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A Review of Quality Measures for Assessing the Impact of Antimicrobial Stewardship Programs in Hospitals

Department of Pharmacy, Pharmacology and Postgraduate Medicine, University of Hertfordshire, Hatfield, AL10 9AB, UK
NIHR Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
Antimicrobial Resistance Program, Public Health England, London, NW9 5EQ, UK
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Christopher C. Butler and Jerod Nagel
Received: 28 September 2015 / Revised: 4 December 2015 / Accepted: 4 January 2016 / Published: 13 January 2016
(This article belongs to the Special Issue Antimicrobial Stewardship)


The growing problem of antimicrobial resistance (AMR) has led to calls for antimicrobial stewardship programs (ASP) to control antibiotic use in healthcare settings. Key strategies include prospective audit with feedback and intervention, and formulary restriction and preauthorization. Education, guidelines, clinical pathways, de-escalation, and intravenous to oral conversion are also part of some programs. Impact and quality of ASP can be assessed using process or outcome measures. Outcome measures are categorized as microbiological, patient or financial outcomes. The objective of this review was to provide an overview of quality measures for assessing ASP and the reported impact of ASP in peer-reviewed studies, focusing particularly on patient outcomes. A literature search of papers published in English between 1990 and June 2015 was conducted in five databases using a combination of search terms. Primary studies of any design were included. A total of 63 studies were included in this review. Four studies defined quality metrics for evaluating ASP. Twenty-one studies assessed the impact of ASP on antimicrobial utilization and cost, 25 studies evaluated impact on resistance patterns and/or rate of Clostridium difficile infection (CDI). Thirteen studies assessed impact on patient outcomes including mortality, length of stay (LOS) and readmission rates. Six of these 13 studies reported non-significant difference in mortality between pre- and post-ASP intervention, and five reported reductions in mortality rate. On LOS, six studies reported shorter LOS post intervention; a significant reduction was reported in one of these studies. Of note, this latter study reported significantly (p < 0.001) higher unplanned readmissions related to infections post-ASP. Patient outcomes need to be a key component of ASP evaluation. The choice of metrics is influenced by data and resource availability. Controlling for confounders must be considered in the design of evaluation studies to adequately capture the impact of ASP and it is important for unintended consequences to be considered. This review provides a starting point toward compiling standard outcome metrics for assessing ASP.
Keywords: antimicrobial stewardship; antimicrobial resistance; quality indicators; outcome; patient; infectious diseases antimicrobial stewardship; antimicrobial resistance; quality indicators; outcome; patient; infectious diseases

1. Introduction

Antimicrobial resistance (AMR) is a growing public health threat which has attracted the attention of national and international bodies. A recent World Health Organization (WHO) surveillance of resistance to antibacterial drugs in bacteria commonly associated with hospital and community infections revealed increasing resistance and/or decreased susceptibilities in the studied bacteria [1]. Resistance of Escherichia coli to third-generation cephalosporins and fluoroquinolones and Staphylococcocus aureus to methicillin (Methicillin Resistant Staphylococcous aureus, MRSA) are reported to be 50% or more in five out of the six WHO regions [1]. Klebsiella pneumoniae resistance to third-generation cephalosporins is reported to be greater than 50% in all six WHO regions. Carbapenem-resistant K. pneumoniae is reportedin all WHO regions, with reports in two regions exceeding 50%. Also, non-susceptibility of Streptococcus pneumoniae to penicillin is reported to be more than 50% in all six WHO regions. A related English AMR surveillance report revealed increased resistance of E. coli and K. pneumoniae to ciprofloxacin, third-generation cephalosporins, gentamicin, and imipenem/meropenem [2]. The report however indicated decreased resistance of Pseudomonas aeruginosa to ceftazidime, gentamicin, and imipenem/meropenem [2]. Recent reports highlightthat patients with infection caused by drug resistant bacteria have a two-fold increase in mortality compared to those with infection with sensitive bacteria [1,3].
Available estimates indicate that between 25%–50% of hospitalized patients receive antibiotics, with between 30% and 50% of antibiotic use being inappropriate [4,5]. Published literature demonstrates a strong link between antibiotic use and the development of resistance [3,6,7,8]. Antimicrobial stewardship programs are therefore quality motivated interventions aimed at improving the use of antibiotics in healthcare facilities. The primary goal is to optimize clinical outcomes and minimize unintended consequences such as Clostridium difficile infection (CDI) and resistance [4,9]. Strategies to achieve these goals have included prospective audit with intervention and feedback, and formulary restriction and preauthorization. Supplemental strategies include education, guidelines and clinical pathways, antimicrobial cycling and scheduled antimicrobial switch, antimicrobial order forms, automatic stop orders, combination therapy, streamlining or de-escalation of therapy, dose optimization, conversion from parenteral to oral therapy, and computer surveillance and decision support [4,10].
A Cochrane review of interventions to improve antibiotic prescribing for hospital inpatients classified these strategies into three main groups namely:
  • persuasive interventions: these include education, audit and feedback, guidelines and clinical pathways.
  • restrictive interventions: formulary restriction, prior approval or preauthorization from infectious diseases (ID) physician, microbiologist or pharmacists, automatic stop orders, antimicrobial cycling or scheduled switch, antibiotic order forms.
  • structural interventions: computerized records, computerized decision support, example computer physician order entry (CPOE) [11].
Effective antimicrobial stewardship programs require a multidisciplinary team with responsibility for promoting prudent antimicrobial use. The Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America (IDSA/SHEA) ASP guidelines [4] recommend a multidisciplinary team which includes an ID physician and a clinical pharmacist with infectious diseases training as core members. Inclusion of a clinical microbiologist, information system specialist, infection control specialist, hospital epidemiologist, and hospital administrator is considered optimal. The English antimicrobial stewardship (AMS) toolkit: “Start Smart, then Focus” recommends other core members should be present, an acute care physician, a surgeon, a senior member of the pharmacy management team, an anesthetist, a pediatrician, and a senior nurse [12].
There is an increased call on healthcare organizations to develop quality measures or indicators to monitor and evaluate the impact of ASP [4,12,13,14]. Previous reviews have reported on the impact of ASP in reducing antimicrobial cost, AMR, superinfection, and patient outcomes (such as length of stay (LOS), readmission rate, and mortality) [11,15]. The objective of this review was to provide an overview of reported quality measures for assessing ASP and report on the impact of published antimicrobial stewardship studies on these measures, with particular focus on patient outcome measures.

