Prediction models for exacerbations in patients with COPD

Beniamino Guerra, Violeta Gaveikaite, Camilla Bianchi, Milo A. Puhan

Research output: Contribution to journalReview article

Abstract

Personalised medicine aims to tailor medical decisions to the individual patient. A possible approach is to stratify patients according to the risk of adverse outcomes such as exacerbations in chronic obstructive pulmonary disease (COPD). Risk-stratified approaches are particularly attractive for drugs like inhaled corticosteroids or phosphodiesterase-4 inhibitors that reduce exacerbations but are associated with harms. However, it is currently not clear which models are best to predict exacerbations in patients with COPD. Therefore, our aim was to identify and critically appraise studies on models that predict exacerbations in COPD patients. Out of 1382 studies, 25 studies with 27 prediction models were included. The prediction models showed great heterogeneity in terms of number and type of predictors, time horizon, statistical methods and measures of prediction model performance. Only two out of 25 studies validated the developed model, and only one out of 27 models provided estimates of individual exacerbation risk, only three out of 27 prediction models used high-quality statistical approaches for model development and evaluation. Overall, none of the existing models fulfilled the requirements for riskstratified treatment to personalise COPD care. A more harmonised approach to develop and validate highquality prediction models is needed to move personalised COPD medicine forward.

Original languageEnglish (US)
Article number160061
JournalEuropean Respiratory Review
Volume26
Issue number143
DOIs
StatePublished - Jan 1 2017
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)
  • Pulmonary and Respiratory Medicine

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    Guerra, B., Gaveikaite, V., Bianchi, C., & Puhan, M. A. (2017). Prediction models for exacerbations in patients with COPD. European Respiratory Review, 26(143), [160061]. https://doi.org/10.1183/16000617.0061-2016