The NHS currently uses prediction algorithms, such as the QCancer scores, to combine relevant information from patient data and identify individuals deemed at high risk of having a currently undiagnosed cancer for further investigation.
Research by Professor Julia Hippisley-Cox and team (formerly at the Nuffield Department of Primary Care and Health Sciences, University of Oxford; now at Queen Mary University of London) used the anonymised electronic health records from over 7.4 million adults in England to create two new algorithms which are much more sensitive than existing models, and which could lead to better clinical decision making and potentially earlier diagnosis of cancer. The research, published today in Nature Communications, showed that the new models could improve how cancer is detected in primary care, and make it easier for patients to get a diagnosis and treatment at much earlier stages.
The algorithms intergrate information about a patient’s age, family history, medical diagnoses, symptoms, and general health, with the results of seven routine blood tests (which measure a person’s full blood count and test liver function) to improve predictive capabilities beyond the existing QCancer risk score.
Professor Julia Hippisley-Cox, now the Professor of Clinical Epidemiology and Predictive Medicine at Queen Mary University of London, and lead author of the study, said: “These algorithms are designed to be embedded into clinical systems and used during routine GP consultations. They offer a substantial improvement over current models, with higher accuracy in identifying cancers — especially at early, more treatable stages. They use existing blood test results which are already in the patients’ records making this an affordable and efficient approach to help the NHS meet its targets to improve its record on diagnosing cancer early by 2028.”