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Molecular Diagnosis & Therapy

, Volume 18, Issue 4, pp 381–388 | Cite as

Defining Phenotypes in COPD: An Aid to Personalized Healthcare

  • Andrea Segreti
  • Emanuele Stirpe
  • Paola Rogliani
  • Mario Cazzola
Review Article

Abstract

The diagnosis of chronic obstructive pulmonary disease (COPD) is based on a post-bronchodilator fixed forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) <70 % ratio and the presence of symptoms such as shortness of breath and productive cough. Despite the simplicity in making a diagnosis of COPD, this morbid condition is very heterogeneous, and at least three different phenotypes can be recognized: the exacerbator, the emphysema–hyperinflation and the overlap COPD–asthma. These subgroups show different clinical and radiological features. It has been speculated that there is an enormous variability in the response to drugs among the COPD phenotypes, and it is expected that subjects with the same phenotype will have a similar response to each specific treatment. We believe that phenotyping COPD patients would be very useful to predict the response to a treatment and the progression of the disease. This personalized approach allows identification of the right treatment for each COPD patient, and at the same time, leads to improvement in the effectiveness of therapies, avoidance of treatments not indicated, and reduction in the onset of adverse effects. The objective of the present review is to report the current knowledge about different COPD phenotypes, focusing on specific treatments for each subgroup. However, at present, COPD phenotypes have not been studied by randomized clinical trials and therefore we hope that well designed studies will focus on this topic.

Keywords

Chronic Obstructive Pulmonary Disease Chronic Obstructive Pulmonary Disease Patient Tiotropium Chronic Obstructive Pulmonary Disease Exacerbation Roflumilast 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements and Disclosures

None.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andrea Segreti
    • 1
  • Emanuele Stirpe
    • 1
  • Paola Rogliani
    • 1
  • Mario Cazzola
    • 1
  1. 1.Unit of Respiratory Medicine, Department of System MedicineUniversity of Rome Tor VergataRomeItaly

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