Dataset Information

Antibiotic resistance, bacterial transmission and improved prediction of bacterial infection in patients with antibody deficiency.

ABSTRACT:

Background

Antibody-deficient patients are at high risk of respiratory tract infections. Many therefore receive antibiotic prophylaxis and have access to antibiotics for self-administration in the event of breakthrough infections, which may increase antimicrobial resistance (AMR).

Objectives

To understand AMR in the respiratory tract of patients with antibody deficiency.

Methods

Sputum samples were collected from antibody-deficient patients in a cross-sectional and prospective study; bacteriology culture, 16S rRNA profiling and PCR detecting macrolide resistance genes were performed. Bacterial isolates were identified using MALDI-TOF, antimicrobial susceptibility was determined by disc diffusion and WGS of selected isolates was done using Illumina NextSeq with analysis for resistome and potential cross-transmission. Neutrophil elastase was measured by a ProteaseTag immunoassay.

Results

Three hundred and forty-three bacterial isolates from sputum of 43 patients were tested. Macrolide and tetracycline resistance were common (82% and 35% of isolates). erm(B) and mef(A) were the most frequent determinants of macrolide resistance. WGS revealed viridans streptococci as the source of AMR genes, of which 23% also carried conjugative plasmids linked with AMR genes and other mobile genetic elements. Phylogenetic analysis of Haemophilus influenzae isolates suggested possible transmission between patients attending clinic.In the prospective study, a negative correlation between sputum neutrophil elastase concentration and Shannon entropy α-diversity (Spearman's ρ = -0.306, P = 0.005) and a positive relationship with Berger-Parker dominance index (ρ = 0.502, P < 0.001) were found. Similar relationships were noted for the change in elastase concentration between consecutive samples, increases in elastase associating with reduced α-diversity.

Conclusions

Measures to limit antibiotic usage and spread of AMR should be implemented in immunodeficiency clinics. Sputum neutrophil elastase may be a useful marker to guide use of antibiotics for respiratory infection.

SUBMITTER: Rofael S

PROVIDER: S-EPMC10720947 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Antibiotic resistance, bacterial transmission and improved prediction of bacterial infection in patients with antibody deficiency.

Rofael Sylvia S Leboreiro Babe Clara C Davrandi Mehmet M Kondratiuk Alexandra L AL Cleaver Leanne L Ahmed Naseem N Atkinson Claire C McHugh Timothy T Lowe David M DM

JAC-antimicrobial resistance 20231214 6

<h4>Background</h4>Antibody-deficient patients are at high risk of respiratory tract infections. Many therefore receive antibiotic prophylaxis and have access to antibiotics for self-administration in the event of breakthrough infections, which may increase antimicrobial resistance (AMR).<h4>Objectives</h4>To understand AMR in the respiratory tract of patients with antibody deficiency.<h4>Methods</h4>Sputum samples were collected from antibody-deficient patients in a cross-sectional and prospect ...[more]

PMID: 38098890

Similar Datasets

Project description:BackgroundThe early prediction of antibiotic resistance in patients with a urinary tract infection (UTI) is important to guide appropriate antibiotic therapy selection.ObjectiveIn this study, we aimed to predict antibiotic resistance in patients with a UTI. Additionally, we aimed to interpret the machine learning models we developed.MethodsThe electronic medical records of patients who were admitted to Yongin Severance Hospital, South Korea were used. A total of 71 features extracted from patients' admission, diagnosis, prescription, and microbiology records were used for classification. UTI pathogens were classified as either sensitive or resistant to cephalosporin, piperacillin-tazobactam (TZP), carbapenem, trimethoprim-sulfamethoxazole (TMP-SMX), and fluoroquinolone. To analyze how each variable contributed to the machine learning model's predictions of antibiotic resistance, we used the Shapley Additive Explanations method. Finally, a prototype machine learning-based clinical decision support system was proposed to provide clinicians the resistance probabilities for each antibiotic.ResultsThe data set included 3535, 737, 708, 1582, and 1365 samples for cephalosporin, TZP, TMP-SMX, fluoroquinolone, and carbapenem resistance prediction models, respectively. The area under the receiver operating characteristic curve values of the random forest models were 0.777 (95% CI 0.775-0.779), 0.864 (95% CI 0.862-0.867), 0.877 (95% CI 0.874-0.880), 0.881 (95% CI 0.879-0.882), and 0.884 (95% CI 0.884-0.885) in the training set and 0.638 (95% CI 0.635-0.642), 0.630 (95% CI 0.626-0.634), 0.665 (95% CI 0.659-0.671), 0.670 (95% CI 0.666-0.673), and 0.721 (95% CI 0.718-0.724) in the test set for predicting resistance to cephalosporin, TZP, carbapenem, TMP-SMX, and fluoroquinolone, respectively. The number of previous visits, first culture after admission, chronic lower respiratory diseases, administration of drugs before infection, and exposure time to these drugs were found to be important variables for predicting antibiotic resistance.ConclusionsThe study results demonstrated the potential of machine learning to predict antibiotic resistance in patients with a UTI. Machine learning can assist clinicians in making decisions regarding the selection of appropriate antibiotic therapy in patients with a UTI.

Dataset Information

Antibiotic resistance, bacterial transmission and improved prediction of bacterial infection in patients with antibody deficiency.

Background

Objectives

Methods

Results

Conclusions

Publications

Antibiotic resistance, bacterial transmission and improved prediction of bacterial infection in patients with antibody deficiency.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets