|
|
Dispatch
Reference Group Choice and
Antibiotic Resistance Outcomes
Keith S. Kaye,*
John J. Engemann,* Essy Mozaffari,† and Yehuda Carmeli‡
Duke University Medical Center, Durham, North Carolina, USA; †Pfizer,
New York, New York, USA; and ‡Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Massachusetts, USA
Online Appendix
Several studies have analyzed attributable outcomes of patients infected
with resistant bacteria who are compared to reference patients infected
with corresponding, susceptible bacteria (1–32). We
have conducted two studies, both at teaching hospitals. Study 1 (methicillin-resistant
Staphylococcus aureus [MRSA]) was conducted at Duke University
Medical Center, a 900-bed tertiary care academic medical center, and Durham
Regional Hospital, a 350-bed community hospital, both located in Durham,
North Carolina. Study 2 (vancomycin-resistant enterococci [VRE]) was conducted
at Beth Israel Deaconess Medical Center, West Campus, a 320-bed urban
tertiary care teaching hospital in Boston, Massachusetts. In both studies,
data were abstracted from various sources, including computerized hospital
databases (e.g., accounting, administrative, infection control, and microbiology
databases) and patient medical records and were compiled into a single
dataset (Access, Microsoft Corp., Redmond, WA). In both studies, organisms
were identified from clinical specimens by using standard microbiologic
methods that are in accordance with the National Committee for Clinical
Laboratory Standards guidelines. Exact methods of data collection, assembly,
and microbiology are described elsewhere (33,34).
To control for confounding, we used multivariable analysis, examining
each of the outcomes independently. The following variables were analyzed
as potential confounders: patient demographics, admitting diagnosis, coexisting
conditions, and number of days in hospital and intensive care before cohort
inclusion. For study 2, propensity score for likelihood of being a VRE
case (35), having a major surgical procedure, and being
infected with Clostridium difficile or MRSA were also analyzed.
Statistical analysis was performed on Stata (Stata Corp., College Station,
TX) software and/or on SAS 8.1 (Cary, NC). Age was analyzed with the Student
t test. and other continuous and ordinal variables were compared
with the two-sided Wilcoxon rank sum test. The Fisher exact test was used
to analyze dichotomous variables. Spearman correlation coefficients were
calculated to detect trends among continuous variables (e.g., between
length of hospital stay and continuous independent variables and between
cost and continuous independent variables). Matched analyses were used
in study 2, for the analysis comparing VRE wound infection to control
patients with VSE wound infection (33,36).
Each outcome was examined independently, with multivariate analysis.
In both studies, death rates were analyzed with logistic regression (conditional
maximum-likelihood in the VRE study, to account for matching) and hospital
charges with linear regression. For the MRSA study, total hospital days
after infection were analyzed by using linear regression. For length of
hospital stay, semiparametric survival models with accelerated failure
time (Weibull) were used for the VRE analysis.
For multivariate linear regression, the following data transformation
was performed. In the MRSA study, cost and length of hospital stay were
log transformed and for the VRE study, cost was log transformed. No log
transformation was performed for logistic regression and survival analyses,
and no log transformation was performed for univariate or bivariate analyses.
The inverse log value was calculated for β coefficients of variables
included in the predictor models, and these effect measures were described
as multiplicative effects (ME) on length of stay and cost. All statistical
tests were two-tailed. A p <0.05 was considered significant.
Adjusted mean attributable outcomes per resistant infection (VRE and
MRSA) were calculated as follows for hospital days and charges. Charges
per VRE infection are used as an example:
Mean attributable charges per VRE infection = [(mean charges for control
patients) x (inverse log of β coefficient for adjusted VRE infection
variable)] – (mean charges for control patients)
Three groups were studied: 121 MRSA surgical site infection (SSI) cases,
193 uninfected surgical controls, and 165 control patients with MSSA SSI.
Descriptive characteristics of these groups and results of bivariate analyses
are in Appendix Table 1.
