11.9%
 5+
3128
8.0%
93
7.6%
 Mean (SD)
1.9 (2.1)
 
2.0 (2.0)
 
0.009
 Median [IQR]
1 [0,3]
 
2 [0, 3]
 
<0.001
Hospital Characteristics
 Census region
  Midwest
10,531
27.1%
288
23.5%
<0.001
  Northeast
5297
13.6%
336
27.4%
  South
16,203
41.6%
310
25.3%
  West
6879
17.7%
293
23.9%
Number of Beds
  < 200
6589
16.9%
192
15.6%
<0.001
 200 to 299
8779
22.6%
338
27.5%
 300 to 499
12,691
32.6%
421
34.3%
 500+
10,851
27.9%
276
22.5%
 Teaching
14,609
37.5%
566
46.1%
<0.001
 Urban
35,079
90.2%
1167
95.1%
<0.001
CSE carbapenem sensitive Enterobacteriaceae, CRE carbapenem resistant Enterobacteriaceae, SD standard deviation, ECF extended care facility, AIDS acquired immune deficiency syndrome, IQR interquartile range
In both the CRE and CSE groups, over one-half the patients had the diagnosis of UTI, with the remaining divided between sepsis (33.3% CRE vs. 32.7% CSE) and pneumonia (15.2% CRE vs. 13.0% CSE) (Table 2). Patients infected with CRE were more likely to have a HCA infection (58.5% vs. 35.4%, p < 0.001) along with a greater illness severity by day 2 of admission (ICU 56.0% vs. 40.8%, p < 0.001; mechanical ventilation 35.6% vs. 15.7%, p < 0.001; though not vasopressors 16.7% vs. 14.9%, p = 0.081) than CSE patients (Table 3). Although among the CRE group there was a higher prevalence of empiric use of carbapenems, aminoglycosides and polymyxins than in those eventually found to be infected with a CSE, those with CRE infections were also significantly more likely to receive IET (52.8% vs. 11.1%, p < 0.001). Unadjusted hospital mortality median LOS and costs among CRE were also significantly greater than CSE, and these differences held across all infection types (Table 3).
Table 3
Infection characteristics, treatment and outcomes
 
CSE
%
CRE
%
P-value
N = 38,910
N = 1227
Infection characteristics
 Sepsis
12,726
32.7%
409
33.3%
0.039
 Pneumonia
5060
13.0%
187
15.2%
 UTI
21,124
54.3%
631
51.4%
 HCA
13,782
35.4%
718
58.5%
<0.001
Illness severity measures by day 2
 ICU admission
15,876
40.8%
687
56.0%
<0.001
 Mechanical ventilation
6092
15.7%
437
35.6%
<0.001
 Vasopressors
5798
14.9%
205
16.7%
0.081
Antibiotics administered by day 2
 Aminoglycosides
3843
9.9%
242
19.7%
<0.001
 Antipseudomonal penicillins
6403
16.5%
313
25.5%
<0.001
 Antipseudomonal floroquinolones
18,468
47.5%
406
33.1%
<0.001
 Antipseudomonal penicillins with beta-lactamase inhibitors
19,727
50.7%
617
50.3%
0.775
 Extended spectrum cephalosporins
13,327
34.3%
415
33.8%
0.755
 Folate pathway inhibitors
251
0.6%
12
1.0%
0.155
 Penicillins with beta-lactamase inhibitors
854
2.2%
26
2.1%
0.837
 Polymyxins
126
0.3%
24
2.0%
<0.001
 Tetracyclines
248
0.6%
6
0.5%
0.519
 Tigecycline
586
1.5%
86
7.0%
<0.001
 Aztreonam
1740
4.5%
56
4.6%
0.878
Empiric treatment appropriateness
 Non-IET
32,197
82.7%
513
41.8%
<0.001
 IET
4336
11.1%
648
52.8%
 Indeterminate
2337
6.0%
66
5.4%
Hospital outcomes
 Mortality
3958
10.2%
178
14.5%
<0.001
 Mean (SD) LOS, days
9.6 (10.7)
 
15.6 (17.4)
 
<0.001
 Median [IQR] LOS, days
7 [4, 11]
 
10 [6, 18]
 