2. Methods

A literature search of papers published in English between the 1990 and June 2015 was identified through a search of five databases: Scopus, Medline, CINAHL, Pubmed, and Embase using the following search terms: (“antimicrobial stewardship” OR “antimicrobial stewardship program” OR “antibiotic control program” OR “antibiotic policy” OR “antibiotic management program”); (outcomes OR impact OR “quality measure” OR “performance measures” OR “length of stay” OR “clinical improvement” OR “C. difficile infection” OR mortality OR resistance OR readmission OR MRSA).

Study Inclusion and Exclusion Criteria

Only primary research studies, published in English and which met the following criteria were included:
  • Defined and/or developed quality measures for assessing ASP in hospital settings.
  • Used quality performance measures (such as change in antimicrobial use) and outcome measures (including resistance patterns, rates of CDI, LOS, readmission, mortality, and cost savings) in evaluating impact of ASP.
  • Involved adult inpatients in acute and community hospital settings.
Studies excluded were:
  • Those that reported prevalence of ASP without evaluation of impact.
  • ASP studies in pediatrics and long term care facilities.

3. Results

The initial search returned 4319 articles. Of these, 152 met the inclusion criteria, and a full-text evaluation was carried out on 63 studies. In summary, four studies defined quality metrics for evaluating ASP. Twenty-one studies assessed impact on antimicrobial utilization and cost, 25 studies evaluated impact on resistance patterns and/or rate of CDI. Thirteen studies assessed impact on patient outcomes including mortality, LOS, and readmission rates.

3.1. Studies that Defined Quality Measures for Evaluating ASP

Table 1 includes studies [16,17,18,19] that developed or defined quality measures for evaluating ASP. Methods used were modified Delphi technique, survey, and interviews. Bumpass et al. [19] surveyed ID physicians’ and pharmacists’ opinions of AMS metrics considered important in evaluating ASP. The authors reported that although appropriateness of antimicrobial use, infection-related mortality, and antibiotic associated length of stay were considered more important outcomes by those surveyed, antimicrobial use and cost were the most commonly collected metrics.
Table 1. Studies that defined quality measures for evaluating antimicrobial stewardship programs antimicrobial stewardship programs (ASP).
Table 1. Studies that defined quality measures for evaluating antimicrobial stewardship programs antimicrobial stewardship programs (ASP).
StudyMethod UsedCategory of MeasureQuality Measures Identified
Nathwani et al., 2002 [16]Expert panelProcess measures for glycopeptideprescribing
  • total number of glycopeptide in defined daily dose (DDD)/1000-patient days
  • number of alert antibiotic forms completed for glycopeptide
  • number of patients prescribed glycopeptide appropriately according to policy
  • number of patients prescribed glycopeptide inappropriately
Chen et al., 2011 [17]Survey by questionnaireand interviewsProcess and outcome
  • DDD/1000 patient-days against state or national data
  • quantity of antimicrobial use within hospital
  • number of prescriptions of restricted antibiotic complaints with approved guideline.
  • cost savings
Morris et al., 2012 [18]Modified DelphiProcess and outcome
  • days of therapy/1000 patient-days
  • number of patients with specific organisms that are drug resistant
  • mortality related to antimicrobial-resistant organisms
  • conservable days of therapy among patients with community-acquired pneumonia (CAP), skin and soft-tissue infections (SSTI), or sepsis and bloodstream infections (BSI)
  • unplanned hospital readmission within 30 days after discharge from the hospital in which the most responsible diagnosis was one of CAP, SSTI, sepsis or BSI
Bumpass et al., 2014 [19]SurveyProcess and outcome
  • appropriateness of antimicrobial use
  • infection-related mortality rate
  • antibiotic-associated length of stay
  • antimicrobial use
  • antimicrobial cost
DDD—Defined daily dose.

