scholarly journals Treatment of Pseudomonas andStaphylococcusBronchopulmonary Infection in Patients with Cystic Fibrosis

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Rashmi Ranjan Das ◽  
Sushil Kumar Kabra ◽  
Meenu Singh
Keyword(s):  
Cystic Fibrosis ◽  
Day Treatment ◽  
Clinical Status ◽  
Additional Benefit ◽  
Antibiotic Regimen ◽  
Pulmonary Infections ◽  
Prior Use ◽  
Choice Of Treatment ◽  
Pseudomonas Infection ◽  
Oral Ciprofloxacin

The optimal antibiotic regimen is unclear in management of pulmonary infections due to pseudomonas andstaphylococcusin cystic fibrosis (CF). We systematically searched all the published literature that has considered the evidence for antimicrobial therapies in CF till June 2013. The key findings were as follows: inhaled antipseudomonal antibiotic improves lung function, and probably the safest/most effective therapy; antistaphylococcal antibiotic prophylaxis increases the risk of acquiringP. aeruginosa; azithromycin significantly improves respiratory function after 6 months of treatment; a 28-day treatment with aztreonam or tobramycin significantly improves respiratory symptoms and pulmonary function; aztreonam lysine might be superior to tobramycin inhaled solution in chronicP. aeruginosainfection; oral ciprofloxacin does not produce additional benefit in those with chronic persistent pseudomonas infection but may have a role in early or first infection. As it is difficult to establish a firm recommendation based on the available evidence, the following factors must be considered for the choice of treatment for each patient: antibiotic related (e.g., safety and efficacy and ease of administration/delivery) and patient related (e.g., age, clinical status, prior use of antibiotics, coinfection by other organisms, and associated comorbidities ones).

Study of efficacy, safety, and patient satisfaction with inhaled tobramycin (Tobramycin-Gobbi) in children with cystic fibrosis and pseudomonas infection

2021 ◽  
Vol 31 (2) ◽  
pp. 197-206
Author(s):  
E. I. Kondratyeva ◽  
A. Yu. Voronkova ◽  
S. V. Trishina ◽  
N. S. Snetkova ◽  
T. I. Safonova ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Patient Satisfaction ◽  
Respiratory Tract ◽  
Antibiotic Therapy ◽  
Chronic Infection ◽  
Day Treatment ◽  
Bacterial Count ◽  
Newly Diagnosed ◽  
Before And After ◽  
Pseudomonas Infection

Study of efficacy, safety, and patient satisfaction with inhaled tobramycin (Tobramycin-Gobbi) in children with cystic fibrosis and pseudomonas infectionAim of the study was to assess efficacy and safety of Tobramycin-Gobbi in CF, as well as the patients’ satisfaction with the treatment.Methods. 35 children from 6 to 18 years with CF were enrolled in this non-interventional prospective cohort multicenter study. All children had P. aeruginosa in the respiratory tract (newly diagnosed, recurrent, or chronic infection). The children received inhalation treatment with Tobramycin-Gobbi in the following cycles: 28-day treatment/28-day break, for 6 months. The studied parameters included respiratory function, bacterial cultures of the respiratory tract with a bacterial count, growth and body weight, antibiotic therapy for the respiratory episodes. The children and parents filled in a questionnaire “Treatment satisfaction assessment” and assessed their state of health on the visual-analog scale before and after each treatment cycle.Results. P. aeruginosa was eradicated in 17.7% of cases (6 patients, including 2 newly diagnosed, 3 recurrent infections, and 1 chronic infection), reduced bacterial count, decreased number of courses of antibiotic therapy, improvement of FEV1. Adverse reactions were reported by one patient.Conclusion. The efficacy, safety, and tolerability of Tobramicine Gobbi were confirmed in the patients with newly diagnosed, recurrent, and chronic infection caused by P. aeruginosa.

Cystic fibrosis. Biofilms involved in pulmonary infections

2013 ◽  
Vol 4 (2) ◽  
pp. 99-104
Author(s):  
Beata Sadowska ◽  
Marzena Więckowska-Szakiel ◽  
Małgorzata Paszkiewicz ◽  
Barbara Różalska
Keyword(s):  
Cystic Fibrosis ◽  
Pulmonary Infections

scholarly journals Inflammation in children with cystic fibrosis: contribution of bacterial production of long-chain fatty acids

2021 ◽  
Author(s):  
Erin Felton ◽  
Aszia Burrell ◽  
Hollis Chaney ◽  
Iman Sami ◽  
Anastassios C. Koumbourlis ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Fatty Acid ◽  
Antibiotic Treatment ◽  
Bacterial Production ◽  
Clinical Status ◽  
Achromobacter Xylosoxidans ◽  
List Type ◽  
Future Studies ◽  
Long Chain

