Rafiq, Meena; (2023) Identifying early signals of cancer from primary care electronic health records. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Most cancer patients are diagnosed after symptomatic presentation to primary care. Only half of these patients have ‘red flag’ symptoms with relatively high predictive value for cancer. The other half present with non-specific symptoms that are challenging to diagnose. These patients often experience convoluted care pathways and delayed diagnosis, associated with poorer prognosis. It remains unclear how far time-to-diagnosis could be brought forward, and what signals of underlying cancer exist in primary care electronic health records, particularly regarding blood test results, to support earlier cancer diagnosis. This thesis includes six studies using primary care data from the UK and Australia. Five studies examine pre-diagnostic time trends in different types of primary care healthcare use (consultations, prescriptions and requests for imaging and blood tests). I demonstrate that primary care activity increases several months before cancer diagnosis and that there is potential for expediting cancer diagnosis in some patients. I also evaluate blood test results during periods preceding cancer diagnosis, identifying increasing abnormalities in several acute phase reactants and red blood cell indices during such periods that can signal underlying cancer. Patterns of blood test abnormalities are different before lung and colorectal cancer diagnosis and abnormalities only increase in symptomatic patients with underlying cancer, compared to symptomatic patients without such diagnoses. The sixth study demonstrates the added information that can be derived from common blood tests in patients with two non-specific abdominal symptoms (abdominal pain and abdominal bloating) and their potential use in triaging symptomatic patients for further cancer assessment. Blood test results can re-classify patients with borderline pre-test risk of cancer (as typically encountered in patients with the two studied symptoms) to either a higher risk category, where further investigation is justified (if the blood test is abnormal), or expectant management (when no blood test abnormality is detected). The findings of this thesis can inform the development of enhanced cancer risk prediction tools combining information from presenting symptoms / blood test results included in primary care electronic records to support diagnostic decisions by General Practitioners. They can also enable the update of NICE guidelines for prioritising patients for urgent cancer referral to improve patient outcomes.

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