<0.001
 Mean (SD) costs, $
20,601 (29702)
 
38,494 (46,964)
 
<0.001
 Median [IQR] costs, $
13,020 [7501, 24,237]
 
22,909 [12,988, 42,815]
 
<0.001
Hospital outcomes stratified by infection type
 UTI
  Mortality
1873
8.9%
78
12.4%
0.002
  Mean (SD) LOS, days
9.0 (9.4)
 
14.6 (15.9)
 
<0.001
  Median [IQR] LOS, days
7 [4, 11]
 
10 [6, 17]
 
<0.001
  Mean (SD) costs, $
19,036 (24,494)
 
33,400 (37,662)
 
<0.001
  Median [IQR] costs, $
12,082 [7104, 21,822]
 
21,154 [12,687, 39,374]
 
<0.001
 Sepsis
  Mortality
1660
13.0%
81
19.8%
<0.001
  Mean (SD) LOS, days
10.9 (12.6)
 
18.0 (20.8)
 
<0.001
  Median [IQR] LOS, days
7 [4, 13]
 
11 [7, 21]
 
<0.001
  Mean (SD) costs, $
26,793 (37,390)
 
50,038 (60,602)
 
<0.001
  Median [IQR] costs, $
15,614 [8584, 30,317]
 
27,264 [14,581, 57,825]
 
<0.001
 Pneumonia
  Mortality
425
8.4%
19
10.2%
0.395
  Mean (SD) LOS, days
9.2 (10.4)
 
13.4 (13.0)
 
<0.001
  Median [IQR] LOS, days
7 [4, 10]
 
9 [6, 16]
 
<0.001
  Mean (SD) costs, $
19,250 (25,743)
 
30,432 (35,089)
 
<0.001
  Median [IQR] costs, $
11,826 [7076, 21,100]
 
19,820 [12,220, 35,713]
 
<0.001
CSE carbapenem sensitive Enterobacteriaceae, CRE carbapenem resistant Enterobacteriaceae, UTI urinary tract infection, HCA healthcare-associated, ICU intensive care unit, IET inappropriate empiric therapy
Comparing the cohort of 37,694 patients (93.9% of all patients with Enterobacteriaceae) with valid antimicrobial treatment data, 32,710 (86.8%) received appropriate therapy (Table 4). While patients receiving appropriate empiric therapy were more likely to have UTI or sepsis than those in the IET group, the frequency of pneumonia was higher among patients on IET (20.0%) than those on appropriate treatment (12.0%) ( p < 0.001) (Table 4). As for the unadjusted hospital outcomes, mortality was higher in patients receiving IET than appropriate therapy (12.2% vs. 9.9%, p < 0.001). Both LOS and costs were significantly higher in the IET group than in the group receiving non-IET (Table 4). These relationships generally held irrespective of the infection type (Table 4).
Table 4
Characteristics of the cohort based on the receipt of inappropriate empiric treatment
 
Non-IET
%
IET
%
P-value
N = 32,710
N = 4984
Baseline characteristics
 Mean age, years (SD)
69.0 (16.0)
 
69.4 (15.3)
 
