Implementation of a computerized decision support system to improve the appropriateness of antibiotic therapy using local microbiologic data

Diagnosis and management of infections caused by multidrug-resistant bacteria: guideline endorsed by the Italian Society of Infection and Tropical Diseases (SIMIT), the Italian Society of Anti-Infective Therapy (SITA), the Italian Group for Antimicrobial Stewardship (GISA), the Italian Association of Clinical Microbiologists (AMCLI) and the Italian Society of Microbiology (SIM)

Management of patients with infections caused by multidrug-resistant organisms is challenging and requires a multidisciplinary approach to achieve successful clinical outcomes. The aim of this paper is to provide recommendations for the diagnosis and optimal management of these infections, with a focus on targeted antibiotic therapy. The document was produced by a panel of experts nominated by the five endorsing Italian societies, namely the Italian Association of Clinical Microbiologists (AMCLI), the Italian Group for Antimicrobial Stewardship (GISA), the Italian Society of Microbiology (SIM), the Italian Society of Infectious and Tropical Diseases (SIMIT) and the Italian Society of Anti-Infective Therapy (SITA). Population, Intervention, Comparison and Outcomes (PICO) questions about microbiological diagnosis, pharmacological strategies and targeted antibiotic therapy were addressed for the following pathogens: carbapenem-resistant Enterobacterales; carbapenem-resistant Pseudomonas aeruginosa; carbapenem-resistant Acinetobacter baumannii; and methicillin-resistant Staphylococcus aureus. A systematic review of the literature published from January 2011 to November 2020 was guided by the PICO strategy. As data from randomised controlled trials (RCTs) were expected to be limited, observational studies were also reviewed. The certainty of evidence was classified using the GRADE approach. Recommendations were classified as strong or conditional. Detailed recommendations were formulated for each pathogen. The majority of available RCTs have serious risk of bias, and many observational studies have several limitations, including small sample size, retrospective design and presence of confounders. Thus, some recommendations are based on low or very-low certainty of evidence. Importantly, these recommendations should be continually updated to reflect emerging evidence from clinical studies and real-world experience.

Can evidence-based decision support tools transform antibiotic management? A systematic review and meta-analyses

OBJECTIVES

To assess the effectiveness of clinical decision support systems (CDSSs) at reducing unnecessary and suboptimal antibiotic prescribing within different healthcare settings.

METHODS

A systematic review of published studies was undertaken with seven databases from database inception to November 2018. A protocol was developed using the PRISMA-P checklist and study selection criteria were determined prior to performing the search. Critical appraisal of studies was undertaken using relevant tools. Meta-analyses were performed using a random-effects model to determine whether CDSS use affected optimal antibiotic management.

RESULTS

Fifty-seven studies were identified that reported on CDSS effectiveness. Most were non-randomized studies with low methodological quality. However, randomized controlled trials of moderate methodological quality were available and assessed separately. The meta-analyses indicated that appropriate antibiotic therapy was twice as likely to occur following the implementation of CDSSs (OR 2.28, 95% CI 1.82-2.86, k = 20). The use of CDSSs was also associated with a relative decrease (18%) in mortality (OR 0.82, 95% CI 0.73-0.91, k = 18). CDSS implementation also decreased the overall volume of antibiotic use, length of hospital stay, duration and cost of therapy. The magnitude of the effect did vary by study design, but the direction of the effect was consistent in favouring CDSSs.

CONCLUSIONS

Decision support tools can be effective to improve antibiotic prescribing, although there is limited evidence available on use in primary care. Our findings suggest that a focus on system requirements and implementation processes would improve CDSS uptake and provide more definitive benefits for antibiotic stewardship.

Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms

The rapid evolution of resistance, particularly among Gram-negative bacteria, requires appropriate identification of patients at risk followed by administration of appropriate empiric antibiotic therapy. A primary tenet of antimicrobial stewardship programs (ASPs) is the establishment of empiric antibiotic recommendations for commonly encountered infections. An important tool in providing empiric antibiotic therapy recommendations is the use of an antibiogram. While the majority of institutions use a traditional antibiogram, ASPs have an opportunity to enhance antibiogram data. The authors provide the rationale for why ASPs should implement alternative antibiograms, and the importance of incorporating an antibiogram into clinical decision support systems with the goal of providing effective empiric antibiotic therapy.

Linking antimicrobial resistance surveillance to antibiotic policy in healthcare settings: the COMBACTE-Magnet EPI-Net COACH project

OBJECTIVES

To systematically summarize the evidence on how to collect, analyse and report antimicrobial resistance (AMR) surveillance data to inform antimicrobial stewardship (AMS) teams providing guidance on empirical antibiotic treatment in healthcare settings.

METHODS

The research group identified 10 key questions about the link between AMR surveillance and AMS using a checklist of 9 elements for good practice in health research priority settings and a modified 3D combined approach matrix, and conducted a systematic review of published original studies and guidelines on the link between AMR surveillance and AMS.

RESULTS

The questions identified focused on AMS team composition; minimum infrastructure requirements for AMR surveillance; organisms, samples and susceptibility patterns to report; data stratification strategies; reporting frequency; resistance thresholds to drive empirical therapy; surveillance in high-risk hospital units, long-term care, outpatient and veterinary settings; and surveillance data from other countries. Twenty guidelines and seven original studies on the implementation of AMR surveillance as part of an AMS programme were included in the literature review.

CONCLUSIONS

The evidence summarized in this review provides a useful basis for a more integrated process of developing procedures to report AMR surveillance data to drive AMS interventions. These procedures should be extended to settings outside the acute-care institutions, such as long-term care, outpatient and veterinary. Without proper AMR surveillance, implementation of AMS policies cannot contribute effectively to the fight against MDR pathogens and may even worsen the burden of adverse events from such interventions.

Effectiveness of Electronic Guidelines (GERH®) to Improve the Clinical Use of Antibiotics in An Intensive Care Unit

The objective of the study was to evaluate the capacity of GERH^®-derived local resistance maps (LRMs) to predict antibiotic susceptibility profiles and recommend the appropriate empirical treatment for ICU patients with nosocomial infection. Data gathered between 2007 and 2016 were retrospectively studied to compare susceptibility information from antibiograms of microorganisms isolated in blood cultures, lower respiratory tract samples, and urine samples from all ICU patients meeting clinical criteria for infection with the susceptibility mapped by LRMs for these bacterial species. Susceptibility described by LRMs was concordant with in vitro study results in 73.9% of cases. The LRM-predicted outcome agreed with the antibiogram result in >90% of cases infected with the bacteria for which GERH^® offers data on susceptibility to daptomycin, vancomycin, teicoplanin, linezolid, and rifampicin. Full adherence to LRM recommendations would have improved the percentage adequacy of empirical prescriptions by 2.2% for lower respiratory tract infections (p = 0.018), 3.1% for bacteremia (p = 0.07), and 5.3% for urinary tract infections (p = 0.142). LRMs may moderately improve the adequacy of empirical antibiotic therapy, especially for lower respiratory tract infections. LRMs recommend appropriate prescriptions in approximately 50% of cases but are less useful in patients with bacteremia or urinary tract infection.

Clinical Decision Support Systems and Their Role in Antibiotic Stewardship: a Systematic Review

PURPOSE OF REVIEW

The purpose of this article is to perform a systematic review over the past 5 years on the role and effectiveness of clinical decision support systems (CDSSs) on antibiotic stewardship.

RECENT FINDINGS

CDDS interventions found a significant impact on multiple outcomes relevant to antibiotic stewardship. There are various types of CDSS implementations, both active and passive (provider initiated). Passive interventions were associated with more significant outcomes; however, both interventions appeared effective. In the reviewed literature, CDSSs were consistently associated with decreasing antibiotic consumption and narrowing the spectrum of antibiotic usage. Generally, guideline adherence was improved with CDSS, although this was not universal. The effect on other outcomes, such as mortality, Clostridiodes difficile infections, length of stay, and cost, inconsistently showed a significant difference. Overall, CDDS implementation has effectively decreased antibiotic consumption and improved guideline adherence across the various types of CDSS. Other positive outcomes were noted in certain settings, but were not universal. When creating a new intervention, it is important to identify the optimal structure and deployment of a CDSS for a specific setting.

Preliminary readings of antimicrobial susceptibility panels: A simple, fast and inexpensive way to detect bacterial resistance and enhance antibiotic treatment of bloodstream infections

Increasing incidence of resistant bacteria needs faster identification (ID) and antibiotic susceptibility testing (AST) in order to improve antimicrobial treatment of severe infections. We propose a preliminary reading of the AST MicroScan® panels coupled with mass spectrometry ID. A total of 157 bacterial clinical isolates were processed for routine ID and AST (in 22 cases, ID and AST were performed directly from positive blood culture bottles). For gram-negatives, data from the initial and final readings were recorded and compared [89.9% category agreement (CA), 6.9% very major errors (VME)]. In adition all the 32 ESBL producers were detected at 5.3-8.6 hours. For Staphylococcus aureus, all the 16 MRSA isolates were detected at 4.5 to 7.5 hours. Thus, we find our preliminary readings approach as a simple, inexpensive and reliable way to detect and identify the most prevalent resistant bacteria in our institution on the same day that ID/AST is performed.

Participatory implementation of an antibiotic stewardship programme supported by an innovative surveillance and clinical decision-support system

BACKGROUND

Antibiotic resistance will cause about 10 million deaths per year by 2050. Fighting antimicrobial resistance is a health priority. Interventions aimed to reduce antimicrobial resistance, such as antibiotic stewardship programmes (ASPs), must be implemented. To be effective, those interventions, and the implementation process, should be matched with social-cultural context. The complexity of ASPs can no longer be developed without considering both organizational and information systems.

AIM

To support ASPs through the co-design and implementation, in collaboration with healthcare workers, of a surveillance and clinical decision-support system to monitor antibiotic resistance and improve antibiotic prescription.

METHODS

The surveillance and clinical decision-support system was designed and implemented in three Portuguese hospitals, using a participatory approach between researchers and healthcare workers following the Design Science Research Methodology.

FINDINGS

Based on healthcare workers' requirements, we developed HAITooL, a real-time surveillance and clinical decision-support system that integrates visualizations of patient, microbiology, and pharmacy data, facilitating clinical decision. HAITooL monitors antibiotic usage and rates of antibiotic-resistant bacteria, allowing early identification of outbreaks. It is a clinical decision-support tool that integrates evidence-based algorithms to support proper antibiotic prescription. HAITooL was considered valuable to support monitoring of antibiotic resistant infections and an important tool for ASP sustainability.

CONCLUSION

ASP implementation can be leveraged through a surveillance and clinical decision-support system such as HAITooL that allows antibiotic resistance monitoring and supports antibiotic prescription, once it has been adapted to the context and specific needs of healthcare workers and hospitals.

A decision support system for antibiotic prescription based on local cumulative antibiograms

BACKGROUND

Local cumulative antibiograms are useful tools with which to select appropriate empiric or directed therapies when treating infectious diseases at a hospital. However, data represented in traditional antibiograms are static, incomplete and not well adapted to decision-making.

METHODS

We propose a decision support method for empiric antibiotic therapy based on the Number Needed to Fail (NNF) measure. NNF indicates the number of patients that would need to be treated with a specific antibiotic for one to be inadequately treated. We define two new measures, Accumulated Efficacy and Weighted Accumulated Efficacy in order to determine the efficacy of an antibiotic. We carried out two experiments: the first during which there was a suspicion of infection and the patient had empiric therapy, and the second by considering patients with confirmed infection and directed therapy. The study was performed with 15,799 cultures with 356,404 susceptibility tests carried out over a four-year period.