In the analysis comparing patients with MRSA SSI to uninfected controls,
in addition to MRSA, significant predictors of mortality included the
American Society of Anesthesiologists-Physical Status (ASA)score >3
and age >73 (Appendix Table 2). When patients
with MRSA SSI were compared to control patients with MSSA SSI, in addition
to MRSA, significant predictors of death included ASA score >3 and
age >61 years. This model was controlled for the confounding effects
of operative duration (Appendix Table 3).
In the analysis comparing patients with MRSA SSI to uninfected controls,
in addition to MRSA, other predictors of increased length of hospital
stay included ASA score, duration of surgery, and length of hospital stay
before surgery. This model was controlled for the confounding effects
of admission to the tertiary care hospital, diabetes, and renal disease
(Appendix Table 2). When patients with MRSA SSI
were compared to control patients with MSSA SSI, significant predictors
of increased length of hospital stay included ASA score, renal disease,
duration of surgery, and length of stay before infection. This model was
controlled for the confounding effects of diabetes mellitus and admission
to a tertiary care hospital (Appendix Table 3).
In the analysis comparing patients with MRSA SSI to uninfected controls,
in addition to MRSA, other predictors of increased cost included ASA score,
admission to tertiary care hospital, duration of surgery, length of hospital
stay, and intensive care unit (ICU) stay prior to surgery. This model
was controlled for the confounding effect of renal disease (Appendix
Table 2). When patients with MRSA SSI were compared to control patients
with MSSA SSI, significant predictors of increased cost, in addition to
MRSA, included ASA score, duration of surgery, length of hospital and
ICU stay before infection, and admission to a tertiary care hospital.
This model was controlled for the confounding effects of renal disease
and diabetes (Appendix Table 3).
Three groups of patients were studied: 99 VRE case patients with wound
infection, 280 matched controls who were not infected with enterococci,
and 213 control patients with VSE wound infections. Descriptive characteristics
and results of bivariate analyses are in Appendix Table
4.
In the analysis comparing patients with VRE wound infection to uninfected
controls, the impact of VRE wound infection on deaths was controlled for
the confounding effects of number of comorbid illnesses and admission
to the intensive care unit (ICU) (Appendix Table 5).
When patients with VRE wound infection were compared to control patients
with VSE wound infection, significant predictors of deaths included admission
to the ICU. This model was controlled for the confounding effects of surgery
and sex (Appendix Table 6).
In the analysis comparing patients with VRE wound infection to uninfected
controls, predictors of increased length of hospital stay, in addition
to VRE, included being transferred from another institution, renal disease,
malignancy, and admission to the ICU. This model was controlled for the
confounding effect of propensity score (i.e., likelihood of having a case
of VRE)] (Appendix Table 5). When patients with
VRE wound infection were compared to control patients with VSE wound infection,
significant predictors of increased length of stay included admission
to the ICU. This model was controlled for the confounding effects of duration
of hospitalization before cohort inclusion and malignancy (Appendix
Table 6).
In the analysis comparing patients with VRE wound infection to uninfected
controls, predictors of increased cost, in addition to VRE, included having
had surgery before cohort inclusion (Appendix Table
5). This model was controlled for the confounding effects of propensity
score (i.e., likelihood of being a VRE case) and duration of hospitalization
before cohort inclusion. When patients with VRE wound infection were compared
to control patients with VSE wound infection, significant predictors of
increased cost, in addition to VRE, included having had surgery before
inclusion in the cohort. This model was controlled for the confounding
effect of time in hospital before cohort inclusion.
The differences in results between the two analyses are much greater
for a virulent primary pathogen than for a nonvirulent, secondary invader.
When a virulent pathogen is studied (e.g., S. aureus), the infected
susceptible group (MSSA) is at much greater risk for adverse clinical
outcomes than the uninfected control group, and analyses comparing resistant
cases (MRSA) to these two control groups produce notably different results.
Enterococci are often nonvirulent secondary invaders (e.g., colonizers)
in wound infections and are frequently part of a mixed flora of infecting
pathogens rather than true primary pathogens. The results obtained when
patients with VRE wound infection were compared to patients not infected
with enterococci were similar to results obtained when patients with VSE
wound infections were used as controls. In our opinion, when resistant
pathogens of low virulence (e.g., VRE in wounds) are analyzed, the infected
susceptible (e.g., VSE) and uninfected control groups approximate one
another, and results of analyses comparing resistant cases to these two
control groups are similar.
