Validation and clinical discovery demonstration of breast cancer data from a real-world data extraction platform.

Objective: To validate and demonstrate the clinical discovery utility of a novel patient-mediated, medical record collection and data extraction platform developed to improve access and utilization of real-world clinical data.

Materials And Methods: Clinical variables were extracted from the medical records of 1011 consented patients with breast cancer. To validate the extracted data, case report forms completed using the structured data output of the platform were compared to manual chart review for 50 randomly-selected patients with metastatic breast cancer. To demonstrate the platform's clinical discovery utility, we identified 194 patients with early-stage clinical data who went on to develop distant metastases and utilized the platform-extracted data to assess associations between time to distant metastasis (TDM) and early-stage tumor histology, molecular type, and germline status.

Results: The platform-extracted data for the validation cohort had 97.6% precision (91.98%-100% by variable type) and 81.48% recall (58.15%-95.00% by variable type) compared to manual chart review. In our discovery cohort, the shortest TDM was significantly associated with metaplastic (739.0 days) and inflammatory histologies (1005.8 days), HR-/HER2- molecular types (1187.4 days), and positive status (1042.5 days) as compared to other histologies, molecular types, and negative status, respectively. Multivariable analyses did not produce statistically significant results.

Discussion: The precision and recall of platform-extracted clinical data are reported, although specificity could not be assessed. The data can generate clinically-relevant insights.

Conclusion: The structured real-world data produced by a novel patient-mediated, medical record-extraction platform are reliable and can power clinical discovery.