Writing today in the Lancet Respiratory Medicine, the researchers developed and tested the tool using the anonymised health records of over 19 million adults from across the UK.
To develop the new tool the researchers used two separate sets of health record data. Using the QResearch Database (which, in total, contains the anonymised health records of over 35 million patients, spanning all ethnicities and social groups across the UK) to identify 13 million people aged between 25 to 84 among whom 73,380 had a diagnosis of lung cancer. They then looked back through health records to identify common factors which might be used to statistically predict their risk of developing the cancer. Factors such as smoking, age, ethnicity, body mass index, medical conditions and social deprivation (and others) were considered as part of the analysis.
Once the tool was developed, it was tested using a separate set of anonymised GP health records – the Clinical Practice Research Datalink (CPRD). The team used the CPRD data (which contained data from an additional 2.54 million people’s anonymised health records) to see which people their new tool predicted were at the greatest risk of developing lung cancer, and then compared this to those who did go on to develop lung cancer.
The new CanPredict tool correctly identified more people who went on to develop lung cancer and was more sensitive than current recommended methods of predicting risk, across 5-, 6-, and 10-year forecasts.
Professor Julia Hippisley-Cox, senior author and Professor of Clinical Epidemiology and General Practice at the Nuffield Department of Primary Care Health Sciences, University of Oxford, said:
"Improving early diagnosis of lung cancer is incredibly important both for the NHS but especially for patients and their families. We hope that this new validated risk tool will help better prioritise patients for screening and ultimately help spot lung cancer earlier when treatments are more likely to help. We’d like to thank the many thousands of GPs who have shared anonymised data for research without whom this would not have been possible."
Read the full story on the Nuffield Department of Primary Care Health Sciences website.
This research is an output of the DART (The Integration and Analysis of Data using Artificial Intelligence to Improve Patient Outcomes with Thoracic Diseases) project. Led by Professor Fergus Gleeson and funded by UK Research and Innovation, Cancer Research UK and industry, DART is a programme of research focusing on accelerating pathways for the earlier diagnosis of lung cancer. Read more about the DART project.