Appendix
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| Appendix
Table 1. Study 1, patient characteristics, methicillin-resistant
Staphylococcus aureus (MRSA), controls not infected with S.
aureus and controls with methicillin-susceptible S. aureus
(MSSA) surgical site infections, bivariable analyses |
|
|
Variable
|
Cases, MRSA (%)
(n = 121)
|
Controls, uninfected patients (%)
(n = 193)
|
p value, (MRSA vs. uninfected controls)
|
Controls, MSSA (%)
(n = 165)
|
p value (MRSA vs. MSSA)
|
|
|
Age, mean ± SD, y
|
63.9 ± 15.4
|
57.3 ± 18.3
|
0.001
|
55.1 ± 17.4
|
<0.001
|
|
Male sex
|
55 (45.5)
|
92 (42.7)
|
0.73
|
90 (54.6)
|
0.15
|
|
Coexisting conditions
|
|
Diabetes mellitus
|
59 (48.8)
|
66 (34.2)
|
0.01
|
57 (34.6)
|
0.02
|
|
Hematologic disorder
|
1 (0.8)
|
1 (0.5)
|
1.00
|
2 (1.2)
|
1.00
|
|
HIV infection
|
0 (0.0)
|
1 (0.5)
|
1.00
|
0
|
1.00
|
|
Hypertension
|
64 (52.9)
|
75 (38.9)
|
0.02
|
80 (48.5)
|
0.48
|
|
Liver disease
|
4 (3.3)
|
1 (0.5)
|
0.07
|
2 (1.2)
|
0.25
|
|
Malignancy
|
15 (12.4)
|
14 (7.3)
|
0.16
|
13 (7.9)
|
0.23
|
|
Obesity
|
10 (8.3)
|
12 (6.2)
|
0.50
|
18 (10.9)
|
0.55
|
|
Peripheral vascular
disease
|
12 (9.9)
|
3 (1.6)
|
0.002
|
9 (5.5)
|
0.17
|
|
Pulmonary disease
|
21 (17.4)
|
23 (11.9)
|
0.19
|
32 (19.4)
|
0.76
|
|
Renal disease
|
19 (15.7)
|
9 (4.7)
|
0.002
|
13 (7.9)
|
0.06
|
|
Transplant
|
1 (0.8)
|
0
|
0.39
|
0
|
0.42
|
|
Tobacco use
|
16 (13.2)
|
20 (10.4)
|
0.47
|
24 (14.6)
|
0.86
|
|
Alcohol abuse
|
4 (3.3)
|
2 (1.0)
|
0.21
|
6 (3.6)
|
1.00
|
|
Hospital-related risk factors
|
|
Treatment at the
academic tertiary
care hospital
|
94 (77.8)
|
125 (64.8)
|
0.02
|
109 (66.1)
|
0.04
|
|
LOS before surgery,
median, IQR
|
1, 0–4
|
0, 0–3
|
0.02
|
0, 0–2
|
0.01
|
|
LOS before culture,
median, IQR
|
8, 5–14
|
NA
|
NA
|
5, 3–10
|
<0.001
|
|
Proportion of patients
with an ICU stay
before surgery
|
11 (9.1)
|
13 (7.9)
|
0.83
|
18 (9.3)
|
1.0
|
|
ASA score, median,
IQR
|
3, 3–4
|
3, 2–4
|
0.03
|
3, 2–4
|
0.15
|
|
Duration of surgery
(min), median, IQR
|
240, 166–305
|
194, 113–276
|
0.004
|
202, 116–285
|
0.01
|
|
Wound class,
median, IQR
|
1, 1–1
|
1, 1–1
|
0.82
|
1, 1–1
|
0.36
|
|
NNIS Risk Index,
median, IQR
|
1, 1–2
|
1, 1–1
|
0.002
|
1, 1–2
|
0.06
|
|
| aLOS, length of
stay; IQR, interquartile range; ASA, American Society of Anesthesiologists-Physical
Status score; NNIS, National Nosocomial Infections Surveillance System. |
| Appendix
Table 2. Study 1: Adjusted outcomes models for methicillin-resistant
Staphylococcus aureus (MRSA) surgical site infection (SSI)
compared to uninfected control patientsa |
|
|
Variable
|
Deaths
|
Length of stayb
