A retrospective study of almost 39,000 patients shows that opportunities to diagnose chronic obstructive pulmonary disease (COPD) at an earlier stage are frequently being missed in both primary and secondary care in the U.K.
The findings, published online on 13 February in the Lancet Respiratory Medicine, found that the missed COPD diagnoses occurred in up to 85% of people.
Study author Dr. Rupert Jones from Plymouth University Peninsula said the substantial numbers of patients misdiagnosed and underdiagnosed is a cause for concern. Early diagnosis of COPD can lead to more effective treatment and reduce lung damage and improve quality of life and life expectancy.
The U.K. Department of Health estimates that around 2.2 million people with COPD in the nation are undiagnosed, and earlier diagnosis and treatment could save the National Health Service (NHS) more than 1 billion euros over 10 years.
Researchers identified 38,859 patients age 40 or older who had received a COPD diagnosis between 1990 and 2009 and for whom data was available for at least two years before and one year after diagnosis.
Results showed that in the five years before diagnosis, 85% of patients had visited their general practitioner or a secondary care clinic at least once with lower-respiratory symptoms.
Those consultations, the study noted, represented missed opportunities to further test patients for COPD. Opportunities for diagnosis were missed in 58% of patients in the six to 10 years before diagnosis and 42% in the 11 to 15 years before diagnosis.










![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)




