efficacy and efficiency of a restrictive antibiotic …antibiotic usage - interventions antibiotic...
TRANSCRIPT
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Modelling the impact of antibiotic use on antibiotic-resistant Escherichia coli using
population-based data from a large hospital and its surrounding community
N. Vernaz*, B. Huttner,
D. Muscionico, J. Salomon, P. Bonnabry,
J.M López-Lozano, J. Schrenzel, S. Harbarth
(Geneva, CH; Alicante, ES)
Efficacy and efficiency of a restrictive antibiotic policyon MRSA in the intensive care unit
N. Vernaz*, R. Aschbacher, B. Moser, S. Harbarth, P. Mian, P. Bonnabry, L. Pagani (Geneva, CH; Bolzano, IT)
Challenge of time series models
• Reinforce the evidence
• Real-world questions
• Real-world data
• Methodological
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Transfer function modelOutcomes of interest:
Incidence of non-duplicate clinical isolates
MRSA E. coli resistant to- ciprofloxacin- cefepime (ESBL)
Explanatory variables: antibiotic usage - interventions
antibiotic usage in ICU antibiotic usage- surrounding community
Restriction antibiotic policy (0,1) - HUG (2’000 beds)
ARIMA
Efficacy and efficiency of a restrictive antibiotic policy
1) November 2003: withdrawing ampicillin/sulbactam prophylaxis2) Mai 2004: targeting vancomycin therapy whenever indicated
on MRSA in the intensive care unit
Intervention model Tranfer function model
Study period: January 2002 to December 2007
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MRSA model identification
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2002 2003 2004 2005 2006 2007
MRSA
MRSA = 2.55 + 0.32 MRSA(t-2) + εt
Restrictive antibiotic policyefficacy
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DD
D
vancomycineintervention
ampi-sulbactam, R2 = 66%
Vancomycin, R2 = 39%vancomycin = 34.9 - 23.5 y + 0.255 AR(3) + εt
0 < Mai 2004
1 ≥ Mai 2004
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DDD
ampi-sulfaintervention
ampi-sulbactam = 301 – 167 y
- 0.267 AR(1) – 0.379 MA(7) + εt
0 < November 2003
1 ≥ November 2003y
y
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Transfer function on MRSA incidence: efficiency
Variable lag Coefficient Std. Error t-Statistic Prob. month
intercept 1.628 0.673 2.420 0.019ceftriaxone 2 0.021 0.008 2.531 0.014ceftriaxone 3 0.023 0.008 2.990 0.004levofloxacine 0 0.012 0.004 2.785 0.007avr.03 -1.613 0.688 -2.346 0.022AR 2 0.316 0.125 2.523 0.014MA 3 0.291 0.137 2.133 0.037
Useful tool for policy maker• Modelling antibiotic usage and resistance is a useful tool
for antimicrobial stewardship
– to drive a more appropriate empirical and targeted antimicrobialtherapy
– to control and prevent the misuse of antimicrobials
– to increase the level of evidence
• Time series analysis is a useful tool even at a ICU level
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Questions?
Modeling the impact of antibiotic use on antibiotic-resistant Escherichia coli using
population-based data from a large hospital and its surrounding community
outside de Box plot: 18% at Geneva surrounding community
excess usage of fluoroquinolones can be associated with the development of resistance
ESF EMRC EXPLORATORY WORKSHOP Antwerp, Belgium, 7-9 September 2005. Antibiotic Prescribing Quality Indicators.
DDD (J01MA) x 100DDD (J01)
Antibiotic prescribing quality indicator: outpatient fluoroquinolone use in 2003
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Settings & Methods
• Incidence of non-duplicate clinical isolates of E. coli resistant to• ciprofloxacin; of community origin (= CA -Cipro-R)• ciprofloxacin; of hospital origin (= HA -Cipro-R)• cefepime (= surrogate of ESBL)
• Antibiotic usage:• Geneva surrounding community: 450’000 inhabitants• HUG: 2’000 beds
• Defined Daily Dose (DDD) normalised • per 100 patients-days
• per 1’000 inhabitants
• Exclude Paediatric, Psychiatry, Rehabilitation wards• Study period: Jan. 2000 to Dec. 2007
Non-duplicate, resistant E. coli isolates
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
01 20
00
6 20
00
11 20
00
4 20
01
9 20
01
2 20
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7 20
02
12 20
02
5 20
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10 20
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3 20
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8 20
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01 20
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6 20
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11 20
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4 20
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9 20
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2 20
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7 20
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12 20
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Inci
denc
e pe
r 100
pat
ient
day
s
R_E.Coli_ciprofloxacineR_E.Coli_cotrimoxazoleESBL
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Total antibiotic usage surrounding community
0
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14
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18
janv
.00
juin.
00
nov.0
0
avr.0
1
sept.0
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févr.
02
juil.0
2
déc.0
2
mai.0
3
oct.0
3
mars.
04
août.
04
janv
.05
juin.
05
nov.0
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avr.0
6
sept.0
6
févr.