|
Costc
|
|
OR (95% CI)
|
ORd (95% CI)
|
OR (95% CI)
|
|
|
MRSA
|
11.4 (2.8 to 34.9)
|
3.2 (2.7 to 3.7)
|
2.2 (2.0 to 2.6)
|
|
ASA scoree,f
|
|
1.3 (1.2 to 1.5)
|
ASA score = 4
3.7 (1.5 to 8.9)
|
|
ASA score = 2
2.0 (1.4 to 2.9)
|
|
ASA score = 3
3.0 (2.1 to 4.3)
|
|
ASA Score = 4
4.1 (2.8 to 6.0)
|
|
>73 y of age
|
4.8 (2.0 to 11.6)
|
|
|
|
Operative duration (min)g
|
|
211–400
|
|
(0.9 to 1.3)
|
1.4 (1.2 to 1.7)
|
|
401–590
|
1.7 (1.2 to 2.4)
|
2.2 (1.6 to 3.1)
|
|
>590
|
1.8 (1.1 to 2.9)
|
2.6 (1.6 to 4.0)
|
|
Length of stay before surgeryh
|
|
7–13 d
|
|
1.6 (1.1 to 2.1)
|
1.7 (1.3 to 2.3)
|
|
14–20 d
|
3.6 (1.4 to 9.6)
|
5.6 (2.3 to 13.4)
|
|
>20 d
|
0.7 (0.2 to 2.6)
|
1.2 (0.3 to 4.3)
|
|
Intensive care unit stay before surgery
|
|
1.5 (1.2 to 2.0)
|
|
Tertiary care hospital
|
|
1.5 (1.2 to 1.7)
|
|
| aOR, odds ratio;
CI, confidence interval; ASA, American Society of Anesthesiologists
-Physical Status. |
| bModel includes
the following confounding variables: admission to the tertiary care
hospital, diabetes, and renal disease. |
| cModel includes
the following confounding variable: renal disease. |
| dFor length of
hospital stay and cost, OR represents multiplicative effect |
| eLength of stay
increases by 1.3-fold for each point increase in ASA score. |
| fFor cost, reference
category is ASA score = 1. |
| gReference category
is operative duration < 211 min. |
| hReference category
is length of stay before surgery < 7 d. |
| Appendix Table
3. Study 1, adjusted outcomes models for methicillin-resistant
Staphylococcus. aureus (MRSA) surgical site infections (SSI)
compared to patients with methicillin-resistant S. aureus (MSSA)
SSIa |
|
| |
Deathsb
|
Length of Stayc
|
Costd
|
|
|
Variable
|
OR (95% CI)
|
OR (95% CI)e
|
ORe (95% CI)
|
|
MRSA
|
3.4 (1.5 to 7.7)
|
1.2 (1.0 to 1.5)
|
1.2 (1.0 to 1.4)
|
|
ASA scoref
|
ASA score = 4
5.1 (2.1 to12.5)
|
ASA score = 2
0.9 (0.5 to 1.7)
|
ASA score = 2
1.0 (0.7 to 1.5)
|
|
ASA score = 3
1.6 (0.9 to 2.9)
|
ASA score = 3
1.4 (1.0 to 2.1)
|
|
Asa score = 4
1.8 (1.0 to 3.5)
|
ASA score = 4
2.1 (1.4 to 3.2)
|
|
Age > 61 years
|
3.0 (1.2 to 7.3)
|
|
|
|
Operative duration, ming
|
|
|
|
|
206–381
|
1.3 (1.0 to 1.6)
|
1.4 (1.1 to 1.6)
|
|
382–557
|
1.3 (0.8 to 2.1)
|
1.8 (1.3 to 2.5)
|
|
>557
|
1.1 (0.5 to 2.6)
|
1.6 (0.9 to 2.8)
|
|
Length (d) of stay before infectionh
|
|
|
|
|
11–20
|
1.4 (1.0 to 1.8)
|
1.6 (1.3 to 2.0)
|
|
21–30
|
1.6 (1.0 to 2.7)
|
1.7 (1.2 to 2.5)
|
|
>30
|
1.3 (0.5 to 3.1)
|
1.8 (0.9 to 3.8)
|
|
Renal disease
|
|
1.5 (1.0 to 2.2)
|
|
|
Length (d) of intensive care unit stay before infectioni
|
|
|
|
|
8–14
|