RESULTS

The most efficient empiric antibiotics are Linezolid and Vancomycin for blood samples and Imipenem and Meropenem for urine samples. In both experiments, the efficacies of recommended antibiotics are all significantly greater than the efficacies of the antibiotics actually administered (P < 0.001). The highest efficacy is obtained when considering 2 years of antibiogram data and 80% of the cumulated prevalence of microorganisms.

CONCLUSION

This extensive study on real empiric therapies shows that the proposed method is a valuable alternative to traditional antibiograms as regards developing clinical decision support systems for antimicrobial stewardship.

The effectiveness of computerised decision support on antibiotic use in hospitals: A systematic review

Background

Inappropriate antimicrobial use has been shown to be an important determinant of the emergence of antimicrobial resistance (AMR). Health information technology (HIT) in the form of Computerised Decision Support (CDS) represents an option for improving antimicrobial prescribing and containing AMR.

Objectives

To evaluate the evidence for CDS in improving quantitative and qualitative measures of antibiotic prescribing in inpatient hospital settings.

Methods

A systematic literature search was conducted of articles published from inception to 20^th December 2014 using eight electronic databases: MEDLINE, EMBASE, PUBMED, Web of Science, CINAHL, Cochrane Library, HMIC and PsychINFo. An updated systematic literature search was conducted from January 1^st 2015 to October 1^st 2016 using PUBMED. The search strategy used combinations of the following terms: (electronic prescribing) OR (clinical decision support) AND (antibiotic or antibacterial or antimicrobial) AND (hospital or secondary care or inpatient). Studies were evaluated for quality using a 10-point rating scale.

Results

Eighty-one studies were identified matching the inclusion criteria. Seven outcome measures were evaluated: adequacy of antibiotic coverage, mortality, volume of antibiotic usage, length of stay, antibiotic cost, compliance with guidelines, antimicrobial resistance, and CDS implementation and uptake. Meta-analysis of pooled outcomes showed CDS significantly improved the adequacy of antibiotic coverage (n = 13; odds ratio [OR], 2.11 [95% CI, 1.67 to 2.66, p ≤ 0.00001]). Also, CDS was associated with marginally lowered mortality (n = 20; OR, 0.85 [CI, 0.75 to 0.96, p = 0.01]). CDS was associated with lower antibiotic utilisation, increased compliance with antibiotic guidelines and reductions in antimicrobial resistance. Conflicting effects of CDS on length of stay, antibiotic costs and system uptake were also noted.

Conclusions

CDS has the potential to improve the adequacy of antibiotic coverage and marginally decrease mortality in hospital-related settings.

Interleukin-6 Kinetics can be Useful for Early Treatment Monitoring of Severe Bacterial Sepsis and Septic Shock

Early appropriate anti-microbial therapy is necessary to improve outcomes of septic patients. We describe 20 case histories of patients with severe bacterial sepsis regarding kinetics of several biomarkers. We found that interleukin-6 is able to predict survival and might be able to evaluate appropriateness of anti-microbial therapy.

Patient and physician predictors of patient receipt of therapies recommended by a computerized decision support system when initially prescribed broad-spectrum antibiotics: a cohort study

OBJECTIVE

Antibiotic computerized decision support systems (CDSSs) were developed to guide antibiotic decisions, yet prescriptions of CDSS-recommended antibiotics have remained low. Our aim was to identify predictors of patients' receipt of empiric antibiotic therapies recommended by a CDSS when the prescribing physician had an initial preference for using broad-spectrum antibiotics.

METHODS

We conducted a prospective cohort study in a 1 500-bed tertiary-care hospital in Singapore. We included all patients admitted from October 1, 2011 through September 30, 2012, who were prescribed piperacillin-tazobactam or carbapenem for empiric therapy and auto-triggered to receive antibiotic recommendations by the in-house antibiotic CDSS. Relevant data on the patient, prescribing and attending physicians were collected via electronic linkages of medical records and administrative databases. To account for clustering, we used multilevel logistic regression models to explore factors associated with receipt of CDSS-recommended antibiotic therapy.

