Spirometric classifications of COPD severity as predictive markers for clinical outcomes: the HUNT Study

Abstract

Rationale GOLD grades based on percent-predicted FEV1 poorly predicts mortality. Studies have recommended alternative expressions of FEV1 for the classification of COPD severity and they warrant investigation. Objective To compare the predictive abilities of ppFEV1 (ppFEV1 quartiles, GOLD grades, ATS/ERS grades), FEV1 z-score (FEV1 z-score quartiles, FEV1 z-score grades), FEV1.Ht-2 (FEV1.Ht-2 quartiles, FEV1.Ht-2 grades), FEV1.Ht-3 (FEV1.Ht-3 quartiles), and FEV1Q (FEV1Q quartiles) to predict clinical outcomes. Methods People aged ≥40 years with COPD (n=890) who participated in the HUNT Study (1995-1997) were followed for 5 years (short-term) and up to 20.4 years (long-term). Survival analysis and time-dependent area under curve (AUC) were used to compare the predictive abilities. A regression tree approach was applied to obtain optimal cut-offs of different expressions of FEV1. The UK Biobank (n=6495) was used as a replication cohort with a 5-year follow-up. Measurements and Main Results As a continuous variable, FEV1Q had the highest AUCs for all-cause mortality (short-term 70.2, long-term 68.3), respiratory mortality (short-term 68.4, long-term 67.7), cardiovascular mortality (short-term 63.1, long-term 62.3), COPD hospitalization (short-term 71.3, long-term 70.9), and pneumonia hospitalization (short-term 67.8, long-term 66.6), followed by FEV1.Ht-2 or FEV1.Ht-3. Generally, similar results were observed for FEV1Q quartiles. The optimal cut-offs of FEV1Q had higher AUCs compared to GOLD grades for predicting short-term and long-term clinical outcomes. Similar results were found in UK Biobank. Conclusions FEV1Q best predicted the clinical outcomes and could improve the classification of COPD severity.

Spirometric Classifications of Chronic Obstructive Pulmonary Disease Severity as Predictive Markers for Clinical Outcomes: The HUNT Study

“…During the follow-up period, 146, 30, and 56 subjects died because of all causes, respiratory diseases, and cardiovascular diseases, respectively, and 172 and 96 were hospitalized because of COPD and pneumonia, respectively. At baseline, the average age of participants was 63.8 years, 6 of 10 participants were men, and more than half (53.3%) of participants were current smokers ( 15 ). A trend for increasing cumulative incidence of all-cause mortality with worsening categories of classifications of COPD severity was observed ( Figure 1 ).…”

“…We defined expressions of FEV 1 such as ppFEV 1 , FEV 1 z -score, FEV 1 ⋅ Ht −2 , FEV 1 ⋅ Ht −3 , and FEV 1 Q (described in detail in Reference 15 ) as suggested by the previous studies ( 1 – 3 , 5 , 6 , 8 , 9 , 16 ). The Global Lung Function Initiative 2012 reference equation was used to calculate ppFEV 1 , ppFVC, FEV 1 z -scores, and FVC z -scores ( 11 , 13 ).…”

“…Participants were followed for 5 years, and right-censoring events were emigration ( n = 3) or end of follow-up. Cause-specific mortality and hospitalizations were identified using International Classification of Diseases codes from medical records and are described in detail elsewhere ( 15 ).…”

Can spirometry improve the performance of cardiovascular risk model in high-risk Eastern European countries?

“…We have previously used both approaches in the same population ( 24 ), and they both performed similarly; therefore, only Z -transformed values were added to the model. Previous studies reported that FEV1 standardised with height might be a more reliable measure for predicting survival in the elderly population ( 56 , 59 – 61 ). We have previously also showed a strong dose–response relationship between lung function (defined as FEV1/height 3 ) and cardiovascular mortality in our recent study ( 39 ).…”