scholarly journals Cancer risk algorithms in primary care: can they improve risk estimates and referral decisions?

2021 ◽  
Author(s):  
Olga Kostopoulou ◽  
Kavleen Arora ◽  
Bence Palfi
Keyword(s):  
Colorectal Cancer ◽  
Risk Assessment ◽  
Primary Care ◽  
Cancer Risk ◽  
Learning Effect ◽  
Learning Tools ◽  
Cancer Symptoms ◽  
Referral Decisions ◽  
Risk Estimates ◽  
Over Time

Background: Cancer risk calculators were introduced to clinical practice in the last decade, but they remain underused. We aimed to test their potential to improve risk assessment and 2-week-wait referral decisions. Methods: 157 GPs were presented with 23 vignettes describing patients with possible colorectal cancer symptoms. GPs gave their intuitive risk estimate and inclination to refer. They then saw the risk score of an algorithm (QCancer was not named) and could update their responses. Half of the sample was given information about the algorithm's derivation, validation, and accuracy. At the end, we measured their algorithm disposition. Results: GPs changed their inclination to refer 26% of the time and switched decisions entirely 3% of the time. Post-algorithm decisions improved significantly vis-a-vis the 3% NICE threshold (OR 1.45 [1.27, 1.65], p<.001). The algorithm's impact was greater where GPs had underestimated risk. GPs who received information about the algorithm had more positive disposition towards it. A learning effect was observed: GPs' intuitive risk estimates became better calibrated over time, i.e., moved closer to QCancer. Conclusions: Cancer risk calculators have the potential to improve 2-week-wait referral decisions. Their use as learning tools to improve intuitive risk estimates is promising and should be further investigated.

scholarly journals Using cancer risk algorithms to improve risk estimates and referral decisions

2022 ◽  
Vol 2 (1) ◽  
Author(s):  
Olga Kostopoulou ◽  
Kavleen Arora ◽  
Bence Pálfi
Keyword(s):  
Colorectal Cancer ◽  
Clinical Practice ◽  
Cancer Risk ◽  
General Practitioners ◽  
Learning Tools ◽  
Cancer Symptoms ◽  
Referral Decisions ◽  
Risk Estimates ◽  
Discernible Effect ◽  
Over Time

Abstract Background Cancer risk algorithms were introduced to clinical practice in the last decade, but they remain underused. We investigated whether General Practitioners (GPs) change their referral decisions in response to an unnamed algorithm, if decisions improve, and if changing decisions depends on having information about the algorithm and on whether GPs overestimated or underestimated risk. Methods 157 UK GPs were presented with 20 vignettes describing patients with possible colorectal cancer symptoms. GPs gave their risk estimates and inclination to refer. They then saw the risk score of an unnamed algorithm and could update their responses. Half of the sample was given information about the algorithm’s derivation, validation, and accuracy. At the end, we measured their algorithm disposition. We analysed the data using multilevel regressions with random intercepts by GP and vignette. Results We find that, after receiving the algorithm’s estimate, GPs’ inclination to refer changes 26% of the time and their decisions switch entirely 3% of the time. Decisions become more consistent with the NICE 3% referral threshold (OR 1.45 [1.27, 1.65], p < .001). The algorithm’s impact is greatest when GPs have underestimated risk. Information about the algorithm does not have a discernible effect on decisions but it results in a more positive GP disposition towards the algorithm. GPs’ risk estimates become better calibrated over time, i.e., move closer to the algorithm. Conclusions Cancer risk algorithms have the potential to improve cancer referral decisions. Their use as learning tools to improve risk estimates is promising and should be further investigated.

scholarly journals The CRISP-P study: feasibility of a self-completed colorectal cancer risk prediction tool in primary care

2019 ◽  
Vol 36 (6) ◽  
pp. 730-735 ◽  
Author(s):  
Elena C Harty ◽  
Jennifer G McIntosh ◽  
Adrian Bickerstaffe ◽  
Nadira Hewabandu ◽  
Jon D Emery
Keyword(s):  
Colorectal Cancer ◽  
Primary Care ◽  
Second Language ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Tertiary Education ◽  
Colorectal Cancer Risk ◽  
Prediction Tool ◽  
Recruitment Methods ◽  
Increased Risk

Abstract Objective Australia and New Zealand have the highest incidence of colorectal cancer (CRC) globally. Our research team has developed a CRC risk prediction tool for use in primary care to increase targeted screening. This study, Colorectal cancer RISk Prediction tool – patient (‘CRISP-P’), aimed to determine the following to inform a future trial design: (i) the feasibility of self-reporting; (ii) the feasibility of recruitment methods; and (iii) the prevalence of CRC risk. Methods Participants aged between 40 and 75 years were recruited consecutively from three primary care waiting rooms. Participants input data into CRISP on a tablet without receiving clinical advice. Feasibility was evaluated using recruitment rate, timely completion, a self-reported ‘ease-of-use’, score and field notes. Prevalence of CRC risk was calculated using the CRISP model. Results Five hundred sixty-one (90%) patients agreed to use the tool and 424 (84%) rated the tool easy to use. Despite this, 41% of people were unable to complete the questions without assistance. Patients who were older, without tertiary education or with English as their second language were more likely to require assistance (P < 0.001). Thirty-nine percent of patients were low risk, 58% at slightly increased and 2.4% were at moderately increased risk of developing colorectal cancer in the next 5 years. Conclusions The tool was perceived as easy to use, although older, less educated people, and patients with English as their second language needed help. The data support the recruitment methods but not the use of a self-completed tool for an efficacy trial.

scholarly journals Performance of the Gail and Tyrer-Cuzick breast cancer risk assessment models in women screened in a primary care setting with the FHS-7 questionnaire

2019 ◽  
Vol 42 (1 suppl 1) ◽  
pp. 232-237 ◽  
Author(s):  
Fernanda Sales Luiz Vianna ◽  
Juliana Giacomazzi ◽  
Cristina Brinckmann Oliveira Netto ◽  
Luciana Neves Nunes ◽  
Maira Caleffi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Primary Care ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Care Setting ◽  
Primary Care Setting ◽  
Cancer Risk Assessment ◽  
Risk Assessment Models ◽  
Breast Cancer Risk Assessment

scholarly journals Primary care physicians' use of family history for cancer risk assessment

2010 ◽  
Vol 11 (1) ◽  
Author(s):  
Brian S Flynn ◽  
Marie E Wood ◽  
Takamaru Ashikaga ◽  
Alan Stockdale ◽  
Greg S Dana ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Primary Care ◽  
Family History ◽  
Cancer Risk ◽  
Primary Care Physicians ◽  
Cancer Risk Assessment
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