0.094
 Gender: male
13,680
41.8%
2169
43.5%
0.024
 Race
  White
23,921
73.1%
3443
69.1%
<0.001
  Black
4384
13.4%
862
17.3%
  Hispanic
919
2.8%
163
3.3%
  Other
3486
10.7%
516
10.4%
 Admission Source
  Non-healthcare facility (including from home)
21,450
65.6%
3034
60.9%
<0.001
  Clinic
1093
3.3%
138
2.8%
  Transfer from ECF
2996
9.2%
759
15.2%
  Transfer from another non-acute care facility
379
1.2%
77
1.5%
  Emergency Department
6688
20.4%
959
19.2%
  Other
104
0.3%
17
0.3%
 Elixhauser Comorbidities
  Congestive heart failure
7836
24.0%
1509
30.3%
<0.001
  Valvular disease
2594
7.9%
425
8.5%
0.148
  Pulmonary circulation disease
1912
5.8%
358
7.2%
<0.001
  Peripheral vascular disease
3564
10.9%
577
11.6%
0.152
  Paralysis
3289
10.1%
770
15.4%
<0.001
  Other neurological disorders
7227
22.1%
1269
25.5%
<0.001
  Chronic pulmonary disease
9079
27.8%
1663
33.4%
<0.001
  Diabetes without chronic complications
9695
29.6%
1623
32.6%
<0.001
  Diabetes with chronic complications
3152
9.6%
524
10.5%
0.052
  Hypothyroidism
5645
17.3%
942
18.9%
0.004
  Renal failure
9024
27.6%
1540
30.9%
<0.001
  Liver disease
1774
5.4%
245
4.9%
0.138
  Peptic ulcer disease with bleeding
15
0.0%
2
0.0%
1.000
  AIDS
8
0.0%
4
0.1%
0.063
  Lymphoma
508
1.6%
74
1.5%
0.716
  Metastatic cancer
1543
4.7%
182
3.7%
0.001
  Solid tumor without metastasis
1335
4.1%
163
3.3%
0.006
  Rheumatoid arthritis/collagen vascular
1422
4.3%
215
4.3%
0.914
  Coagulopathy
4626
14.1%
540
10.8%
<0.001
  Obesity
5079
15.5%
822
16.5%
0.081
  Weight loss
5583
17.1%
1117
22.4%
<0.001
  Fluid and electrolyte disorders
17,961
54.9%
2702
54.2%
0.357
  Chronic blood loss anemia
459
1.4%
79
1.6%
0.313
  Deficiency Anemia
12,735
38.9%
2096
42.1%
<0.001
  Alcohol abuse
1139
3.5%
163
3.3%
0.446
  Drug abuse
789
2.4%
103
2.1%
0.135
  Psychosis
1979
6.1%
294
5.9%
0.676
  Depression
4859
14.9%
806
16.2%
0.018
  Hypertension
20,987
64.2%
3154
63.3%
0.229
 Charlson Comoribidity Score
  0
10,353
31.7%
1239
24.9%
<0.001
  1
6517
19.9%
1072
21.5%
  2
6595
20.2%
1047
21.0%
  3
4223
12.9%
757
15.2%
  4
2400
7.3%
465
9.3%
  5+
2622
8.0%
404
8.1%
  Mean (SD)
1.9 (2.1)
 
2.0 (2.0)
 
<0.001
  Median [IQR]
1 [0, 3]
 
2 [1 3]
 
<0.001
Infection characteristics and treatment
 Infection characteristics
  Sepsis
10,736
32.8%
1468
29.5%
<0.001
  Pneumonia
3936
12.0%
995
20.0%
  UTI
18,038
55.1%
2521
50.6%
  HCA
11,413
34.9%
2221
44.6%
<0.001
  CRE
513
1.6%
648
13.0%
<0.001
 Illness severity
  ICU admission
13,524
41.3%
2074
41.6%
0.720
  Mechanical ventilation
5064
15.5%
1062
21.3%
<0.001
  Vasopressors
4929
15.1%
709
14.2%
0.111
 Antibiotics administered
  Aminoglycosides
3694
11.3%
351
7.0%
<0.001
  Antipseudomonal penicillins
6199
19.0%
347
7.0%
<0.001
  Antipseudomonal floroquinolones
15,995
48.9%
2480
49.8%
0.258
  Antipseudomonal penicillins with beta-lactamase inhibitors
16,874
51.6%
2008
40.3%
<0.001
  Extended spectrum cephalosporins
12,174
37.2%
1134
22.8%
<0.001
  Folate pathway inhibitors
225
0.7%
36
0.7%
0.809
  Penicillins with beta-lacatamase inhibitors
681
2.1%
147
2.9%
0.005
  Polymyxins
102
0.3%
32
0.6%
<0.001
  Tetracyclines
210
0.6%
15
0.3%
0.004
  Tigecycline
485
1.5%
110
2.2%
<0.001
  Aztreonam
1319
4.0%
258
5.2%
<0.001
Hospital Characteristics
 Area
  Midwest
8848
27.0%
1133
22.7%
<0.001
  Northeast
4397
13.4%
950
19.1%
  South
13,579
41.5%
1951
39.1%
  West
5886
18.0%
950
19.1%
 Number of Beds
   < 200
5597
17.1%
744
14.9%
<0.001
  200 to 299
7508
23.0%
1171
23.5%
  300 to 499
10,540
32.2%
1781
35.7%
  500+
9065
27.7%
1288
25.8%
  Teaching
12,096
37.0%
1988
39.9%
0.217
  Urban
29,418
89.9%
4574
91.8%
<0.001
Hospital outcomes
  Mortality
3234
9.9%
607
12.2%
<0.001
  Mean (SD) LOS, days
9.0 (8.5)
 