3.2. Impact of ASP on Quality Measures

Impact of ASP on different quality measures is summarized in Table 2, Table 3 and Table 4. This is grouped into:
  • impact on antimicrobial use and cost savings
  • impact on C. difficile infection and resistance patterns
  • impact on patient outcomes (LOS, readmission rate, mortality)
Some of the studies used more than one measure in assessing impact and majority (29) employed before-after or pre-post-intervention (quasi-experimental) design without control. The pre-phase consisted of retrospective collection of baseline data before ASP implementation.

3.2.1. Impact of ASP on Antimicrobial Use and Cost of Antimicrobials

Change in the use of specific antibiotic or antibiotic class is considered a process measure [4,20]. The majority of the programs that assessed impact of ASP on antibiotic use also assessed cost savings. Twenty-one studies assessed impact on antimicrobial use and/or cost. The majority of the studies that reported significant cost savings did not provide the cost of implementing the program. Table 2 summarizes studies [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41] that assessed the impact of ASP on antimicrobial use and cost of antimicrobials.
Table 2. Impact of ASP on antimicrobial use and cost of antimicrobials.
Table 2. Impact of ASP on antimicrobial use and cost of antimicrobials.
StudySettingAMS StrategyDesignResults
Mercer et al., 1999, USA [21]450-bed community hospitalRestriction, pre-authorization, clinical pathwayBefore-afterCost of IV and oral antibiotics reduced by 26% and 10% respectively. Use of high cost IV antibiotics reduced by 22%.
Bassetti et al., 2000, Italy [22]2500-bed teaching hospitalFormulary restriction, sequential therapy.Before-afterCost of antibiotics decreased by 10.5% following formulary introduction with cost savings of €345,000. Ceftazidime cost reduced by 52%, and antibiotic cost per day of hospital stay decreased from €4.53 to €4.18.
Berlid et al. 2001, Norway [23]600-bed acute hospitalGuidelines, educationProspective before-after23% reduction in use of broad-spectrum antibiotics. Cost of antibiotic reduced by 27% and 32% in the first and second year of the program respectively.
Ansari et al. 2003, Scotland, UK [24]900-bed University-affiliated hospitalGuideline, review and feedbackBefore-after with interrupted time series (ITS)Cost savings from targeted antibiotics was (£133,269) (p < 0.0001). Cost of program was £20,133. Use of targeted antibiotic reduced by 0.27 DDD/100 bed-days/month.
Cook et al., 2004, USA [25]730-bed university teaching hospitalRestriction, pre-authorization, review and feedbackBefore-afterBroad spectrum antibiotic use decreased by 28% with no change in susceptibilities of common nosocomial gram-negative organisms.
Mcgregor et al., 2006, USA [26]648-bed, tertiary-care referral centerComputerized decision support system, review and feedbackRandomized controlled trialCost savings of $84,194 (23%) in the intervention group.
Siddiqui et al., 2007, Pakistan [27]12-bed adult ICU at a teaching hospitalRestriction policy, stamp on chart, feedbackBefore- after34% reduction in use of broad spectrum antibiotics and 40% cost reduction.
Cheng et al. 2009, Hong Kong [28]1500-bed university- affiliated hospitalGuidelines, education, feedbackBefore-afterAntimicrobial use reduced from 73.06 (baseline) to 64.01 DDD/1000 patient-days.Reduction in broad-spectrum intravenous and total antibiotics expenditure.
Teo et al., 2012, Singapore [29]1700-bed teaching hospitalGuidelines, algorithm, review, audit and feedback,Before-after9.9% decrease in antibiotic consumption (p = 0.032) with cost savings of $198,575 for the hospital, and $91,194 for patients.
