Mortality associated with metabolic syndrome in people with COPD managed in primary care.

Objective: The prevalence of metabolic syndrome (MetS) has been reported to be higher in selected populations of people with COPD. The impact of MetS on mortality in COPD is unknown. We used routinely collected healthcare data to estimate the prevalence of MetS in people with COPD managed in primary care and determine its impact on 5-year mortality. Methods: Records from 103 955 patients with COPD from the Clinical Practice Research Datalink (CPRD-GOLD) between 2009 to 2017 were scrutinised. MetS was defined as the presence of three or more of: obesity, hypertension, lowered high-density lipoprotein cholesterol, elevated triglycerides or type 2 diabetes mellitus (T2DM). Univariate and multivariable Cox regression models were constructed to determine the prognostic impact of MetS on 5-year mortality. Similar univariate models were constructed for individual components of the definition of MetS. Results: The prevalence of MetS in the COPD cohort was 10.1%. Univariate analyses showed the presence of MetS increased mortality (hazard ratio (HR) 1.19, 95% CI: 1.12-1.27, p<0.001), but this risk was substantially attenuated in the multivariable analysis (HR 1.06, 95% CI: 0.99-1.13, p=0.085). The presence of hypertension (HR 1.70, 95% CI: 1.63-1.77, p<0.001) and T2DM (HR 1.41, 95% CI: 1.34-1.48, p<0.001) increased and obesity (HR 0.74, 95% CI: 0.71-0.78, p<0.001) reduced mortality risk. Conclusion: MetS in patients with COPD is associated with higher 5-year mortality, but this impact was minimal when adjusted for indices of COPD disease severity and other comorbidities. Individual components of the MetS definition exerted differential impacts on mortality suggesting limitation to the use of MetS as a multicomponent condition in predicting outcome in COPD.

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

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.