RESULTS

One-quarter of the 1 886 patients received CDSS-recommended antibiotics. More patients treated for pneumonia (33.2%) than sepsis (12.1%) and urinary tract infections (7.1%) received CDSS-recommended antibiotic therapies. The prescribing physician - but not the attending physician or clinical specialty - accounted for some (13.3%) of the variation. Prior hospitalization (odds ratio [OR] 1.32, 95% CI, 1.01-1.71), presumed pneumonia (OR 6.77, 95% CI, 3.28-13.99), intensive care unit (ICU) admission (OR 0.38, 95% CI, 0.21-0.66), and renal impairment (OR 0.70, 95% CI, 0.52-0.93) were factors associated with patients' receipt of CDSS-recommended antibiotic therapies.

CONCLUSIONS

We observed that ICU admission and renal impairment were negative predictors of patients' receipt of CDSS-recommended antibiotic therapies. Patients admitted to ICU and those with renal impairment might have more complex clinical conditions that require a physician's assessment in addition to antibiotic CDSS.

Computerized Clinical Decision Support: Contributions from 2014

OBJECTIVE

To summarize recent research and propose a selection of best papers published in 2014 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook.

METHOD

A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry systems in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers.

RESULTS

Among the 1,254 returned papers published in 2014, the full review process selected four best papers. The first one is an experimental contribution to a better understanding of unintended uses of CDSSs. The second paper describes the effective use of previously collected data to tailor and adapt a CDSS. The third paper presents an innovative application that uses pharmacogenomic information to support personalized medicine. The fourth paper reports on the long-term effect of the routine use of a CDSS for antibiotic therapy.

CONCLUSIONS

As health information technologies spread more and more meaningfully, CDSSs are improving to answer users' needs more accurately. The exploitation of previously collected data and the use of genomic data for decision support has started to materialize. However, more work is still needed to address issues related to the correct usage of such technologies, and to assess their effective impact in the long term.

Combining expert knowledge and knowledge automatically acquired from electronic data sources for continued ontology evaluation and improvement

INTRODUCTION

A common bottleneck during ontology evaluation is knowledge acquisition from domain experts for gold standard creation. This paper contributes a novel semi-automated method for evaluating the concept coverage and accuracy of biomedical ontologies by complementing expert knowledge with knowledge automatically extracted from clinical practice guidelines and electronic health records, which minimizes reliance on expensive domain expertise for gold standards generation.

METHODS

We developed a bacterial clinical infectious diseases ontology (BCIDO) to assist clinical infectious disease treatment decision support. Using a semi-automated method we integrated diverse knowledge sources, including publically available infectious disease guidelines from international repositories, electronic health records, and expert-generated infectious disease case scenarios, to generate a compendium of infectious disease knowledge and use it to evaluate the accuracy and coverage of BCIDO.

RESULTS

BCIDO has three classes (i.e., infectious disease, antibiotic, bacteria) containing 593 distinct concepts and 2345 distinct concept relationships. Our semi-automated method generated an ID knowledge compendium consisting of 637 concepts and 1554 concept relationships. Overall, BCIDO covered 79% (504/637) of the concepts and 89% (1378/1554) of the concept relationships in the ID compendium. BCIDO coverage of ID compendium concepts was 92% (121/131) for antibiotic, 80% (205/257) for infectious disease, and 72% (178/249) for bacteria. The low coverage of bacterial concepts in BCIDO was due to a difference in concept granularity between BCIDO and infectious disease guidelines. Guidelines and expert generated scenarios were the richest source of ID concepts and relationships while patient records provided relatively fewer concepts and relationships.

CONCLUSIONS

Our semi-automated method was cost-effective for generating a useful knowledge compendium with minimal reliance on domain experts. This method can be useful for continued development and evaluation of biomedical ontologies for better accuracy and coverage.