Michaels et al., 2012, USA [30]236-bed acute-care community hospitalRestriction, review and feedback, guidelines, educationBefore-afterAntimicrobial use decreased from 821.33 DDD/1000 patient-days to 778.77 DDD/1000 patient-days. Cost savings approached $290,000 from reduction in antibiotic expenditure.
Hagert et al., 2012, USA [31]39-bed acute care and 38-bed community hospitalComputerized decision support system, review and feedbackRetrospective (before-after) chart reviewPercentage of patients on antimicrobial decreased from 36.8% to 25% (p < 0.001). Total inpatient antimicrobial costs decreased by $48,044
Vettese et al., 2013, USA [32]253-bed Community hospitalIV to oral conversion, dose optimization, reviewBefore-after6.4% decline in days of therapy and a 37% reduction in total antimicrobial expenditure.
Cisneros et al., 2014, Spain [33]1251-bed teaching hospitalEducation and training, guidelines, counseling interviews, feedbackBefore-afterReduction in antimicrobial consumption from 1150 DDD/1000 patient-days to 852 DDD/1000 patient-days with 42% reduction in antimicrobial expenditure.
Borde et al., 2014, Germany [34]1600-bed teaching hospitalGuidelines revision information and education, review and feedbackBefore-after with interrupted time seriesSignificant decline in overall antibiotic use (p < 0.0001), significant decrease in cephalosporins and fluoroquinolones use (p < 0.001).
Bartlett & Siola, 2014, USA [35]155-bed community hospitalFormulary restriction, IV to oral conversion, automatic stop, review and feedbackBefore-afterAcquisition costs decreased by 25.5%, from $569,786 to $424,433 with a direct cost savings of $145,353. Antimicrobial use decreased from 1627 to 1338 DDD/1000 patient-days, a decrease of 17.8%.
Hou et al., 2014, China [36]12-bed ICU of a 700-bed tertiary teaching hospitalEducation, formulary restriction & preauthorizationBefore-afterTotal ICU antibiotic consumption decreased from 197.65 to 143.41DDD/100patient-days with improvement in bacterial resistance. Hospital-wide consumption also decreased from 69.69 DDDs to 50.76 (27.16% decrease)
Palmay et al., 2014, Canada [37]6 clinical service sections at a 1275-bed university hospitalEducation, audit and feedbackStepped-wedge randomized trialASP intervention was associated with 21% reduction in targeted antimicrobial (p = 0.004) with no reduction in cost and microbiological outcomes.
Chandy et al., 2014, India [38]2140-bed teaching hospitalAntibiotic policy guidelinesSegmented time seriesOverall antibiotic use increased at a monthly rate in segments 1, 2 & 3 of the study but drop significantly in monthly antibiotic use in segment 5.
Fukuda et al., 2014, Japan [39]429-bed community hospitalProspective audit with intervention and feedback, dose optimization, de-escalationBefore-after25.8% decrease in antimicrobial cost (p = 0.005), 80.0% decrease in aminoglycosides use (p < 0.001).
Cook & Gooch, 2015, USA [40]904-bed, tertiary-care teaching hospitalRestriction and prior approval, review and feedback, automatic stopProspective interventionalTotal antimicrobial use decreased by 62.8% (p < 0.0001). Aminoglycosides use decreased by 91.3% (p < 0.0001), cephalosporins decreased by 68.3% (p < 0.0001), extended-spectrum penicillins decreased by 77.7% (p < 0.0001), quinolones by 78.7% (p < 0.0001). Antifungal use decreased by 71.0% (p < 0.0001) during 13-year study period.
Taggart et al., 2015, Canada [41]2 ICUs at a 465-bed teaching hospitalAudit and feedbackControlled before-after withinterrupted time seriesTotal monthly antimicrobial use in one of the ICUs decreased by 375 DDD/1000 patient-days (p < 0.0009) after intervention.

3.2.2. Impact of ASP on Resistance Patterns and Clostridium Difficile Infection (CDI)

A total of 25 studies [42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66] assessed the impact of ASP on microbiological outcomes (bacterial resistance patterns) and/or CDI rates. Thirteen of the 25 studies assessed impact on CDI rates and other outcomes. Nine out of 13 studies reported a statistically significant reduction in the rate of CDI following ASP implementation [41,42,51,52,54,56,58,61,64]. Khan and Cheesbrough [44] and Malani et al. [62] reported a progressive fall in the rate of C. difficile-associated diarrhea (CDAD) and 50% decrease in the likelihood of developing CDI respectively. A restriction policy on ciprofloxacin and ceftriaxone resulted in a 70.20% reduction in the CDI with non-significant effect on extended spectrum beta lactamases (ESBL)—producing colliforms (p = 0.075) [63]—a proxy for antimicrobial resistance development.
Twelve studies assessed the impact on the resistance patterns of organisms commonly associated with hospital infections (ESKAPE: Enterococcus faecium, Staphylococcocus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species, e.g., Escherichia coli) [67] and reported a reduction in resistance or unchanged susceptibilities in these organisms following AMS interventions. Saizy-Callaert et al. [45] reported a significant fall in the rate of ESBL-producing Enterobacteriaceae (p < 0.001) following the development of a multidisciplinary consultative approach which included developing local prescribing consensus with all prescribers; restricted prescriptions policy; regular audits of use of restricted antibiotics and institutional wide training and information for prescribers. A restriction policy on ceftazidime resulted in a significant decrease in A. baumannii (p = 0.01) [48]. Table 3 summarizes studies that assessed impact on CDI rate and resistance patterns.
Table 3. Impact of ASP on resistance patterns and C. difficile infection.
Table 3. Impact of ASP on resistance patterns and C. difficile infection.
StudySettingAMS StrategyDesignResults
McNulty et al., 1997, UK [42]Elderly unit at a600-bed district hospitalGuideline, restriction following outbreak of CDIBefore-afterCDAD cases fell from 37 to 16 following restriction of cefuroxime.
Carling et al., 2003, USA [43]University-affiliated teaching hospitalFormulary, ProspectivemonitoringProspective interventionalSignificant fall in rates of CDI and Enterobacteriaceae infections, (p = 0.002 and p = 0.02) respectively during 7 years of ASP.
Khan & Chessbrough 2003, UK [44]800-bed district hospitalFormulary change, IV to oral conversionBefore-afterProgressive fall in incidence of CDAD over 5-year period.
Saizy-Callaert et al., 2003, France [45]600-bed hospital with 5 teaching departmentGuideline, restriction, training, feedbackBefore-afterSignificant fall in ESBL-producing Enterobacteriaceae (p < 0.001). MRSA and CRP rates remained stable.
Bantar et al., 2003, Argentina [46]250-bed teaching hospital for adultsAntibiotic order form, feedback, education, prescription changeProspective interventionalNS change in resistance of E. coli and K. pneumoniae to 3rd-generation cephalosporins, but decreasing resistance of P. mirabilis and E. cloacae observed. Imipenem-resistant P. aeruginosa decreased to 0%.
Martin et al., 2005, USA [47]University hospitalGuidelines, formulary restrictionProspective interventionalIncreased susceptibility of P. aeruginosa to piperacillin/tazobactam, ceftazidime and fluoroquinolones. 3% reduction in MRSA rate and decreased resistance of K. pnuemoniae to ceftazidime.
Brahmi et al., 2006, Tunisia [48]12-bed ICUCeftazidime restrictionBefore-afterSignificant (p = 0.01) decrease in A. baumannii to ceftazidime. Considerable reduction in ESBL-producing K. pneumoniaeresistance to ceftazidime.
Ntagiopoulos et al., 2007, Greece [49]12-bed ICU of 700-bed university-affiliated general hospitalRestriction of fluoroquinolones and ceftazidimeBefore-afterSignificant increase in susceptibilities of A. baumannii, P. aeruginosa and K. pneumoniae to ciprofloxacin (p < 0.01).
Mach et al., 2007, Czech Republic [50]500-bed general hospitalGuidelines, restriction, educationBefore-afterNS decrease in resistance to restricted antimicrobials, and NS increase in resistance to non-restricted antimicrobials. Decreased resistance of E. aerogenesand K. pneumoniae to ofloxacin, gentamicin and ceftazidime.