1.8 (1.1, 2.8)
|
|
15–21
|
2.1 (1.1, 8.8)
|
|
>21
|
1.9 (0.4, 8.0)
|
|
Tertiary care hospital
|
|
|
1.3 (1.1, 1.6)
|
|
| aOR, odds ratio;
CI, confidence interval; ASA, American Society of Anesthesiologists
-Physical Status. |
| bModel includes
the following confounding variable: operative duration >222 min. |
| cModel includes
the following confounding variables: admission to tertiary care hospital
and diabetes. |
| dModel includes
the following confounding variables: diabetes and renal disease. |
| eFor length of
hospital stay and cost, OR represents multiplicative effect. |
| fFor deaths, reference
category is ASA score < 1; for length of stay and cost, reference
category is ASA score = 1. |
| gReference category
is operative duration < 206 min. |
| hReference category
is length of stay prior to infection < 11 d. |
| iReference category
is intensive care unit length of stay prior to infection < 8 d. |
| Appendix
Table 4. Study 2, patient characteristics, vancomycin-resistant
enterococci (VRE) wound infections, controls not infected with enterococci,
and controls with vancomycin-susceptible enterococci (VSE) wound infections,
bivariate analyses |
|
|
Variable
|
Cases, VRE wound (%)
(n = 99)
|
Controls, not infected (%)
(n = 280)
|
P Value
(VRE vs. controls not infected)
|
Controls, VSE (%) (n = 213)
|
p value
(VRE vs. VSE)
|
|
|
Age, mean (y)
|
60.3
|
63.6
|
0.09
|
59.1
|
0.51
|
|
Sex (female)
|
46 (46)
|
124 (44.3)
|
0.7
|
127 (59.6)
|
0.03
|
|
Main diagnosis
|
|
|
|
|
|
|
Orthopedic condition
|
11 (11)
|
30 (10.7)
|
|
18 (8.4)
|
|
|
Cardiovascular condition
|
25 (25)
|
117 (41)
|
|
61 (28.6)
|
|
|
Endocrine disorder
|
3 (3)
|
6 (2.1)
|
|
4 (1.9)
|
|
|
Gastrointestinal disorder
|
25 (25)
|
60 (21.4)
|
|
62 (29.1)
|
|
|
Genitourinary disorder
|
6 (6)
|
12 (4.2)
|
|
9 (4.3)
|
|
|
Infectious disease
|
16 (16)
|
6 (2.1)
|
|
20 (9.4)
|
|
|
Hematologic disease
|
0 (0)
|
2 (.7)
|
|
0
|
|
|
Neurologic disease
|
11 (11)
|
32 (11.4)
|
|
34 (16)
|
|
|
Pulmonary disease
|
2 (2)
|
14 (5)
|
|
5 (2.4)
|
|
|
Coexisting conditions
|
|
|
|
|
|
|
Cardiovascular disease
|
73 (74)
|
204 (72.9)
|
0.86
|
150 (70.4)
|
0.55
|
|
Lung disease
|
11 (11)
|
33 (11.7)
|
0.9
|
26 (12.2)
|
0.78
|
|
Diabetes mellitus
|
67 (67.7)
|
139 (49.6)
|
0.002
|
127 (59.6)
|
0.17
|
|
Organ transplant recipient
|
14 (14)
|
21 (7.5)
|
0.08
|
18 (8.4)
|
0.12
|
|
Renal disease
|
18 (18.2)
|
39 (14)
|
0.7
|
28 (13.2)
|
0.24
|
|
Malignancy
|
7 (7.1)
|
27 (9.6)
|
0.5
|
32 (15)
|
0.05
|
|
AIDS
|
2 (2)
|
2 (0.7)
|
0.27
|
0
|
0.1
|
|
Hepatobiliary disease
|
16 (16.6)
|
40 (14.3)
|
0.8
|
31 (14.5)
|
0.71
|
|
Charlson comorbidity score, mean
