Risk factors for pneumonia caused by antimicrobial drug-resistant or drug-sensitive Acinetobacter baumannii infections: A retrospective study

Insertion Sequences within Oxacillinases Genes as Molecular Determinants of Acinetobacter baumannii Resistance to Carbapenems-A Pilot Study

Carbapenem-resistant Acinetobacter baumannii is one of the major problems among hospitalized patients. The presence of multiple virulence factors results in bacteria persistence in the hospital environment. It facilitates bacterial transmission between patients, causing various types of infections, mostly ventilator-associated pneumonia and wound and bloodstream infections. A. baumannii has a variable number of resistance mechanisms, but the most commonly produced are carbapenem-hydrolyzing class D β-lactamases (CHDLs). In our study, the presence of blaOXA-23, blaOXA-40 and blaOXA-51 genes was investigated among 88 clinical isolates of A. baumannii, including 53 (60.2%) strains resistant to both carbapenems (meropenem and imipenem) and 35 (39.8%) strains susceptible to at least meropenem. Among these bacteria, all the isolates carried the blaOXA-51 gene. The blaOXA-23 and blaOXA-40 genes were detected in two (5.7%) and three (8.6%) strains, respectively. Among the OXA-23 carbapenemase-producing A. baumannii strains (n = 55), insertion sequences (ISAba1) were detected upstream of the blaOXA-23 gene in fifty-two (94.5%) carbapenem-resistant and two (3.6%) meropenem-susceptible isolates. A. baumannii clinical strains from Poland have a similar antimicrobial resistance profile as those worldwide, with the presence of ISAba1 among blaOXA-23-positive isolates also being quite common. Carbapenem resistance among A. baumannii strains is associated with the presence of CHDLs, especially when insertion sequences are present.

A systematic review and meta-analysis for risk factor profiles in patients with resistant Acinetobacter baumannii infection relative to control patients

BACKGROUND

Acinetobacter baumannii is a major cause of nosocomial infections and high mortality rates. Evaluation of risk factors for such resistant infections may aid surveillance and diagnostic initiatives, as well as, can be crucial in early and appropriate antibiotic therapy.

OBJECTIVE

To identify the risk factors in patients with resistant A. baumannii infection with respect to controls.

METHODS

Prospective or retrospective cohort and case-control studies reporting the risk factors for resistant A. baumannii infection were collected through two data sources, MEDLINE/PubMed and OVID/Embase. Studies published in the English language were included while animal studies were excluded. The Newcastle-Ottawa Scale was used to assess the quality of studies. The odds ratio of developing antibiotic resistance in patients with A. baumannii infection was pooled using a random-effect model.

RESULTS

The results are based on 38 studies with 60878 participants (6394 cases and 54484 controls). A total of 28, 14, 25, and 11 risk factors were identified for multi-drug resistant (MDRAB), extensive-drug resistant (XDRAB), carbapenem-resistant (CRAB) and imipenem resistant A. baumannii infection (IRAB), respectively. In the MDRAB infection group, exposure to carbapenem (OR 5.51; 95% CI: 3.88-7.81) and tracheostomy (OR 5.01; 95% CI: 2.12-11.84) were identified with maximal pool odd's ratio. While previous use of amikacin (OR 4.94; 95% CI: 1.89-12.90) and exposure to carbapenem (OR 4.91; 95% CI: 2.65-9.10) were the foremost factors associated with developing CRAB infection. Further analysis revealed, mechanical ventilation (OR 7.21; 95% CI: 3.79-13.71) and ICU stay (OR 5.88; 95% CI: 3.27-10.57) as the most significant factors for XDRAB infection.

CONCLUSION

The exposure of carbapenem, amikacin (previous) and mechanical ventilation were the most significant risk factors for multidrug, extensive-drug, and carbapenem resistance in patients with A. baumannii infection respectively. These findings may guide to control and prevent resistant infections by identifying the patients at higher risk of developing resistance.

Facing Resistant Bacteria with Plant Essential Oils: Reviewing the Oregano Case

Antibiotic resistance is a serious global threat, and the misuse of antibiotics is considered its main cause. It is characterized by the expression of bacterial defense mechanisms, e.g., β-lactamases, expulsion pumps, and biofilm development. Acinetobacter baumannii and Pseudomonas aeruginosa are antibiotic-resistant species that cause high morbidity and mortality. Several alternatives are proposed to defeat antibiotic resistance, including antimicrobial peptides, bacteriophages, and plant compounds. Terpenes from different plant essential oils have proven antimicrobial action against pathogenic bacteria, and evidence is being generated about their effect against antibiotic-resistant species. That is the case for oregano essential oil (Lippia graveolens), whose antibacterial effect is widely attributed to carvacrol, its main component; however, minor constituents could have an important contribution. The analyzed evidence reveals that most antibacterial evaluations have been performed on single species; however, it is necessary to analyze their activity against multispecies systems. Hence, another alternative is using plant compounds to inactivate hydrolytic enzymes and biofilms to potentiate antibiotics' effects. Despite the promising results of plant terpenes, more extensive and deep mechanistic studies are needed involving antibiotic-resistant multispecies to understand their full potential against this problem.

Derivation and Validation of a Predictive Scoring Model of Infections Due to Acinetobacter baumannii in Patients with Hospital Acquired Pneumonia by Gram-Negative Bacilli

Background

The prognosis of ABA-HAP patients is very poor. This study aimed to develop a scoring model to predict ABA-HAP in patients with GNB-HAP.

Methods

A single center retrospective cohort study was performed among patients with HAP caused by GNB in our hospital during January 2019 to June 2019 (the derivation cohort, DC). The variables were assessed on the day when qualified respiratory specimens were obtained. A prediction score was formulated by using independent risk factors obtained from logistic regression analysis. It was prospectively validated with a subsequent cohort of GNB-HAP patients admitted to our hospital during July 2019 to Dec 2019 (the validation cohort, VC).

Results

The final logistic regression model of DC included the following variables: transferred from other hospitals (3 points); blood purification (3 points); risk for aspiration (4 points); immunocompromised (3 points); pulmonary interstitial fibrosis (3 points); pleural effusion (1 points); heart failure (3 points); encephalitis (5 points); increased monocyte count (2 points); and increased neutrophils count (2 points). The AUROC of the scoring model was 0.845 (95% CI, 0.796 ~ 0.895) in DC and 0.807 (95% CI, 0.759 ~ 0.856) in VC. The scoring model clearly differentiated the low-risk patients (the score < 8 points), moderate-risk patients (8 ≤ the score < 12 points) and high-risk patients (the score ≥ 12 points), both in DC (P < 0.001) and in VC (P < 0.001).

Conclusion

This simple scoring model could predict ABA-HAP with high predictive value and help clinicians to choose appropriate empirical antibiotic therapy.