Decision Support for Antibiotic Selection in Sepsis

(IDEAS-CRXO Trial)

Recruiting at 2 trial locations

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Ottawa Hospital Research Institute

Must be taking: Antibiotics

Disqualifiers: Pregnancy, End-of-life care, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 3 Jurisdictions This treatment is already approved in other countries

Compensation: Varies

Number of Visits: Unknown

Duration: 48 hours

1440 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new decision support tool to help doctors select the right antibiotics for patients with suspected sepsis, a serious infection that can spread throughout the body. The goal is to ensure patients receive the appropriate level of antibiotic treatment, avoiding overly broad or narrow options that can lead to antibiotic resistance. The trial compares usual care with this new tool, which uses patient-specific data to suggest the best antibiotic choices. It is suitable for those recently hospitalized with suspected sepsis and started on certain antibiotics like Vancomycin or Meropenem (also known as Merrem IV). As an unphased trial, this study allows patients to contribute to innovative research that could improve antibiotic selection and patient outcomes.

Will I have to stop taking my current medications?

The trial does not specify if you need to stop taking your current medications. However, it focuses on patients who have just started certain antibiotics, so it might be best to discuss your specific situation with the trial coordinators.

What prior data suggests that this decision support intervention is safe for patients with sepsis?

Research has shown that clinical decision support systems, like the one tested in this trial, are generally safe for hospital use. These systems assist doctors in making better decisions when treating sepsis, a serious infection. Although specific data on side effects is unavailable because this is not a drug or physical treatment, previous research suggests these systems can improve care without causing harm.

For instance, hospitals commonly use computerized decision support systems to detect sepsis early. They have not been linked to any known negative effects on patients. Instead, they assist doctors in selecting the right antibiotics, leading to better outcomes.

In summary, while the decision support system is neither a medicine nor a procedure, it is expected to be safe and help doctors treat patients more effectively without any known risks. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the clinical decision support algorithm for sepsis antibiotic selection because it introduces a personalized approach to treatment. Unlike standard care, where antibiotic selection is routine and based on general guidelines, this method uses a pharmacist-facilitated decision support system to tailor antibiotic choices to individual patients. This could lead to more effective and precise treatments, potentially improving outcomes for sepsis patients by reducing antibiotic resistance and ensuring quicker recovery.

What evidence suggests that this decision support intervention is effective for antibiotic selection in sepsis?

Research has shown that special computer programs can help doctors make better treatment decisions for sepsis, a serious infection. In this trial, one group will receive standard care without decision support, while another group will use a Clinical Decision Support Algorithm for Empiric Antibiotics in Sepsis. These programs help doctors spot sepsis early and choose the right antibiotics. By using these tools, doctors can predict sepsis more accurately and quickly decide on the best treatment plan. This approach ensures patients receive the most effective antibiotics for their specific needs, leading to better health outcomes. Evidence suggests that these tools can also reduce the unnecessary use of strong antibiotics while still effectively treating sepsis. ^{1 2 6 7 8}

Who Is on the Research Team?

Derek R Principal Investigator

Principal Investigator

The Ottawa Hospital Research Institute

Are You a Good Fit for This Trial?

This trial is for hospitalized adults over 18 years old with suspected sepsis who have had blood cultures ordered within 12 hours before or after starting antibiotics. It's not suitable for those allergic to the listed antibiotics like Vancomycin, Linezolid, and others.

Inclusion Criteria

I am currently admitted to a hospital.

My blood was tested for infection before or after starting antibiotics.

I am not allergic to common antibiotics like Vancomycin, Ciprofloxacin, or Gentamicin.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

1-2 weeks

Treatment

Implementation of a clinical decision support intervention for empiric antibiotic selection in sepsis

48 hours

Continuous monitoring during hospital stay

Follow-up

Participants are monitored for safety and effectiveness after treatment, including assessment of antibiotic de-escalation and other outcomes

90 days

Regular assessments during hospital stay and follow-up visits

What Are the Treatments Tested in This Trial?

