Predicting Future Health Risk in COPD: Differential Impact of Disease-Specific and Multi-Morbidity-Based Risk Stratification

Abstract

Objective

Multi-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.

Methods

Health 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.

Results

LR 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)).

Conclusion

Stratification 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.

Keywords:

• COPD
• UK primary care
• CPRD
• hospitalisation
• mortality
• multi-morbidity

Transparency Statement

The lead authors (DG and UK) affirm this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as originally planned (and, if relevant, registered) have been explained.

Data Sharing Statement

Data are available on request from the Clinical Practice Research Datalink (CPRD). All data accessed complies with relevant data protection and privacy regulations. Their provision requires the purchase of a license and our license does not permit us to make them publicly available to all. We used data from the version collected in January 2018 and have clearly specified the data selected in our Methods section. To allow identical data to be obtained by others, via the purchase of a license, we will provide the code lists on request. Licences are available from the CPRD (http://www.cprd.com): The Clinical Practice Research Datalink Group, The Medicines and Healthcare products Regulatory Agency, 10 South Colonnade, Canary Wharf, London E14 4PU.

Author Contributions

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work. David Groves and Urvee Karsanji are co-first authors.

Disclosure

JB and SS report personal fees from Johns Hopkins Health Solutions, outside the submitted work; JKQ reports grants and personal fees from AstraZeneca, grants and personal fees from Bayer, grants from Boehringer Ingelheim, grants from Chiesi, grants from Asthma UK, grants and personal fees from GSK, grants from MRC, grants from The Health Foundation, outside the submitted work; MS reports personal fees from GSK, non-financial support from Boehringer Ingelheim, outside the submitted work; NG reports grants, personal fees and non-financial support from GlaxoSmithKline, personal fees and non-financial support from Chiesi, personal fees and non-financial support from Boehringer Ingelheim, personal fees from AstraZeneca, outside the submitted work; RAE reports grants from CLARHC, during the conduct of the study; personal fees from Chiesi, personal fees from GSK, personal fees from TEVA, personal fees from BMJ, outside the submitted work.

The authors report no other conflicts of interest in this work.

Additional information

Funding

This work was funded by the National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (CLAHRC EM) and supported by NIHR Applied Research Collaboration East Midlands (ARC EM) and the Leicester Real World Evidence Unit. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.