Abstract
ObjectiveMulti-morbidity contributes to mortality and hospitalisation in COPD, but it is uncertain how this interacts with disease severity in risk prediction. We compared contributions of multi-morbidity and disease severity factors in modelling future health risk using UK primary care healthcare data.MethodsHealth records from 103,955 patients with COPD identified from the Clinical Practice Research Datalink were analysed. We compared area under the curve (AUC) statistics for logistic regression (LR) models incorporating disease indices with models incorporating categorised comorbidities. We also compared these models with performance of The John Hopkins Adjusted Clinical Groups® System (ACG) risk prediction algorithm.ResultsLR models predicting all-cause mortality outperformed models predicting hospitalisation. Mortality was best predicted by disease severity (AUC & 95% CI: 0.816 (0.805–0.827)) and prediction was enhanced only marginally by the addition of multi-morbidity indices (AUC & 95% CI: 0.829 (0.818–0.839)). The model combining disease severity and multi-morbidity indices was a better predictor of hospitalisation (AUC & 95% CI: 0.679 (0.672–0.686)). ACG-derived LR models outperformed conventional regression models for hospitalisation (AUC & 95% CI: 0.697 (0.690–0.704)) but not for mortality (AUC & 95% CI: 0.816 (0.805–0.827)).ConclusionStratification of future health risk in COPD can be undertaken using clinical and demographic data recorded in primary care, but the impact of disease severity and multi-morbidity varies depending on the choice of health outcome. A more comprehensive risk modelling algorithm such as ACG offers enhanced prediction for hospitalisation by incorporating a wider range of coded diagnoses.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International journal of chronic obstructive pulmonary disease
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.