Fowler et al., 2007, UK [51]Three acute-care wards for elder at a 1200-bed tertiary hospitalNarrow-spectrum’ antibiotic policy, feedback, cephalosporin restrictionBefore-after with ITSSignificant (p = 0.009) fall in CDI, no reported rise in infection control procedures; MRSA remained unchanged (p = 0.32).
Valiquet et al.,2007, Canada [52]683-bed secondary/tertiary care hospitalGuidelines, educationBefore-after with ITSSignificant (p = 0.007) fall in CDAD incidence, no change (p = 0.63) following enhanced infection control.
Ozorowski et al., 2009, Poland [53]120-bed hematology and blood transfusion tertiary care centerGuidelines, educationBefore-afterSuccessful control of VRE outbreak and improvement in the resistance patterns of gram-negative bacteria.
Talpaert et al., 2011, [54]450-bed university affiliated general hospitalGuideline and restriction of ‘high-risk’ antibiotics, educationQuasi-experimental with ITSSignificant fall in CDI incidence (p < 0.0001).
Altunsoy et al., 2011, Turkey [55] Nation-wide restriction programBefore-afterDecrease in MRSA rates from 44% to 41%. Decrease in the use of carbapenems correlated with decrease in carbapenem-resistant Pseudomonas and Acinetobacter species.
Cook et al., 2011 [56]861-bed university teaching hospitalEMR implementationBefore-after with ITS18.7% decrease in CDI (p = 0.07) and 45.2% decrease in MRSA (p < 0.0001).
Niwa et al., 2012, Japan [57]606-bed university hospitalProspective review, guidelines, de-escalation, educationBefore-afterSignificant reduction in MRSA and Serratia marcescens occurrence (p = 0.026 and p = 0.026) respectively. NS decrease in P. aureginosa resistant to ceftazidime and piperacillin.
Aldeyab et al., 2012, UK [58]233-bed hospitalRevised antibiotic policy that avoided ‘high-risk’ antibioticsRetrospective intervention with ITSSignificant decrease in CDI incidence rate (p  =  0.0081); CDI decreased by 0.0047/100 bed-days per month.
Jaggi et al., 2012, India [59]Tertiary care hospitalAntibiotic policy, restriction, audit and feedbackProspective interventional4.03% reduction in carbapenem-resistant Pseudomonas. Rising trend in E.coli, K. pnemoniae and A. baumanniicarbapenem resistance was recorded.
Sarraf-Yazdi et al., 2012, USA [60]16-bed surgical ICU at an academic medical centerAntibiotic cyclingControlled before-afterImproved susceptibility of pseudomonal isolates to ceftazidime (p = 0.003) and (piperacillin/tazobactam p = 0.02). Improved susceptibility of E. coli to piper/tazobactam (p < 0.0005).
Nowak et al., 2012, USA [61]583-bed tertiary referral hospitalComputer surveillance & decision support system (data-mining software), educationProspective before-afterSignificant decrease in rates of CDI and VRE, (p = 0.018 and = 0.0004 respectively). NS difference in rate of MRSA (p = 0.09).
Malani et al., 2013, USA [62]535-bed non-university affiliated community teaching hospitalReview, feedback, automatic stop, de-escalationRetrospective observationalLikelihood of developing CDI decreased by 50% (p < 0.01).
Dancer et al, 2013, UK [63]450-bed district general hospitalEducation, restriction following outbreakProspective interventional77% reduction in CDI rate. NS effect on MRSA rate (p = 0.62) and borderline effect of ESBL-producing coliforms (p = 0.075).
Wenisch et al., 2014, Austria [64]1000-bed tertiary care community hospitalMoxifloxacin restriction, educationBefore-after46% reduction in CDI cases (p = 0.0044).
Knudsen & Andersen, 2014, Denmark [65]University hospitalGuidelines, educationControlled before-after with ITSSignificant reduction in ESBL-producing K. pneumoniae infections (p < 0.001).Significant increase in pipercillin-tazobactam-resistant P. aeruginosa and E. faeciuminfections were also recorded (p < 0.033).
Sarma et al., 2015, UK [66]2 acute hospitals (combined bed 800)Fluoroquinolone restrictionBefore-after with ITSSignificant fall in CDI over a 60-month period.
NS—Non-significant, CRP—Ceftazidimie-resistant Pseudomonas, CDAD—Clostridium difficile associated diarrhea, ID—Infectious diseases, MRSA—Methicillin-resistant Staphylococcus aureus, CDI—Clostridium difficile infection, ESBL—Extended spectrum-producing beta-lactamases, VRE—Vancomycin-resistant enterococcus, SSI—surgical site infections, EMR—Electronic medical record.

