Abstract 1312
Background
Predictive online calculators are used by clinicians as decision aids in early breast cancer (EBC). While use statistics for these calculators have not been published, as of 2017 NHS Predict was being accessed more than 20,000 times a month. These predictive tools have not had accuracy & benefit of use prospectively confirmed in EBC, yet use of calculators has been encouraged in EBC guidelines. It is important to understand the populations informing model development & validation, to understand how data bias may impact predictions in under-represented subpopulations. This work sought to elucidate the risk of bias in model development & validation for 3 online EBC calculators (NHS Predict, Adjuvant! & Cancermath), in an effort to highlight sub-populations where calculated risk & therefore treatment benefit estimates may be less reliable.
Methods
A literature search was conducted in PubMed, search terms were “predict*” “adjuvant” “breast” & “algorithm”. Results were screened for relevance to the three predictive tools under scrutiny & additional references were extracted from relevant papers. Using a modified CHARMS checklist, the relevant sections of the development & validation papers were extracted.
Results
6 development & 24 validation papers were reviewed as summarised in the TableTable:
264P
| Predict | Adjuvant | Cancermath | |
|---|---|---|---|
| Development population size & date range | 5694 1977-2008 | 37,968 1977-2007 | 499,724 1977-2007 |
| Aged <35 in development population | 2% (111) | 0 | >0.5% |
| Aged >65 in development population | 32% (1781) | 0 | >17% |
| Tumour size >5cm in development population | 5% (287) | 0 | 0 |
| Number of validation studies | 10 | 13 | 3 |
| % retrospective | 100 | 100 | 100 |
| Total number of patients in validation studies | 19,864 | 19,618 | 11,203 |
| Age >65 in validations | 35% (7134) | 42% (8313) | 40% (4519) |
| Age <35 in validations | 16% (3235) | 8% (1518) | 9% (1007) |
| Tumour size >5cm in validations | 5% (287) | 5% (1015) | 6% (634) |
| Universal exclusions | Multi-focal, inflammatory, male | Multi-focal, inflammatory, male | Multi-focal, inflammatory, male |
| Neoadjuvant chemotherapy not an exclusion | 1 study (121 patients) | 0 | 0 |
| Overall conclusions of validation authors | Earlier versions under-predicted mortality in women <35 Poor performance in tumours >5cm. | Poor performance in general in: <35 and >65 More advanced disease Malay ethnicity Overly optimistic survival predictions across subgroups in UK population. | Poor performance in < 35 Systematically under-predicted mortality, especially for ER-negative tumours. |
Conclusions
All 3 predictive tools have under-represented groups in their development cohorts, specifically those under 35 & over 65 years old, as well as larger tumours. Validation studies consistently demonstrate worse performance in these groups. However, due to inconsistent methodology in validation studies, quantitating the summary performance within & across tools is difficult. These predictive tools should be used with caution in under-represented populations. More work is required to look at clinical utility of tools as well as their statistical performance.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.
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