What is the magnitude of inappropriate antibiotic use?

Antimicrobial Stewardship:
A-team or computer-associated decision support system
CDSS:
What is the magnitude of inappropriate antibiotic use?
 Erasmus MC
 Cross-sectional point prevalence survey on May 4 and 16, 2013
 E-surveillance:
What is the magnitude of inappropriate antibiotic use?
 Erasmus MC
 Cross-sectional point prevalence survey on May 4 and 16 2013
 Inclusion: patients using at least 1 therapeutic antibiotic drug
 Exclusion antiviral, antiparasitic, or antifungal medication or admission to ICU,
Sophia children’s hospital or psychiatric ward.
 Evaluation by 2 ID specialists
 standardized method developed by Gyssens et al.
JAC 1992. 30:724–727
Results
996 patients admitted
337 patienten (33,8%) used ≥1 antibiotic drugs
221 patients (22,2%) used 307 therapeutic antibiotic drugs
Method: JAC 1992. 30:724–727
Conclusie
 29,3 % of antibiotics is prescribed inappropriately
 Literature (32%-47%)
Prezies data
Intern Med J. 2012 June; 42(6): 719–721].
Percentage effective empirical antibiotic therapy
Kerremans et al., 2009
To be improved:
• Empirical therapy
• IV-oral switch
• Narrowing down
• Correct dose
• Correct dosing interval
• Correct duration
• Correct choice
• Antimicrobial resistance
The Ultimate A-team?
AST/CDSS and antibiotic use
 Decreased
 Remained stable
JAC 2008;62:608
Int J Med Inform 2007;76:760
Inf.Control Hosp Epidemiol 2012;33:434
JAC 2009;63:400
 No reduction in cost
Cephalosporins
quinolones
Glycopeptides
aminoglyc.
Carbapenems
Beta-lactams
Number of computerized approvals for restricted antimicrobial drugs per month: total
number of approvals (standard + non-standard indications) and non-standard approvals
(approved individually by the ID clinicians).
Buising K L et al. J. Antimicrob. Chemother. 2008;62:608616
© The Author 2008. Published by Oxford University Press on behalf of the British Society for
Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail:
[email protected]
AST/CDSS and antimicrobial resistance
 Improved or remained stable
 At least 6 months intervention necessary
JAC 2010;65:1062
JAC 2008;62:608
Inf.Control Hosp Epidemiol 2012;33:434
CDSS and medication errors
 rates of renal function deterioration were lowered from 12.4% to 9.5%
 dosing conformity improved
Am J kidney Dis 2010;56:809
Int J Clin Pharmacol Ther. 2012;50:375
J Am Med Inform Assoc 2010;17:308
AST/CDSS and empirical therapy
Significant improvement matching empiric antibiotic therapy
Approptiate antibiotic therapy
Control
ITT
per protocol
65%
73%
85%
Initial susceptibility mismatch
Pre-intervention
intervention
48/197 (24%)
23/151 (15%)
JAC 2006;58:1238
Int Qual Health Care 2006;18:224-31.
Thus
 CDSS seems a useful tool in CDSS
 But implementation not worldwide……….
 94% improved practice
 When
 (1) integrating CDSSs into clinicians’ workflow;
 (2) CDSSs that offered recommendations rather than mere assessments;
 (3) decision support at the time and place of decision-making;
 (4) computer assessment of eligibility for services.
BMJ. 2005;330:765.
Development of an AST CDSS
Advice empirical therapy
 Not mentioned in chart (1/3)
 More than one possible diagnosis
 Prevous cultures into account
Questions
1. presumed diagnosis
2 empirical or targeted therapy
Outcome
 Advice according to SWAB
 Consult infectious diseases/medical microbiology
Vd Bosch et al., CID in press
Development of an AST CDSS
IV-oral switch
 Target culture (among cultures taken)
 Alternative antibiotic drug
Rules:
 Clinical improvement
 Fever, WBC, CRP ?
 Hemodynamically stable: pulse, RR?
 Development (inter)national consensus for switch rule in CDSS
Conclusion
AST:
Decrease antibiotic use
Increase correct antibiotic use
Decrease susceptibility mismatch
CDSS:
High potential as AST tool
More than generating lists for A-team
Interactive use of SWAB guidelines
Development
Testing
Implementation
Acknowledgements
 Roel Verkooijen
 Jan Prins
 Hassana Akhloufi
 Michiel van Agtmael
 Heleen van der Sijs
 Monique Jaspers
 Damian Melles
 IT- team
 Alieke Vonk
 Staff MMIZ
 Johan Mouton
 SWAB

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