- Divo MJ et al. A Simple, Low cost, and ease of IMplementation (SLIM) risk calculator. Eur Respir J 2023.
The problem is that, in addition to annual spirometry, doctors would have to carry out chest CT examinations and measurements of diffusion capacity in order to identify potential patients at risk. This is not only time-consuming and resource-intensive, but also simply not possible, especially in countries with limited healthcare systems. For this reason, the GOLD (Global Initiative for Chronic Obstructive Lung Disease), among others, has been endeavouring for some time to identify those affected at earlier stages of the disease. But how?
Miguel Divo and his team may now have found an answer to this question. For their study (DOI: 10.1183/13993003.00806-2023), they recruited almost 700 people from an existing observation cohort who had at least 15 pack years (py = pack years = smoking years × number of packs smoked per day > 1 pack per day × 15 years = 15 pack years), but who had unremarkable spirometry values at the start of the study and in the following three years (FEV1/FVC ≥ 0.7 and FEV1 ≥ 80 per cent). They were observed for a total of 6 years and received an average of 5 spirometry measurements.
During this period, 110 people (16 per cent) developed an incipient airway obstruction, 15 (2 per cent) a restriction according to the so-called PRISm classification (Preserved Ratio Impaired Spirometry: FEV1/FVC ≥ 0.70 and FEV1 < 80 per cent). 63 (9 per cent) showed an "unstable" pattern, and 489 (72 per cent) retained normal spirometry.
The researchers derived four predictors that increase the risk of chronic obstructive pulmonary disease:
In particular, an FEV1/FVC ratio between 0.70 and 0.75 correlated strongly with the development of COPD. If all criteria were met at the same time, the risk increased to 85 per cent over a period of 6 years - compared to just 2 per cent for those without all predictors.
The prediction model was externally validated on subjects from another COPD study and showed similar results there. It achieved a sensitivity of 0.72 and a specificity of 0.85.
The authors see the prediction model as a practical and economical clinical tool for the prognosis of smokers. This would enable doctors and researchers to identify people at risk earlier and, in the best case scenario, initiate effective disease-modifying interventions right at the start of the disease.