|
3.17
|
2.66
|
0.07
|
|
|
|
Hospital-related risk factors
|
|
|
|
|
|
|
Transfer from another
institution
|
34 (34.3)
|
102 (36.4)
|
0.5
|
34 (16)
|
<0.001
|
|
Surgery
|
29 (29.3)
|
94 (33.6)
|
0.08
|
90 (42.3)
|
0.03
|
|
Admission to ICU
|
26 (26.2)
|
58 (20.7)
|
0.9
|
53 (33.3)
|
0.8
|
|
| Appendix
Table 5. Study 2, adjusted outcomes models for vancomycin-resistant
enterococcus (VRE) wound infection compared to uninfected control
patientsa |
|
|
Variable
|
Deathsb
|
Variable
|
Length of Stayc
|
Variable
|
Costd
|
|
|
|
|
OR (95% CI)
|
ORe (95% CI)
|
ORe (95% CI)
|
|
|
VRE infection
|
2.0 (0.8 to 5.2)
|
VRE infection
|
1.8 (1.3 to 2.4)
|
VRE infection
|
1.5 (1.3, 1.8)
|
| |
|
Transfer from another hospital
|
1.5 (1.2 to 1.9)
|
Surgerye
|
1.4 (1.1, 1.8)
|
| |
|
Renal disease
|
2.0 (1.5 to 2.7)
|
|
|
| |
|
Malignancy
|
0.7 (0.5 to 0.9)
|
|
|
| |
|
Intensive care unit stayf
|
2.3 (1.6 to 3.3)
|
|
|
|
| aOR, odds ratio; CI, confidence
interval. |
| bModel includes the following
confounding variables: intensive care unit (ICU) stay and number of
coexisting conditions. |
| cModel includes the following
confounding variable: propensity score (i.e., likelihood of being
a VRE case). |
| dModel includes the following
confounding variables: propensity score [i.e., likelihood of being
a VRE case (Appendix)] and length of stay before infection (index
date for controls). |
| eFor length of hospital stay
and cost, OR represents multiplicative effect. |
| fBefore infection for cases
and before index date for controls. |
| Appendix Table
6. Study 2, adjusted outcomes models for vancomycin-resistant
enterococcus (VRE) wound infection compared to control patients with
wound infection due to vancomycin-susceptible enterococcus (VSE)a |
|
|
Variable
|
Deathsb
|
Variable
|
Length of Stayc
|
Variable
|
Costd
|
|
|
|
|
Odds Ratio (OR)
(95% Confidence Interval [CI])
|
ORe (95% CI)
|
ORe (95% CI)
|
|
|
VRE
|
2.5 (1.1, 6.1)
|
VRE
|
1.1 (0.9, 1.4)
|
VRE
|
1.4 (1.2, 1.6)
|
|
Intensive care unit stay (ICU)f
|
9.0 (3.0, 27.4)
|
ICU stayf
|
1.8 (1.3, 2.5)
|
Surgeryf
|
1.2 (1.1, 1.3)
|
|
| aOR, odds ratio;
CI, confidence interval; ICU, intensive care unit. |
| bModel includes
the following confounding variables: gender and surgery before infection. |
| cModel includes
the following confounding variable: malignancy and length of stay
before infection. |
| dModel includes
the following confounding variables: length of stay before cohort
inclusion. |
| eFor length of
hospital stay and cost, OR represents multiplicative effect. |
| fBefore infection
for cases and before index date for controls. |
|
