title: Prediction models for exacerbations in patients with COPD.
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 risk-stratified treatment to personalise COPD care. A more harmonised approach to develop and validate high- quality prediction models is needed to move personalised COPD medicine forward.
authors.affiliation: Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
authors.forename: Beniamino
authors.initials: B
authors.lastname: Guerra
authors.affiliation: Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
authors.forename: Violeta
authors.initials: V
authors.lastname: Gaveikaite
authors.affiliation: Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
authors.forename: Camilla
authors.initials: C
authors.lastname: Bianchi
authors.affiliation: Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
authors.forename: Milo A
authors.initials: MA
authors.lastname: Puhan
mesh_terms: D000893:Anti-Inflammatory Agents / Q000009:adverse effects; D001993:Bronchodilator Agents / Q000009:adverse effects; D016009:Chi-Square Distribution; D003661:Decision Support Techniques; D018450:Disease Progression; D006801:Humans; D016015:Logistic Models; D008168:Lung / Q000187:drug effects / Q000503:physiopathology*; D015999:Multivariate Analysis; D016017:Odds Ratio; D018579:Patient Selection; D057285:Precision Medicine; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D029424:Pulmonary Disease, Chronic Obstructive / Q000175:diagnosis* / Q000188:drug therapy / Q000503:physiopathology; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index
chemical_list: D000893:Anti-Inflammatory Agents; D001993:Bronchodilator Agents