Evaluation of the Artificial Intelligence-based Prescription Support Software iAST® for the Choice of Empirical and Semi-targeted Antibiotic Treatment

NCT06174519 | Completed DMC

• 1 other identifier: EVIAST
• Study Type: observational
• Target: 325
• Locations: 1 country
• Sites: 1

Brief Summary

Inadequate treatment of infections frequently leads to complications that cause new visits to the doctor, lengthen hospital stays and can lead to sepsis, even causing the death of affected patients. Several scientific studies have documented that up to 20%-30% of antibiotic prescriptions are incorrect and do not cover the microorganism causing the infection. iAST® is a simple antibiotic prescribing aid tool that applies complex algorithms based on the latest artificial intelligence technologies to accurately predict the best specific antibiotic for a patient, before knowing the definitive microbiological results (bacterial identification and antibiogram). The objective of the present trial is to demonstrate the non-inferiority of iAST® with respect to physicians for the appropriate choice of empiric and semi-directed therapy of common infectious diseases, including sepsis, urinary tract infections and ventilator-associated pneumonias or tracheobronchitis. The adequacy of the medical prescription and the iAST® prediction will be compared taking the antibiogram report as a reference. The study design is retrospective, so that no intervention will be done on the patients. The investigators will conduct a retrospective search for infection cases and note the antibiotic treatment prescribed by the doctors. In parallel, they will enter basic patient data such as age, sex, service where they were treated, type of infection and microorganism (in the case of semi-directed treatment evaluation) into the iAST® software and will write down the first three treatment options recommended by the tool. The treatments of both arms (medical treatment and iAST® prediction) will be compared with the microbiological results and the success rate of each of them will be calculated.

Conditions

Bacterial Infections

Keywords

Sepsis; Pneumonia; Urinary tract infection; Bacterial infection; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• To demonstrate the non-inferiority of iAST® compared to physicians for the prescription of the empiric and semitargeted antibiotic therapy in patients with common infectious diseases.

  The appropriateness of antibiotic prescription and the iAST® prediction will be compared with the results from the antibiogram report as standard.Two-sided 95% confidence intervals (CIs) for the difference between treatments will be calculated using the unstratified method of Miettinen and Nurminen. The demonstration of non-inferiority of iAST® to doctor prescription for both primary and secondary efficacy endpoints will be established if the lower limit of the two-sided 95% CI for the treatment difference exceeded 5%. Additionally, a p-value will be computed for the corresponding one-sided non-inferiority hypothesis test.

  4 months

Secondary Outcomes (4)

• To assess the accuracy in the antibiotic prescription from the physicians and the software iAST® predictions (for empiric and semitargeted therapy) compared to the antibiogram report, respectively.

  4 months

• To evaluate the software iAST® accuracy in the antibiotic prediction of the 4 study population subgroups compared to the antibiogram report as standard.

  4 months

• To compare the rate of used/recommended antibiotics from the Access, Watch and Reserve antibiotics list (from the WHO Aware classification), between the prescriptions from physicians and the iAST® software predictions.

  4 months

• To collect information related to user experience by completing a usability questionnaire by physicians when working with the software iAST®.

  4 months

Study Arms (1)

Study group

For this clinical investigation, the clinical data for 325 subjects were used to demonstrate the non-inferiority of iAST® application in comparison with physician prescription. In any case, the data retrospectively analyzed for these 325 subjects were simulated using the iAST® application, in such a way that the same subjects were considered case and control at the same time.

Device: Medical device simulation

Interventions

Medical device simulation DEVICE

For the subjects included, investigators used the iAST® tool to predict which antibiotics would have been recommended as the top three choices both for empiric and semi-targeted therapy and recorded this data with the percentage of coverage predicted (the first three antibiotics in the iAST® ranking that have been tested in the antibiogram of the center where the study was carried were chosen). Investigators checked the final microbiological reports and logged if the recovered bacteria were susceptible to the drug prescribed by doctors and simulated by the iAST® tool according to the final antibiogram results.

Study group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Any patient who meets the inclusion criteria and admitted to any hospital in the HM Hospitales group from February 2023.

You may qualify if:

• Data for analysis should proceed from subjects over 18 years old that were admitted into HM Hospitals from 01Feb2023.
• Subjects who:
• have attended the Emergency Department of the hospital with suspected urinary tract infection (UTI) or;
• have presented an episode of bacteremia/sepsis at/during hospital admission or;
• have been admitted to the hospital ICU and presented a tracheobronchitis or pneumonia associated with mechanical ventilation or;
• have presented another type of infection, were treated and which have a bacterium identified with an antibiogram result.

You may not qualify if:

• Patients with concomitant infections.
• Data from subjects suffered from infections with no bacterial etiology: fungal or viral infections.
• Data from subjects with infections without microbiological documentation (including antibiogram results).
• Data from subjects prescribed with more than one antibiotic.

Sponsors & Collaborators

• Pragmatech AI Solutions: lead
• NAMSA: collaborator
• Grupo Hospital de Madrid: collaborator

Study Sites (1)

Grupo HM Hospitales

Madrid, 28015, Spain

Location

Related Publications (20)

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  PMID: 39194206 DERIVED

MeSH Terms

Conditions

Bacterial Infections; Sepsis; Pneumonia; Urinary Tract Infections

Condition Hierarchy (Ancestors)

Bacterial Infections and Mycoses; Infections; Systemic Inflammatory Response Syndrome; Inflammation; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Respiratory Tract Infections; Lung Diseases; Respiratory Tract Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Study Officials

• José Barberán, MD, PhD

  Grupo HM Hospitales

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 8, 2023
• First Posted: December 18, 2023
• Study Start: August 1, 2023
• Primary Completion: December 1, 2023
• Study Completion: December 1, 2023
• Last Updated: December 22, 2023

Record last verified: 2023-12

Locations

ES (1)