Dataset Information

Duration of antimicrobial therapy in community acquired pneumonia: less is more.

ABSTRACT: Community acquired pneumonia (CAP) represents the most common cause of infection-related morbidity and mortality worldwide. Appropriate treatment of CAP is challenging and sometimes limited by the availability to obtain rapid and timely identification of the etiologic agent in order to initiate or deescalate the correct antimicrobial therapy. As a consequence, prescribers frequently select empiric antimicrobial therapy using clinical judgment, local patterns of antimicrobial resistance, and, sometimes, individual patient expectations. These issues may contribute to prolonged courses of inappropriate therapy. In this review, we discuss the evidence and recommendations from international guidelines for the management of CAP and the clinical trials that specifically addressed duration of antimicrobial therapy for CAP in adults. In randomized controlled trials comparing the clinical efficacy of a short-course antimicrobial regimen versus an extended-course regimen, no differences in terms of clinical success, bacterial eradication, adverse events, and mortality were observed. The use of biomarkers, such as procalcitonin, to guide the initiation and duration of antimicrobial therapy may reduce total antibiotic exposure and treatment duration, healthcare costs, and the risk of developing antimicrobial resistance. In clinical practice, antimicrobial stewardship interventions may improve the management of CAP and may help in reducing treatment duration. Sometimes "less is more" in CAP.

SUBMITTER: Pinzone MR

PROVIDER: S-EPMC3918712 | biostudies-other | 2014

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:IntroductionCommunity-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP) are common complications in idiopathic inflammatory myopathy (IIM) patients, and are frequently associated with unfavorable outcome as well as prolonged antibiotic therapy. In this study, we intended to clarify whether clinical pulmonary infection score (CPIS) and multiple serum biomarkers are valuable in predicting unfavorable outcomes and prolonged antibiotic therapy in adult IIM patients complicated with CAP or HAP.MethodsData of IIM patients with CAP or HAP who were admitted to three tertiary centers from December 2010 to November 2019 were retrospectively collected. Cox proportional hazards regression analysis and logistic regression analysis were adopted to identify risk factors for unfavorable outcomes and prolonged antibiotic therapy in these patients. The predictive values of potential predictors were assessed using receiver operating characteristic analysis.ResultsThe mortality rate was 60.6% in 109 IIM patients complicated with CAP or HAP. Myositis Disease Activity Assessment Visual Analogue Scales (MYOACT) score, CPIS and timely adjustment to antibiotics based on drug susceptibility test (DST-based antibiotic) were significantly associated with long-term outcome in these patients. With an optimal cutoff value of 6.5 and area under the curve (AUC) of 0.813, CPIS was a more satisfying predictor compared with MYOACT score. The peak C-reactive protein (CRP) level, DST-based antibiotics, and complication of interstitial lung disease (ILD) were also significantly correlated with prolonged antibiotic therapy.ConclusionsIIM patients complicated with CAP or HAP frequently suffer from unfavorable outcomes. Compared with IIM disease activity, CPIS worked as a better predictor of outcome in these patients. Also, the peak CRP level during hospitalization might be valuable in predicting prolonged antibiotic therapy. The existence of ILD might impede early discontinuation of antibiotics. Timely adjustment to antibiotics based on drug susceptibility testing would decrease the mortality rate and reduce the incidence of prolonged antibiotic therapy.

Dataset Information

Duration of antimicrobial therapy in community acquired pneumonia: less is more.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets