Development of a primary care head and neck risk assessment tool (PhD)
Start Date Aug 2017
Code L3-Aff, PhD
Less prevalent cancers, like head and neck cancer, by their rare nature are more difficult to predict because the symptoms with which a patient presents are common and not sensitive nor specific for diagnosis. This study is to inform future work to develop and trial a clinical risk assessment tool for use in primary care for patients who present with symptoms suspicious of a head and neck cancer. As yet, no tool has been developed for head and neck symptoms from primary care data, although a calculator has been generated using secondary care data that is currently being externally and prospectively validated. The paucity of positive predictive values for symptoms of Head and Neck Cancer is an area which requires development.
The aim is to evaluate the stakeholders’ understanding and acceptance of existing risk assessment tools and the development of a new tool. Additionally, the study aims to provide some statistical modelling to aid the development of a tool for future triage of referrals from primary to secondary care. The combination of evidence from both primary and secondary care can only contribute to this valuable body of work and further improve the patient pathway and the early diagnosis of Head and Neck Cancer.
£2,000 from Royal College of General Practitioners, Scientific Foundation Board, Practitioners Allowance Grant for Public and Patient Involvement and transcription costs.
£7,000 from Oracle Cancer Trust plus £3,000 from British Association of Head and Neck Oncologists for database.
Aims & objectives
To qualitatively assess perceptions of a cancer risk assessment tool for head and neck cancer by general practitioners, head and neck cancer surgeons and patients and explore views on a new clinical tool to help direct the route of referral for ear nose and throat symptoms particularly those suspicious for head and neck cancer.
Use existing patient records to undertake quantitative predictive modelling of ear nose and throat symptoms to provide some predictive values for symptoms, demographics, comorbidities or combinations of variables which are more suspicious and therefore predictive of an eventual diagnosis of a head and neck cancer.
A qualitative study to explore clinician (GP and Head and Neck Surgeons), patient and public attitudes to current and trialled risk assessment tools in use in practice in primary care, assess acceptability of a potential head and neck risk assessment tool and explore barriers and facilitators to introduction and use of a head and neck risk assessment tool in the primary care setting.
A quantitative study to statistically model head and neck symptoms, demographic and comorbidity patient information from a primary care electronic patient record database to provide predictive values for a future head and neck cancer diagnosis.
Outputs & impact
The positive predictive values modelled from the data will provide the called for evidence to support and influence any changes in National Institute of Clinical Excellence head and neck cancer referral guidelines.
Impact for patients will be evaluated by adjustments in the referral pathway, the tools’ validity, acceptance to both clinicians and patients/public and its trial, use and implementation in clinical practice.
Recruitment of clinicians for one to one interviews has begun, ethics (Integrated Research Application System) application to recruit head and neck cancer patients for interview is planned after which patients will be recruited and interviewed. Following interviews qualitative analysis of data will commence.
£3,000 has been granted by the British Association of Head and Neck Oncologists and a further grant has been awarded of £7,000 from Oracle Cancer Trust to purchase a database of electronic patient record information from The Health Information Network via The University of Birmingham. Once the database is purchased there will be a period of data cleaning, the variables to be modelled will be selected and statistical modelling will be applied.