3.2.3. Impact of ASP on Patient Outcomes

Thirteen studies [68,69,70,71,72,73,74,75,76,77,78,79,80] were identified that reported impact on patient outcomes. Gums et al. [68] reported shorter LOS in the intervention group than the control group (9.0 vs. 5.7; p = 0.0001) and 6.3% mortality in the intervention group compare to 12.0% in the control group. Similarly, Ng et al. [70] reported a significant (7.46 and 6.97 respectively; p < 0.001) difference in LOS between the periods before and after ASP implementation with no difference in mortality. Six studies reported non-significant difference in mortality [69,71,72,73,75,78].Okumura et al. [80] however reported lower 30-day mortality with bundled ASP (intervention consisting of clinical pharmacist chart review, discussion with microbiologist and infectious disease physicians, local education and continuous follow-up) (p < 0.01) than conventional ASP. One study assessed incidence of adverse reactions following carbapenem de-escalation and reported that the de-escalated group had fewer adverse reaction (11/204 (5.4%) vs. 12/96 (12.5%); p = 0.037) [79].
A summary of studies that assessed impact on patient outcomes is provided in Table 4.
Table 4. Impact of ASP on patient outcomes.
Table 4. Impact of ASP on patient outcomes.
StudySettingAMS StrategyDesignResults
Gum et al., 1999, USA [68]275-bed community hospitalProspective review with interventionProspective RCTShorter LOS in the intervention group than the control group (9.0 vs. 5.7; p = 0.0001). Mortality rate was 12.0% (15/125) in the control group and 6.3% (8/127) in the intervention group.
Chang et al., 2006, Taiwan [69]921-bed medical centerGuidelines, restriction and prior approval, educationBefore-afterNo change in LOS, mortality and readmission rates in the pre- and post-intervention periods.
Ng et al., 2008, Hong Kong [70]1800-bed acute hospitalGuideline, antibiotic order form, restriction, review and feedbackBefore-afterSignificant difference in LOS between pre- and post-ASP (7.46 vs.6.97 days, (p < 0.001).
NS difference in mortality (8.8% vs. 8.4%, (p = 0.28). Significant unplanned readmissions related to infections post-ASP (17.6% vs. 18.7%, (p = 0.008).
Chan et al., 2011, Taiwan [71]3500-bed medical centerHospital-wide computerized antimicrobial approval system linked to electronic medical record, monitoring, review, feedbackProspective interventionalDecreasing trends in mortality over a period of 7 years 3.45%, 3.53%, 3.41%, 3.30%, 3.28%, 3.27%, and 3.23%.
Liew et al., 2012, Singapore [72]1559-bed tertiary-care hospitalGuidelines, posters, prospective review with interventionRetrospective review of ASP interventionsShorter LOS in patients whose physicians accepted interventions than those interventions were rejected (19.9 vs. 24.2 days, p < 0.001). NS (p = 0.191) difference in overall mortality and infection-related mortality between the two groups. Infection-related readmission and 14-day re-infection was higher in patients whose physicians rejected AS interventions (p < 0.001 and p = 0.009) respectively.
DiazGranados, C., 2012, USA [73]ICU at a 1000-bed community teaching hospitalProspective audit with intervention and feedback (PAIF)Prospective quasi-experimentalNS (p = 0.68) difference in mortality between patients in intervention group and baseline. Hospital and ICU LOS was shorter in the PAIF group than the baseline.
Rimawi et al., 2013, USA [74]24-bed medial ICU at 861-bed teaching hospitalReview and feedbackBefore-afterSignificant reduction in mechanical ventilation days (p = 0.0053), LOS (p = 0.0188), and hospital mortality (p = 0.0367). NS difference in medical ICU mortality (p = 0.4970).
Lin et al., 2013, Taiwan [75]415-bed non-university affiliated community teaching hospitalEducation, prospective review with intervention and feedbackBefore-afterNS difference inLOS and mortality.
Tsukamoto et al., 2014, Japan [76]600-bed university teaching hospitalDaily review and feedbackBefore-after30-day mortality was lower in post-intervention than pre-intervention period (14.3% vs. 22.9%, p = 0.2).
Pasquale et al., 2014, USA [77]577-bed community teaching hospitalDe-escalation, dose optimization, ID consultRetrospective review of ASP interventions in patients with ABSSSIsMean LOS was shorter (4.4 days vs. 6.2 days; p < 0.001) compared to historical data. 30-day all-cause readmission rate was lower (6.5% vs. 16.71%, p = 0.05) in intervention group but 30-day ABSSSI readmission rate did not differ between intervention and historical groups (p = 0.483).
Rosa, Goldani & dos Santos, 2014, Brazil [78]Hematology ward of teaching hospitalASP guidelines for cancer patients with febrile neutropeniaProspective cohortAdherence to ASP guidelines was associated with lower mortality (hazard ratio, 0.36; 95% confidence interval, 0.14–0.92).
Lew et al., 2015, Singapore [79]1500-bed teaching hospitalDe-escalation of carbapenem therapyRetrospective review of ASP interventionsNS difference in clinical success, survival at discharge, 30 day mortality, 30 day readmission and LOS between de-escalated and non-de-escalated groups. There was difference in antibiotic-associated diarrhea (4.4% vs. 12.5%; p = 0.015) the between the two groups.
Okumura, da Silva & Veroneze, 2015, Brazil [80]550-bed university hospitalBundled ASP comprisingdaily review and feedback, de-escalation, education, follow up till resolutionRetrospective historical cohort30-day mortality was lower with bundled ASP (p < 0.01) than conventional ASP (which comprised passive chart review, discussion with ID and telephone call when intervention was necessary).
NS—Non-significant, LOS—Length of stay, ABSSSIs—acute bacterial skin and skin structure infections.

