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Background <p>With continued improvements in breast cancer (BC) outcomes and risk of recurrence occurring until at least 20&#xa0;years post-diagnosis, it is important to continue to follow up clinical trial participants to characterise long-term treatment impact. Traditionally, follow-up has been via hospitals, entailing burden on patients and site staff. Using routinely collected health datasets (RCHD) as an alternative method is attractive, but historically, cancer recurrence is poorly recorded, unlike initial cancer diagnosis. Here we use data collected prospectively from large, multi-centre BC clinical trials to develop and test a procedure to identify recurrence within RCHD.</p> Methods <p>Data from four trials of early breast cancer (TACT2, POETIC, IMPORT-HIGH and FAST-Forward) where recurrence data has been collected prospectively (gold standard) was linked with RCHD (incl. cancer registry and hospital episode statistics; HES) managed by NHS England. The procedure identified episodes of clinical activity within RCHD to classify each event type (local and distant recurrence, second cancers, death) separately, then combined to derive time-to-recurrence (TTR), disease-free survival (iDFS) and overall survival (OS) outcomes. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Hazard ratios using Cox regression modelling, log-rank test <i>p</i>-values, and 3-year survival rates for the randomised treatments were reported separately for RCHD and trial data.</p> Results <p>The final procedure used Cancer Registry diagnoses to identify initial BCs for quality control purposes and second primary cancers. Deaths were identified via death dates and cause. Distant recurrence was identified predominantly by direct indicators of metastases (e.g. ICD10 codes C77X-79X). Local recurrence was identified via relevant surgeries’ OPCS4 codes. For TTR, iDFS and OS, agreement between study and RCHD events was reasonable. Specificity was good across all endpoints (range: 97.9–99.9% for three training datasets combined), as was NPV (range: 95.2–99.6%). Sensitivity and PPV were more variable, with sensitivity ranging between 72.9 and 97.2% and PPV ranging between 82.6 and 99.5%. Values were similar when considering the test dataset. Survival estimates for TTR, iDFS and OS were similar between study and RCHD data.</p> Conclusion <p>It is possible, with reasonable accuracy, to identify cancer recurrences using RCHD in the place of hospital-based data collection after the point of primary analysis.&#xa0;</p>

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Use of routinely collected health data (England) to identify subsequent disease-related events in patients with primary breast cancer: a practical alternative to hospital-based follow-up for breast cancer clinical trials

  • 期刊论文

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  • 发表日期：

    2025年9月26日

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  • Lucy S. Kilburn,
  • Victoria Hinder,
  • Sikhuphukile G. Mahati,
  • Judith M. Bliss

摘要

Background

With continued improvements in breast cancer (BC) outcomes and risk of recurrence occurring until at least 20 years post-diagnosis, it is important to continue to follow up clinical trial participants to characterise long-term treatment impact. Traditionally, follow-up has been via hospitals, entailing burden on patients and site staff. Using routinely collected health datasets (RCHD) as an alternative method is attractive, but historically, cancer recurrence is poorly recorded, unlike initial cancer diagnosis. Here we use data collected prospectively from large, multi-centre BC clinical trials to develop and test a procedure to identify recurrence within RCHD.

Methods

Data from four trials of early breast cancer (TACT2, POETIC, IMPORT-HIGH and FAST-Forward) where recurrence data has been collected prospectively (gold standard) was linked with RCHD (incl. cancer registry and hospital episode statistics; HES) managed by NHS England. The procedure identified episodes of clinical activity within RCHD to classify each event type (local and distant recurrence, second cancers, death) separately, then combined to derive time-to-recurrence (TTR), disease-free survival (iDFS) and overall survival (OS) outcomes. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Hazard ratios using Cox regression modelling, log-rank test p-values, and 3-year survival rates for the randomised treatments were reported separately for RCHD and trial data.

Results

The final procedure used Cancer Registry diagnoses to identify initial BCs for quality control purposes and second primary cancers. Deaths were identified via death dates and cause. Distant recurrence was identified predominantly by direct indicators of metastases (e.g. ICD10 codes C77X-79X). Local recurrence was identified via relevant surgeries’ OPCS4 codes. For TTR, iDFS and OS, agreement between study and RCHD events was reasonable. Specificity was good across all endpoints (range: 97.9–99.9% for three training datasets combined), as was NPV (range: 95.2–99.6%). Sensitivity and PPV were more variable, with sensitivity ranging between 72.9 and 97.2% and PPV ranging between 82.6 and 99.5%. Values were similar when considering the test dataset. Survival estimates for TTR, iDFS and OS were similar between study and RCHD data.

Conclusion

It is possible, with reasonable accuracy, to identify cancer recurrences using RCHD in the place of hospital-based data collection after the point of primary analysis.