14.7 (19.4)
 
<0.001
  Median [IQR] LOS, days
7 [4, 11]
 
9 [5, 16]
 
<0.001
  Mean (SD) costs, $
20,227 (25,616)
 
33,216 (49,567)
 
<0.001
  Median [IQR] costs, $
12,719 [7401, 23,275]
 
17,386 [9255, 35,625]
 
<0.001
Hospital outcomes stratified by infection type
 UTI
  Mortality
1548
8.6%
267
10.6%
<0.001
  Mean (SD) LOS, days
8.5 (7.8)
 
13.3 (17.1)
 
<0.001
  Median [IQR] LOS, days
6 [4, 10]
 
9 [5, 15]
 
<0.001
  Mean (SD) costs, $
18,103 (21,440)
 
28,069 (40,490)
 
<0.001
  Median [IQR] costs, $
11,862 [7015, 21,222]
 
16,209 [8828, 31,535]
 
<0.001
 Sepsis
  Mortality
1356
12.6%
260
17.7%
<0.001
  Mean (SD) LOS, days
9.9 (9.9)
 
18.9 (23.3)
 
<0.001
  Median [IQR] LOS, days
7 [4, 12]
 
12 [6, 22]
 
<0.001
  Mean (SD) costs, $
24,532 (32,043)
 
47,881 (64,812)
 
<0.001
  Median [IQR] costs, $
15,048 [8312, 28,558]
 
25,121 [12,382, 55,529]
 
<0.001
 Pneumonia
  Mortality
330
8.4%
80
8.0%
0.726
  Mean (SD) LOS, days
8.5 (7.6)
 
12.0 (17.6)
 
<0.001
  Median [IQR] LOS, days
7 [4, 10]
 
7 [4, 13]
 
<0.001
  Mean (SD) costs, $
18,220 (21,710)
 
24,623 (38,753)
 
<0.001
  Median [IQR] costs, $
11,742 [7125, 20,561]
 
13,040 [7393, 26,339]
 
<0.001
IET inappropriate empiric therapy, SD standard deviation, ECF extended care facility, AIDS acquired immune deficiency syndrome, IQR interquartile range, HCA healthcare-associated, CSE carbapenem sensitive Enterobacteriaceae, CRE carbapenem resistant Enterobacteriaceae, UTI urinary tract infection, ICU intensive care unit, IQR interquartile range 25–75%
In a parse generalized regression model exploring the impact of CRE on the risk of IET, resistance was the single strongest predictor of receiving IET (adjusted relative risk ratio 3.95, 95% confidence interval 3.51, 4.46, p < 0.001) (Table 5). The confirmatory analyses produced similar risk ratios (Table 5).
Table 5
Adjusted risk of inappropriate empiric therapy, hospital mortality, excess LOS and costs
 
Marginal effect, CSE
Marginal effect, CRE
Adjusted relative risk ratio/excess days or costs (95% confidence interval)
P-value
Risk of IET
 Parse Model
11.8%
47.7%
3.95 (3.51, 4.46)
<0.001
 Propensity score (based on 100% CRE cases matched to CSE 1:1)
13.1%
55.8%
4.27 (3.64, 5.00)
<0.001
 Non-parse model
11.9%
47.7%
4.00 (3.48, 4.59)
<0.001
 