4. Discussion

Our review identified a number of quality measures used in assessing ASP in primary studies. These include change in antimicrobial use, cost savings, resistance patterns of some difficult to treat organisms, rates of CDI, length of stay (LOS), readmission rate and mortality. These measures were classified into two main categories namely process and outcome measures [20,81]. Change in antimicrobial use (such as total quantity of antimicrobial or targeted antimicrobial class) measured usually in the WHO recommended defined daily dose (DDD)/100 or 1000 patient-days [81] is a process measure [4,20]. Other process measures recommended for use in assessing ASP include documentation of indication for antibiotic prescribed, documentation of stop/review date, 48–72 hours review after initiation of antibiotic therapy, level of adherence to hospital-specific guidelines, level of acceptance of AMS recommendations, time to appropriate therapy in patients with sepsis, and rate of de-escalation of initial therapy [12,14,82]. Outcome measures are categorized into microbiological, clinical and financial outcomes [82]. Microbiological outcomes include measures such as percentage of difficult to treat organisms e.g., MRSA, ESBL-producing Enterobacteriaceae, rate of isolation of resistant organisms, and rate of CDI [83]. Clinical outcome measures used in assessing impact of ASP include all-cause mortality, LOS and readmission rates; clinical improvement and rate of adverse antimicrobial reactions have also been recommended [20,82,84].
Currently, there are no standard, universally accepted metrics for assessing ASP. For example, DDD whilst widely used in quantifying and reporting antimicrobial use continues to be debated because of its limitations [85]. The limitations of using DDD include its inability to provide information on the number of patients actually exposed to antibiotics; it cannot be used for children, and it underestimates the use for drugs that require reduced dosage due to renal impairment [85,86]. Morris et al. [18], in a structured panel to determine quality metrics for ASP, suggested days of therapy/1000 patient-days as a more appropriate measure for public reporting of ASP impact. Similarly, Aldeyab et al. [85] in a study that adjusted DDD to include age-adjusted comorbidity score (DDD/100 bed-days/age-adjusted comorbidity score) concluded that the modified unit provides “an innovative approach to measuring antibiotic use while taking into account the effect of patient case mix”. Prescribed daily dose has also been suggested as an alternative or a complement to DDD [20]. Whether these metrics provide the appropriate standards for assessing ASP has not been determined. However, the majority of the studies that reported significant reductions in antimicrobial use employed DDD/1000 or 100 patient-days as the metric [24,33,34].
Assessing the impact of ASP on resistance using the identified metrics has inherent limitations. This is because several factors affect the development of resistance, which makes it difficult to establish a clear causal association between AMS interventions and decrease in resistance [9,20,49]. However, ASP especially those employing restriction on use of ‘high-risk’ antibiotic classes (second- and third-generation cephalosprins, fluoroquinolones) have been shown to reduce resistance and/or improve bacterial susceptibilities [47,63,65]. Although stewardship interventions have been shown to reduce resistance, their use as a primary measure for evaluating ASP has been cautioned [87]. Rate of CDI has been used as a measure for assessing ASP. Programs incorporating restriction or avoidance of the ‘high-risk’ antibiotic classes and clindamycin are notably associated with significant reduction in CDI rate [42,44,51,52,54,58,63]. Studies with marked reduction in CDI often also have strict infection control programs in place; which makes the association between ASP and the reduction in CDI rate difficult. However, infection control alone has been shown not to effectively control the outbreak of CDI. A significant reduction in rates followed stewardship interventions that involved restriction or avoidance of the ‘high-risk’ antibiotics [42,52,63].
The primary goal of ASP is to optimize patient outcomes. Six out of 13 studies included in this review reported non-significant difference in mortality [69,71,72,73,75,79]. Six studies reported shorter LOS between the pre- and post-intervention periods [68,70,72,73,74,77]. Notably, Ng et al. [70] reported significant difference in LOS between the periods before and after ASP implementation (7.46 and 6.97 respectively; p < 0.001). Interestingly, the same study reported statistically significant (p < 0.001) higher unplanned readmissions related to infections post-ASP.
Evaluation of ASP requires the use of patient-specific measures that demonstrate attainment of the primary goal. However, some limitations affect effective evaluation. These include difficulty in establishing a clear causal association between ASP interventions and measures such as mortality and LOS due to confounders that affect these measures [9,20]. Mortality related to antimicrobial-resistant organisms and infection-related hospital stay has been suggested as better patient measures for use in assessing impact [18,70]. Lack of personnel, funds, and health information technology personnel, and the inability to generate and analyze ASP-specific data have also been identified as limitations to effective ASP [9,88]. Inadequate study design also limits a clear association between ASP interventions and reported impact. Studies of interventions to improve hospital antimicrobial use are reported to be largely of poor design [11].
This review did not apply the strict quality criteria required for a systematic review of included studies, and risk of bias was not assessed. The purpose of this review was to provide an overview of the quality measures used in assessing ASP in primary studies, therefore all study designs were included. Studies assessing patient specific outcomes were of particular interest. Future work is planned to include evaluation and impact of ASP in pediatric patients.

5. Conclusions

Patient outcomes need to be a key component of ASP evaluation. The choice of metrics is influenced by data and resource availability. Controlling for confounders and unintended adverse consequences must be considered in the design of evaluation studies to adequately capture the impact of ASP. This review provides a starting pointfor compiling standard outcome metrics for assessing ASP.


Mary Richard Akpan is a doctoral student at the University of Hertfordshire, Hatfield. Her PhD is funded by University of Uyo, Nigeria and the Federal Republic of Nigeria. Raheelah Ahmad is partially funded by the National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU-2012-10047) in Healthcare Associated Infections and Antimicrobial Resistance at Imperial College London in partnership with Public Health England (PHE) and the NIHR Imperial Patient Safety Translational Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England. Naomi Fleming Advanced Pharmacist: Microbiology and Infectious Disease, Kettering General Hospital is acknowledged for her review and comments of the manuscript prior to submission of the revised manuscript.

Author Contributions

M.R.A. did the literature search and wrote the initial draft of the manuscript. R.A., N.A.S. and D.A.-O. discussed the draft and together produced the final version.

Conflicts of Interest

The authors declare no conflict of interest.


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