Using Machine Learning To Define the Impact of Beta-Lactam Early and Cumulative Target Attainment on Outcomes in Intensive Care Unit Patients with Hospital-Acquired and Ventilator-Associated Pneumonia

Author:

Alshaer Mohammad H.12ORCID,Maranchick Nicole12,Bai Chen3,Maguigan Kelly L.4,Shoulders Bethany24ORCID,Felton Timothy W.56,Mathew Sumith K.7ORCID,Mardini Mamoun T.3ORCID,Peloquin Charles A.12ORCID

Affiliation:

1. Infectious Disease Pharmacokinetics Lab, Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA

2. Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida, USA

3. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA

4. Department of Pharmacy, UF Health Shands Hospital, Gainesville, Florida, USA

5. North West Ventilation Unit, Manchester University NHS Foundation Trust, Manchester, United Kingdom

6. Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom

7. Department of Pharmacology and Clinical Pharmacology, Christian Medical College, Vellore, Tamil Nadu, India

Abstract

Hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) are the most common intensive care unit (ICU) infections. We aimed to evaluate the association of early and cumulative beta-lactam pharmacokinetic/pharmacodynamic (PK/PD) parameters with therapy outcomes in pneumonia.

Publisher

American Society for Microbiology

Subject

Infectious Diseases,Pharmacology (medical),Pharmacology

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