Interventions

• Integrated Clinical Decision Support

Trial Overview

The study tests a clinical decision support algorithm designed to help doctors choose the right empiric antibiotic therapy for sepsis patients. The goal is to balance treatment effectiveness with antibiotic stewardship to prevent resistance.

How Is the Trial Designed?

Group I: Clinical Decision Support Algorithm for Empiric Antibiotics in Sepsis Experimental Treatment 1 Intervention

The planned intervention consists of a pharmacist-facilitated clinical decision support intervention, where pharmacists provide options and recommendations on empiric sepsis antibiotic selection to hospital providers.

Group II: Standard of Care Active Control 1 Intervention

Non-intervention group. No decision support is provided. Patient care is routine.

Integrated Clinical Decision Support is already approved in United States, European Union, Canada for the following indications:

Approved in United States as Merrem IV for:

• Complicated skin and skin structure infections
• Intra-abdominal infections
• Bacterial meningitis

Approved in European Union as Meropenem for:

• Severe pneumonia
• Urinary tract infections
• Complicated skin and soft tissue infections
• Intra-abdominal infections
• Gynaecological infections
• Septicaemia
• Bacterial meningitis

Approved in Canada as Meropenem for:

• Complicated skin and skin structure infections
• Intra-abdominal infections
• Bacterial meningitis

Who Is Running the Clinical Trial?

Ottawa Hospital Research Institute

Lead Sponsor

Trials

585

Recruited

3,283,000+

Canadian Institutes of Health Research (CIHR)

Collaborator

Trials

1,417

Recruited

26,550,000+

Published Research Related to This Trial

The two-stage clinical decision support system successfully identified 16% of hospitalized patients at risk for sepsis, achieving a sensitivity of 72% and a positive predictive value of 73% when compared to the gold standard of 'suspected infection'.

After alerts were triggered, bedside screenings by providers showed even better performance, with 81% sensitivity and 94% positive predictive value, indicating that the system effectively enhances early recognition and stratification of sepsis patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A two-stage clinical decision support system for early recognition and stratification of patients with sepsis: an observational cohort study. Amland, RC., Lyons, JJ., Greene, TL., et al.[2020]

A new graphical user interface (GUI) for a clinical decision support system (CDSS) was developed to help nurses manage patients on vasopressors in the ICU, and initial evaluations showed that nurses found it potentially useful and safe.

Despite overall enthusiasm, there was significant variability in user perceptions, with some nurses finding certain features helpful while others found the same features confusing, highlighting the need for tailored user experiences in clinical technology.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

End-user evaluation of an interface for clinical decision support using predictive algorithms. Kehoe, IE., Pepino, JA., Lee, J., et al.[2022]

The TREAT decision support system significantly improved the rate of appropriate antibiotic treatment in patients with suspected bacterial infections, achieving 70% compliance compared to 57% by physicians, while also using less broad-spectrum antibiotics and reducing costs by half.

In a randomized trial involving 2326 patients, intervention wards using TREAT had a higher rate of appropriate treatment (73% vs. 64%) and showed significant reductions in hospital stay length and total antibiotic costs, highlighting its efficacy in optimizing antibiotic use and minimizing resistance.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Improving empirical antibiotic treatment using TREAT, a computerized decision support system: cluster randomized trial. Paul, M., Andreassen, S., Tacconelli, E., et al.[2022]

The computerized antiinfectives-management program significantly reduced inappropriate drug orders, such as those for patients with reported allergies, and decreased excess drug dosages and antibiotic-susceptibility mismatches, leading to improved patient safety.

Patients treated with the recommended regimens from the program experienced lower costs for antiinfective agents and total hospital costs, as well as shorter hospital stays, demonstrating the program's efficacy in enhancing care while reducing expenses.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A computer-assisted management program for antibiotics and other antiinfective agents. Evans, RS., Pestotnik, SL., Classen, DC., et al.[2022]

Antibiotics are a leading cause of drug-related adverse events and medical malpractice claims, highlighting the need for improved prescribing practices.