Abstract Background Cystic fibrosis (CF) affects >70,000 people worldwide, yet the microbiologic trigger for pulmonary exacerbations (PExs) remains unknown. The objective of this study was to identify changes in bacterial metabolic pathways associated with clinical status. Methods Respiratory samples were collected at hospital admission for PEx, end of intravenous (IV) antibiotic treatment, and follow-up from 27 hospitalized children with CF. Bacterial DNA was extracted and shotgun DNA sequencing was performed. MetaPhlAn2 and HUMAnN2 were used to evaluate bacterial taxonomic and pathway relative abundance, while DESeq2 was used to evaluate differential abundance based on clinical status. Results The mean age of study participants was 10 years; 85% received combination IV antibiotic therapy (beta-lactam plus a second agent). Long-chain fatty acid (LCFA) biosynthesis pathways were upregulated in follow-up samples compared to end of treatment: gondoate (p = 0.012), oleate (p = 0.048), palmitoleate (p = 0.043), and pathways of fatty acid elongation (p = 0.012). Achromobacter xylosoxidans and Escherichia sp. were also more prevalent in follow-up compared to PEx (p < 0.001). Conclusions LCFAs may be associated with persistent infection of opportunistic pathogens. Future studies should more closely investigate the role of LCFA production by lung bacteria in the transition from baseline wellness to PEx in persons with CF. Impact Increased levels of LCFAs are found after IV antibiotic treatment in persons with CF. LCFAs have previously been associated with increased lung inflammation in asthma. This is the first report of LCFAs in the airway of persons with CF. This research provides support that bacterial production of LCFAs may be a contributor to inflammation in persons with CF. Future studies should evaluate LCFAs as predictors of future PExs.

Using chest CT scan and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis

2021 ◽  
pp. 2101344
Author(s):  
Alienor Campredon ◽  
Enzo Battistella ◽  
Clémence Martin ◽  
Isabelle Durieu ◽  
Laurent Mely ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cystic Fibrosis ◽  
Ct Scan ◽  
Structural Changes ◽  
Clinical Status ◽  
Chest Ct ◽  
Ct Scans ◽  
Wall Thickening ◽  
Unsupervised Machine Learning ◽  
One Year

ObjectivesLumacaftor-ivacaftor is a cystic fibrosis transmembrane conductance regulator (CFTR) modulator known to improve clinical status in people with cystic fibrosis (CF). This study aimed to assess lung structural changes after one year of lumacaftor-ivacaftor treatment, and to use unsupervised machine learning to identify morphological phenotypes of lung disease that are associated with response to lumacaftor-ivacaftor.MethodsAdolescents and adults with CF from the French multicenter real-world prospective observational study evaluating the first year of treatment with lumacaftor-ivacaftor were included if they had pretherapeutic and follow-up chest computed tomography (CT)-scans available. CT scans were visually scored using a modified Bhalla score. A k-mean clustering method was performed based on 120 radiomics features extracted from unenhanced pretherapeutic chest CT scans.ResultsA total of 283 patients were included. The Bhalla score significantly decreased after 1 year of lumacaftor-ivacaftor (−1.40±1.53 points compared with pretherapeutic CT; p<0.001). This finding was related to a significant decrease in mucus plugging (−0.35±0.62 points; p<0.001), bronchial wall thickening (−0.24±0.52 points; p<0.001) and parenchymal consolidations (−0.23±0.51 points; p<0.001). Cluster analysis identified 3 morphological clusters. Patients from cluster C were more likely to experience an increase in percent predicted forced expiratory volume in 1 sec (ppFEV1) ≥5 under lumacaftor–ivacaftor than those in the other clusters (54% of responders versus 32% and 33%; p=0.01).ConclusionOne year treatment with lumacaftor-ivacaftor was associated with a significant visual improvement of bronchial disease on chest CT. Radiomics features on pretherapeutic CT scan may help in predicting lung function response under lumacaftor-ivacaftor.

Cystic fibrosis pulmonary infections

The Lancet ◽  
1996 ◽  
Vol 348 (9024) ◽  
pp. 391
Author(s):  
Beryl J Rosenstein
Keyword(s):  
Cystic Fibrosis ◽  
Pulmonary Infections

scholarly journals Unsupervised Phenotypic Clustering for Determining Clinical Status in Children with Cystic Fibrosis

2021 ◽  
pp. 2002881
Author(s):  
Nicole Filipow ◽  
Gwyneth Davies ◽  
Eleanor Main ◽  
Neil J. Sebire ◽  
Colin Wallis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cystic Fibrosis ◽  
Lung Function ◽  
Disease Severity ◽  
Learning Algorithm ◽  
Severe Disease ◽  
Clinical Status ◽  
Oral Antibiotics ◽  
Multisystem Disease ◽  
Street Hospital

BackgroundCystic Fibrosis (CF) is a multisystem disease in which assessing disease severity based on lung function alone may not be appropriate. The aim of the study was to develop a comprehensive machine-learning algorithm to assess clinical status independent of lung function in children.MethodsA comprehensive prospectively collected clinical database (Toronto, Canada) was used to apply unsupervised cluster analysis. The defined clusters were then compared by current and future lung function, risk of future hospitalisation, and risk of future pulmonary exacerbation (PEx) treated with oral antibiotics. A K-Nearest Neighbours (KNN) algorithm was used to prospectively assign clusters. The methods were validated in a paediatric clinical CF dataset from Great Ormond Street Hospital (GOSH).ResultsThe optimal cluster model identified four (A-D) phenotypic clusters based on 12 200 encounters from 530 individuals. Two clusters (A,B) consistent with mild disease were identified with high FEV1, and low risk of both hospitalisation and PEx treated with oral antibiotics. Two clusters (C,D) consistent with severe disease were also identified with low FEV1. Cluster D had the shortest time to both hospitalisation and PEx treated with oral antibiotics. The outcomes were consistent in 3124 encounters from 171 children at GOSH. The KNN cluster allocation error rate was low, at 2.5% (Toronto), and 3.5% (GOSH).ConclusionMachine learning derived phenotypic clusters can predict disease severity independent of lung function and could be used in conjunction with functional measures to predict future disease trajectories in CF patients.

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