Marginal effect, non-IET
Marginal effect, IET
Adjusted relative risk ratio/excess days or costs (95% confidence interval)
P-value
Risk of death
 Hierarchical model
9.8%
11.0%
1.12 (1.03, 1.23)
0.013
 Propensity score (based on 96.4% IET cases matched to non-IET 1:1)
10.5%
11.9%
1.13 (1.01, 1.27)
0.030
Length of stay (days)
 Hierarchical model
8.2
13.4
5.2 (4.8, 5.6)
<0.001
 Propensity score (based on 96.4% IET cases matched to non-IET 1:1)
9.6
14.6
5.0 (4.4, 5.6)
<0.001
Hospital costs
 Hierarchical model
$20,508
$30,819
$10,312 ($9497, $11,126)
<0.001
 Propensity score (based on 96.4% IET cases matched to non-IET 1:1)
$22,005
$32,837
$10,831 ($9254, $12,409)
<0.001
IET inappropriate empiric therapy, CSE carbapenem sensitive Enterobacteriaceae, CRE carbapenem resistant Enterobacteriaceae
In a hierarchical 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 1.12; 95% confidence interval 1.03, 1.23, p = 0.013) (Table 5). In other hierarchical models, the excess LOS and costs associated with IET exposure were 5.2 days (95% confidence interval 4.8, 5.6, p < 0.001) and $10,312 (95% confidence interval $9497, $11,126, p < 0.001). Propensity-matched analyses produced similar estimates (Table 5).
An interaction term suggested a greater impact on mortality of IET in the setting of sepsis, which prompted a sensitivity analysis in the group whose organisms were cultured from blood. In this set of analyses, including 12,807 patients (186 CRE, 1.5%), the impact of IET on mortality was indeed greater (relative risk ratio 1.55, 95% confidence interval 1.18 to 2.03) than in the overall cohort.

Discussion

We demonstrate in this large multicenter observational cohort that among patients admitted from the community with a UTI, sepsis or pneumonia, over 17% have an infection with Enterobacteriaceae, of which approximately 3% are CRE. Although infrequent, the presence of CRE increases the risk of receiving IET substantially. In turn, receiving IET is associated with a rise in hospital mortality, LOS and costs, a rise particularly pronounced in patients with sepsis.
Multiple studies have noted an increase in the prevalence of CRE among patients with serious infections in the hospital. A recent US surveillance study reported the annual population incidence of CRE infections to be nearly 3 cases per 100,000 population [ 28]. A US Centers for Disease Control and Prevention study noted a rise in CRE prevalence from 1.2% in 2001 to 4.2% in 2011 [ 29]. The same study analyzing a different database, however, noted an increase in CRE from 0 in 2001 to 1.4% in 2010, echoing findings of other investigators [ 19, 30, 31]. Our findings are generally in agreement with these numbers. Although CRE incidence and prevalence are far lower than such common pathogens as methicillin-resistant Staphylococcus aureus or Clostridium difficile, there are few treatment alternatives for CRE, which underscores the need for more precise information about the epidemiology and outcomes related to CRE infections [ 32, 33]. Consequently, this study helps to address this need for more granular information regarding this pathogen. In addition we confirm that at this point, CRE is encountered most often as a urinary pathogen, which may mediate the otherwise high mortality rate associated with CRE infections. Despite this, the increasing frequency of this organism as a cause of sepsis indicates that CRE is poised to become a major contributor to infectious disease related mortality in the US.
Though thought of mostly as healthcare-associated pathogens, our data suggest that this may be too narrow a view. Namely, in our cohort, over 40% of patients with CRE did not have an identifiable exposure to the healthcare system. There are several potential sources for misclassifying this burden, one of which may be the 90-day period for prior hospitalization as a risk factor for HCA infection. Though it remains unclear how long the impact of prior hospitalization persists on the risk of resistance, and 90 days is a standard interval used in many other studies, in some investigations this period is longer [ 34]. Although a probable overestimate due to misclassification and because of limitations in the patient records, our data are not the first to bring into question this assumption in a US population. In the surveillance study of CRE by Guh et al., 2/3 of the cultures derived from the outpatient setting [ 35]. More importantly, 8% lacked any markers of healthcare exposure [ 35]. In an additional small study by Tang et al., community-acquired CRE accounted for 30% of all CRE infections [ 36]. Though higher in our study, the fact remains that persons with no ongoing relevant exposure to the healthcare system may still contract an infection with this organism. This finding is troubling in that it parallels what has been observed with extended-spectrum beta-lactamase carrying pathogens and their increasing prevalence in community-acquired infections [ 3740].
There is mounting evidence to demonstrate that rising antimicrobial resistance impedes clinical efforts at instituting appropriate empiric treatment [ 14]. We confirm the important role resistance plays in thwarting the ability to choose appropriately, whereby the risk of receiving IET in the setting of CRE rose 4-fold compared to CSE. In turn, though modest, IET’s adverse impact on hospital mortality is consistent with what has been reported in other infections [ 113]. The more pronounced impact of IET on hospital LOS (~5 excess days) and costs (~additional $10,000) is a novel finding for infections with CRE, and provides a sound rationale for investing in technologies that identify patients at risk for CRE more rapidly, particularly given that this is approximately double the attributable burden reported in infections caused by other resistant organisms [ 41]. Moreover, having a precise estimate of the attributable costs of these infections helps put into perspective the potential value of various prevention and treatment paradigms. It is methodologically challenging to estimate the attributable impact of carbapenem resistance on cost and LOS in nosocomial CRE infections since those outcomes are confounded by the cause of the initial hospitalization. Therefore, our findings help clarify this issue.
Our study has a number of strengths and limitations. The limitations that are common to both the current and previous studies are discussed in citation #22. Specific to the current analysis, a potential source of misclassification is a relatively high prevalence of Proteus mirabilis as a pathogen, as this microbe may have naturally occurring higher MICs for imipenem (Table 1) [ 42, 43]. Since susceptibility data in Premier are reported not by the MIC, but by susceptibility designation (S, I, R, see above in Methods), for the purpose of this analysis we had to presume that clinical adjudication occurred at each individual institution. However, this type of misclassification, if present, is likely to lead to an underestimate of the impact of CRE on outcomes, thus suggesting that in fact, CRE, when determined without this potential misclassification, may have an even greater effect on the risk of IET exposure.

