Abstract & Article Details
Research Article • Vol.5, Issue 6 • ISSN: 2766-2276 • Open Access • CC BY 4.0
The i2b2 Temporal Data-Mining Solution for Automated Reconstruction of Different Breast Cancer Patterns of Care over a Decade of Observation
Abstract
Background: In oncology, Patterns of Care (PoC) provide a detailed overview of all cancer-treatment interventions and recapitulate the entire patient’s journey. While manual-chart PoC reviewing is time-consuming, the use of Electronic Health Records (EHRs) enables the creation of data-mining solutions to automatically reconstruct the whole cancer trajectory with different granularity.
Methods: We tested the ability of the i2b2 (Informatics for Integrating Biology and Bedside) solution to support the automatic reconstruction of the PoC for consecutive and unselected HER2+ and TNBC breast cancer patients through a retrospective EHRs analysis over a decade of observations.
Results: From 2008 to 2017, 561 HER2+ and 412 TNBC patients were retrospectively identified by i2b2 platform at the Papa Giovanni XXIII Hospital in Bergamo. Most patients, 74.3% in the HER2 group and 71.8% in the TNBC group, received a (neo) adjuvant chemotherapy, with anti-HER2 drugs whenever indicated. Among the HER2 cohort, the 5-year Time to Treatment Change (TTC) and Overall Survival (OS) were 69.4% and 77.4% respectively, with 25% of patients receiving up to 3 lines of treatment in metastatic setting. Among the TNBC cohort, the 5-year TTC and OS were 59.3% and 69.4% respectively, with only 2% of patients receiving active treatment as third line of therapy. The consistency of the automated PoC reconstruction had to be reviewed in 1/3 of cases to clean conflicting data.
Conclusion: The i2b2 solution has the potential to provide a retrospective, automated reconstruction of the different PoC, with limited manual-chart review refinement and might contribute to support investigations in the field of real-world data.
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How to Cite
Article Information
| Journal | Journal of Biomedical Research & Environmental Sciences (JBRES) |
|---|---|
| ISSN | 2766-2276 |
| DOI | DOI 10.37871/jbres1935 |
| Volume / Issue | Vol. 5, Issue 6 |
| Received | June 14, 2024 |
| Accepted | June 21, 2024 |
| Published | June 21, 2024 |
| Article Type | Research Article |
| Pages | 630-639 |
| License | CC BY 4.0 — Open Access |
| Publisher | SciRes Literature LLC, Sheridan, WY, USA |
| Language | English |
Published under CC BY 4.0 — free to share, copy, adapt, and redistribute with attribution.