The proposed clinical decision support system uses expert medical knowledge and advanced technologies to evaluate antibiotic treatments based on their toxicity risks, aiming to help physicians make safer prescribing decisions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Reducing the Toxicity Risk in Antibiotic Prescriptions by Combining Ontologies with a Multiple Criteria Decision Model. Souissi, SB., Abed, M., Elhiki, L., et al.[2023]

The implementation of a computer-assisted antibiotic-dose monitor at LDS Hospital significantly reduced the number of patients receiving excessive dosages of antibiotics from 50% to 44% during the intervention period, along with a decrease in the average duration of excessive dosing from 4.7 days to 2.9 days.

The intervention also led to a reduction in adverse drug events (ADEs) related to antibiotics, with only 14 ADEs (0.3%) reported during the intervention compared to 82 ADEs (0.9%) in the preintervention period, indicating improved safety in antibiotic administration.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of a computer-assisted antibiotic-dose monitor. Evans, RS., Pestotnik, SL., Classen, DC., et al.[2017]

Current mortality prediction models (MPMs) for sepsis do not adequately address patients classified as 'good,' highlighting a gap in tools available for clinicians to assess risk in this group.

Machine learning techniques using minimal medical records show promise in developing more efficient and user-friendly MPMs, which could enhance clinical decision-making and improve patient outcomes in sepsis management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Prognostic tools for elderly patients with sepsis: in search of new predictive models. Gamboa-Antiñolo, FM.[2021]

The implementation of a computerized decision support tool in an ICU led to a significant reduction in the use of broad-spectrum antibiotics, including carbapenems and third-generation cephalosporins, indicating improved antibiotic stewardship.

The tool also resulted in fewer mismatches between antibiotic susceptibility and initial therapy, promoting the use of narrower spectrum antibiotics and enhancing the overall effectiveness of antibiotic prescribing practices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Reduction of broad-spectrum antibiotic use with computerized decision support in an intensive care unit. Thursky, KA., Buising, KL., Bak, N., et al.[2016]

A clinical decision support system (CDSS) was developed to assist physicians in prescribing antibiotics, involving stakeholder engagement to ensure its relevance and effectiveness.

In a real-world test with 114 patients, adherence to CDSS recommendations was inconsistent, with 54 patients not following the advice, highlighting challenges in user compliance and system usability.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The development and implementation of a guideline-based clinical decision support system to improve empirical antibiotic prescribing. Akhloufi, H., van der Sijs, H., Melles, DC., et al.[2022]

A decision support tool for antibiotic therapy was implemented in the STRICU, leading to a significant reduction in antibiotic costs, averaging $87 less per patient compared to the previous year.

The tool was associated with a lower rate of adverse drug events (0.9% in the study group vs. 2.4% in the control group), indicating it may enhance safety in antibiotic prescribing, although the difference was not statistically significant.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A decision support tool for antibiotic therapy. Evans, RS., Classen, DC., Pestotnik, SL., et al.[2018]

This study aims to systematically review how machine learning tools for predicting adverse events in hospitals are designed and integrated into clinical practice, focusing on user interfaces and clinical workflows.

The review will analyze primary research articles from various databases, ensuring that only studies describing the implementation of patient deterioration surveillance tools are included, which will help understand the impact of these tools on clinician decision-making and patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review. Wan, YJ., Del Fiol, G., McFarland, MM., et al.[2022]

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frontiersin.org

frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.665464/full

Artificial Intelligence for Clinical Decision Support in Sepsis

AI-derived algorithms can be applied to multiple stages of sepsis, such as early prediction, prognosis assessment, mortality prediction, and optimal management.

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Evaluating the impact of a computerized surveillance algorithm and decision support system on sepsis mortality. Journal of the American Medical Informatics ...

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qualitysafety.bmj.com

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Electronic health record-based clinical decision support ...

An EHR-based severe sepsis alert did not result in a statistically significant improvement in several sepsis treatment performance measures.