# Identification of recurrences in women diagnosed with early invasive breast cancer using routinely collected data in England

## TL;DR

This study shows that routinely collected data in England can effectively identify breast cancer recurrences, offering a cost-effective way to monitor long-term outcomes.

## Contribution

A deterministic algorithm was developed and validated to identify breast cancer recurrences using routinely collected data in England.

## Key findings

- The algorithm detected distant recurrences with 95.6% sensitivity and any recurrence with 96.6% sensitivity.
- Specificity was 91.9% for distant recurrence and 77.7% for any recurrence.
- The algorithm could improve cost-effectiveness in long-term follow-up of breast cancer treatment trials.

## Abstract

Breast cancer is the commonest cancer in the UK, with around 55,000 women diagnosed annually. Information is routinely available on breast cancer mortality but not on recurrence.

We used a database compiled by the West Midlands Cancer Intelligence Unit during 1997–2011 to develop and train a deterministic algorithm to identify recurrences in routinely collected data (RCD) available within NHS England. We trained the algorithm further using 150 women with stage II-III breast cancer who were recruited into the AZURE trial during 2003–2006 and invited to approximately 24 clinic follow-up visits over ten years. We then evaluated its performance using data for the remaining 1930 women in England in the AZURE trial.

The sensitivity of the RCD to detect distant recurrences recorded in the AZURE trial during the ten years following randomisation was 95.6% and its sensitivity to detect any recurrence was 96.6%. The corresponding specificities were 91.9% for distant recurrence and 77.7% for any recurrence.

These findings demonstrate the potential of routinely collected data to identify breast cancer recurrences in England. The algorithm may have a role in several settings and make long-term follow-up in randomised trials of breast cancer treatments more cost-effective.