07
juil.0
7
déc.0
7
DD
D p
er 1
'000
inha
bita
nts
trimethoprim-sulfamethoxazoleIII gen. cephalosporinsII gen cephalosporinsmacrolidesfluoroquinolonesamoxicillinamoxicillin/clavunate
Average antimicrobial use: 14.22 (8.6-19.77) DDD/1’000 inhabitants
Outpatient FQ usage Fluroquinolones consumption
0.000.200.400.600.801.001.201.401.601.802.00
janv
.00
juil.
00
janv
.01
juil.
01
janv
.02
juil.
02
janv
.03
juil.
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janv
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juil.
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janv
.05
juil.
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janv
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jui
l.06
janv
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juil.
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DI D
OfloxacineCiprofloxacineNorfloxacineLoméfloxacineFléroxacineLévofloxacineMoxifloxacine
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Antibiotic usage Geneva Univ. Hospitals
0
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2000
6 2
000
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4 2
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9 2
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2 2
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7 2
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12
2002
5 2
003
10
2003
3 2
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8 2
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6 2
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4 2
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9 2
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2 2
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7 2
007
12
2007
DDD
per 1
00 p
atie
nt d
ays
glycopeptidestrimethoprim-sulfamethoxazolemacrolidescarbapenemsfluoroquinolonescefepimeIII gen cephalosporinsII gen cephalosporinscefazolineamoxicillinamoxicillin/clavulanate
Average antimicrobial use: 54.99 (45.63-62.17) DDD/100 patients-days
ESBL transformation
.00
.01
.02
.03
.04
.05
I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV
2002 2003 2004 2005 2006 2007
-.03
-.02
-.01
.00
.01
.02
.03
I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV
2002 2003 2004 2005 2006 2007
Null Hypothesis: R_ESBL has a unit root Exogenous: None Lag Length: 4 (Automatic - based on SIC, maxlag=11)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic 0.373541 0.7894 Test critical values: 1% level -2.599934
5% level -1.945745 10% level -1.613633
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(R_ESBL) has a unit root Exogenous: None Lag Length: 3 (Automatic - based on SIC, maxlag=11)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -7.865141 0.0000 Test critical values: 1% level -2.599934
5% level -1.945745 10% level -1.613633
*MacKinnon (1996) one-sided p-values.
t*: 0.37 > -1.94 non stationary t* -7.89 < -1.94 stationary
ESBL Yt- Yt-1: D_ESBL
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Transfer function model for E. coli resistant to cefepime (ESBL)
Variable Lag
(months)
Parameter (SE) t-Statistic P-value
d - ceftriaxone HUG 0 0.0041 (0.0017) 2.4047 0.0195
d - ciprofloxacin HUG 1 0.0043 (0.0013) 3.2126 0.0022
5 0.0045 (0.0014) 3.2045 0.0022
d - cefepime HUG 3 0.0034 (0.0016) 2.1502 0.0358
d - piperacilline/tazobactam HUG 3 0.0099 (0.0042) 2.3073 0.0247
d - ciprofloxacin GE 4 0.0247 (0.0080) 3.0809 0.0032
Autoregressive term 1 -0.5877 (0.01084) -5.4236 0.0000
Transfer function model on ESBL incidence explains 51% of ESBL variation over time
Cipro-R- CA, R2 = 0.52 Cipro-R-HA, R2=0.18
Variable Lag
(months)
Parameter
(SE)
t-
Statistic
P-value Lag
(months)
Parameter
(SE)
t-
Statistic
P-value
Constant -3.54(0.24) -14.76 0.0000 -3.34 (0.01) -42.86 0.0000
log ciprofloxacin GE 0 1.31(0.49) 2.68 0.0089
1 1.01(0.49) 2.08 0.0406 1 0.82 (0.30) 2.76 0.0069
log moxifloxacin GE 4 0.44(0.16) 2.72 0.0081
Autoregressive term 1 0.31(0.11) 2.89 0.0050 1 0.24 (0.10) 2.43 0.0170
Moving average term 8 0.36(0.11) 3.24 0.0017
Transfer function model on E. coliresistant to
ciprofloxacin, community origin ciprofloxacin, hospital origin
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Conclusion• Added value of time series analysis:
to better understand the interaction between community and hospital antibiotic prescribing
• Temporal relationship between:outpatient fluoroquinolone use and incidence of– CA- Cipro-R- E Coli– HA- Cipro-R- E Coli– ESBL
• Support efforts to reduce prescriptions of fluoroquinolones
Useful tool for policy maker:hierarchy of evidence
Colleagues or members of the multidisciplinary team
Views of colleagues / peersVI
Evidence based local procedures and care pathways
Opinions from respected authorities, based on clinical evidence, descriptive studies or reports from committees
V
Articles published in peer-reviewed journals
Evidence from well designed non-experimentalstudies from more than one centre or research group
IV
Articles published in peer-reviewed journals
Evidence from well designed trials without randomization: cohort study, time-series or matched case control studies
III
Articles published in peer-reviewed journals
Evidence from at least one properly designed RCT of appropriate size
II
Meta-AnalysisThe Cochrane Collaboration
Strong evidence from at least ONE systematic review of well designed RCT
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ExampleDescriptionLevel
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“Remember that all models are wrong;
the practicalquestion is
how wrong do they have to be to not be useful”
G. Box, N. Draper