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Home › Conditions › Pseudomonas aeruginosa infection

Pseudomonas aeruginosa infection

Part of Pseudomonas Infection

4 published articles · Updated continuously

Clinical Trial Landscape

Clinical Trials for Pseudomonas aeruginosa infection

3 trials tracked for Pseudomonas aeruginosa infection: 3 in phase 3 or 4 and 2 with published results. The most-cited published study has 54 citations.

3Trials tracked
3Phase 3 & 4
0Recruiting
2With published results
Phase distribution
Phase 4 2 Phase 3 1
  1. Phase 3 Study to Evaluate Arikayce™ in CF Patients With Chronic Pseudomonas Aeruginosa Infections Completed · 54 cited
  2. Phase 4 Population Pharmacokinetics and Safety of Intravenous Ceftolozane/Tazobactam in Adult Cystic Fibrosis Patients Completed · 49 cited
  3. Phase 4 Population Pharmacokinetics of Prolonged Infusion Meropenem in Cystic Fibrosis (CF) Children Completed

Showing the 3 most-cited and recently-updated of 3 trials. Browse the full registry →

Trial data sourced from ClinicalTrials.gov. Counts describe the research landscape and are not a treatment recommendation. Informational only — not medical advice.

HCP Mode — summaries include clinical detail, trial data, and statistical outcomes.
Patient Mode — summaries use plain language, avoiding clinical jargon.
Ceftolozane-tazobactam shows comparable efficacy to ceftazidime-avibactam but lower risk of recurrent infection
Infectious Disease Meta-analysis
Ceftolozane-tazobactam shows comparable efficacy to ceftazidime-avibactam but lower risk of recurrent infection New analysis shows lower risk of repeat infections for patients
This systematic review and meta-analysis of 3,361 patients with drug-resistant Pseudomonas aeruginosa infections found no significant differ…
A new review of 3,361 patients found that one specific antibiotic may help prevent recurring infections in those with drug-resistant bacteri…
Aug 3, 2026
Bibliometric analysis reveals growing research on Pseudomonas aeruginosa biofilm resistance
Infectious Disease Sys. Review
Bibliometric analysis reveals growing research on Pseudomonas aeruginosa biofilm resistance Scientists reviewed thousands of papers about fighting tough bacteria infections in the lungs and wounds
A review of 6,537 publications shows steady research growth and emerging strategies against P. aeruginosa biofilm-mediated drug resistance.
A big look at medical papers shows more research on tough bacteria infections between 2014 and 2025. Doctors are now focusing on new ways to…
Frontiers Jun 1, 2026
Machine learning model aids early risk stratification for carbapenem-resistant Pseudomonas aeruginosa infection
Infectious Disease Cohort
Machine learning model aids early risk stratification for carbapenem-resistant Pseudomonas aeruginosa infection New tool helps identify resistant bacteria early in hospital patients
This retrospective cohort study evaluated a clinico-laboratory machine-learning model based on routine complete blood count parameters in pa…
A new tool uses routine blood tests to predict dangerous antibiotic resistance in hospital patients before final lab results arrive.
Frontiers Apr 17, 2026
Meta-analysis finds no mortality difference between CA and CT for drug-resistant P. aeruginosa infections
Infectious Disease Meta-analysis
Meta-analysis finds no mortality difference between CA and CT for drug-resistant P. aeruginosa infections Which antibiotic works better for dangerous drug-resistant infections?
A meta-analysis of 10 observational studies including 1409 patients with drug-resistant P.
Two powerful new antibiotics show equal survival rates for patients fighting dangerous, drug-resistant infections.
Apr 2, 2026

Questions about Pseudomonas aeruginosa infection

What are new research strategies for treating Pseudomonas aeruginosa infection in biofilms?

Research into Pseudomonas aeruginosa infections focuses on new antibiotic combinations, machine learning for early risk detection, and targeting virulence factors to overcome biofilm-mediated resistance.

Full answer →
Can machine learning help predict risks for carbapenem-resistant Pseudomonas aeruginosa infection?

Machine learning models can help identify patients at high risk for carbapenem-resistant Pseudomonas aeruginosa infections during the critical window before lab results are finalized.

Full answer →