Conclusions

In summary, CRE is an uncommon but important pathogen in community-onset UTI, pneumonia and sepsis. We confirm that, similar to other resistant organisms, it evades appropriate empiric treatment and exposure to IET worsens both clinical and economic outcomes. Although the true extent of the problem requires further study, our data confirm that a substantial proportion of CRE may be acquired in the community irrespective of exposure to the healthcare system. In sum, our study provides compelling evidence to hasten development of rapid identification methods and new antibiotic treatments in order to optimize empiric therapy among hospitalized patients with serious infections.

Acknowledgements

No one other than the listed authors participated in the study design, analysis, interpretation or manuscript drafting or revision.

Funding

Funding: This study was funded by a grant from The Medicines Company. Ms. Sulham and Ms. Fan are employees of The Medicines Company.
Sponsor role: Although Ms. Sulham and Ms. Fan are employees of the sponsor and participated in the study as co-investigators, the larger sponsor had no role in study design, data analysis or interpretation or publication decisions.

Availability of data and material

The data that support the findings of this study are available from Premier, Inc., but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

Authors’ contributions

MDZ contributed substantially to the study design, data interpretation, and the writing of the manuscript. BHN contributed substantially to the study design, data interpretation, and the writing of the manuscript. He 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. KS contributed substantially to the study design, data interpretation, and the writing of the manuscript. WF contributed substantially to the study design, data interpretation, and the writing of the manuscript. AFS contributed substantially to the study design, data interpretation, and the writing of the manuscript. All authors read and approved the final manuscript.

Competing interests

This study was supported by The Medicines Company.
Dr. Zilberberg is a consultant to The Medicines Company. Her employer, Evi Med Research Group, LLC, has received research grant support from The Medicines Company.
Dr. Nathanson is an employee of OptiStatim, LLC, who received grant support from Evi Med Research Group, LLC, for conducting this study.
Ms. Fan and Ms. Sulham are employees of and stockholders in The Medicines Company.
Dr. Shorr is a consultant and has received research grant support from The Medicines Company.
Drs. Zilberberg and Shorr have received grant support and have served as consultants to Pfizer, Merck, Inc., Tetraphase, Melinta, Asahi Kasei, Shionogi, Archaogen and Theravance.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study was a retrospective analysis of a de-identified database. As such, it is not considered human subject research.

Disclosure

This study was funded by The Medicines Company, Parsippany, NJ, USA. The data from this study were in part presented at ID Week 2016 meeting. I certify that all coauthors have seen and agree with the contents of the manuscript. I certify that the submission is not under review by any other publication.

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