Exploring Breast Cancer Systemic Drug Therapy Patterns in Real-World Data.

PURPOSE: To explore medications and their administration patterns in real-world patients with breast cancer. METHODS: A retrospective study was performed using TriNetX, a federated network of deidentified, Health Insurance Portability and Accountability Act-compliant data from 21 health care organizations across North America. Patients diagnosed with breast cancer between January 1, 2013, and May 31, 2022, were included. We investigated a rule-based and unsupervised learning algorithm to extract medications and their administration patterns. To group similar administration patterns, we used three features in k-means clustering: total number of administrations, median number of days between administrations, and standard deviation of the days between administrations. We explored the first three lines of therapy for patients classified into six groups on the basis of their stage at diagnosis (early as stages I-III v late as stage IV) and the sensitivity of the tumor's receptors to targeted therapies: hormone receptor-positive/human epidermal growth factor 2-negative (HR+/ERBB2-), ERBB2-positive (ERBB2+/HR±), or triple-negative (TN; HR-/ERBB2-). To add credence to the derived regimens, we compared them to the National Comprehensive Cancer Network (NCCN): Breast Cancer (version 2.2023) recommendations. RESULTS: In early-stage HR+/ERBB2- and TN groups, the most common regimens were (1) cyclophosphamide and docetaxel, administered once every 3 weeks for three to six cycles and (2) cyclophosphamide and doxorubicin, administered once every 2 weeks for four cycles, followed by paclitaxel administered once every week for 12 cycles. In the early-stage ERBB2+/HR± group, most patients were administered carboplatin and docetaxel with or without pertuzumab and with trastuzumab (for six or more cycles). Medications most commonly administered in our data set (7,798 patients) agreed with recommendations from the NCCN in terms of medications (regimens), number of administrations (cycles), and days between administrations (cycle length). CONCLUSION: Although there is a general agreement with the NCCN Guidelines, real-world medication data exhibit variability in the medications and their administration patterns.

A global federated real-world data and analytics platform for research.

Objective: This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research. Materials and Methods: TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities. Industry participants provide the financial resources to support, expand, and improve the technology platform in return for access to network data, which provides increased efficiencies in clinical trial design and deployment. Results: TriNetX is a growing global network, expanding from 55 HCOs and 7 countries in 2017 to over 220 HCOs and 30 countries in 2022. Over 19 000 sponsored clinical trial opportunities have been initiated through the TriNetX network. There have been over 350 peer-reviewed scientific publications based on the network's data. Conclusions: The continued growth of the TriNetX network and its yield of clinical trial collaborations and published studies indicates that this academic-industry structure is a safe, proven, sustainable path for building and maintaining research-centric data networks.