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.

The Detection of Date Shifting in Real-World Data.

OBJECTIVES: Analysis of health care real-world data (RWD) provides an opportunity to observe the actual patient diagnostic, treatment, and outcome events. However, researchers should understand the possible limitations of RWD. In particular, the dates in these data may be shifted from their actual values, which might affect the validity of study conclusions. METHODS: A methodology for detecting the presence of shifted dates in RWD was developed by considering various approaches to confirm the expected occurrences of medical events, including unique temporal occurrences as well as recurring seasonal or weekday patterns in diagnoses or procedures. Diagnosis and procedure data was obtained from 71 U.S. health care data provider organizations (HCOs), members of the TriNetX global research network. Synthetic data was generated for various degrees of date shifting corresponding to the diagnoses and procedures studied, yielding the resulting patterns when various degrees of shifting (including no shift) were applied. These patterns were compared with those produced for each HCO to predict the presence and degree of date shifting. These predictions were compared with statements of date shifting by the originating HCOs to determine the predictive accuracy of the methods studied. RESULTS: Twenty-eight of the 71 HCOs analyzed were predicted by methodology and confirmed by their data providers to have shifted data. Likewise, 39 were predicted and confirmed to not have shifted data. With four HCOs, agreement between predicted and stated date shifting status was not obtained. The occurrence of routine medical exams, only happening during weekdays, for these U.S. HCOs was most predictive (0.92 correlation coefficient) of the presence or absence of date shifting. CONCLUSION: The presence of date shifting for U.S. HCOs may be reliably detected assessing whether the routine exams should always occur on weekdays.

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.

Evolving Effect of the COVID-19 Pandemic on Cancer-Related Encounters.

PURPOSE: This is an update to a previously published report characterizing the impact that efforts to control the COVID-19 pandemic have had on the normal course of cancer-related encounters. METHODS: Data were analyzed from 22 US health care organizations (members of the TriNetX global network) having relevant, up-to-date encounter data. Although the original study compared encounter data pre-COVID-19 (January-April 2019) with the corresponding months in 2020, this update considers data through April 2021. As before, cohorts were generated for all neoplasm patients (malignant, benign, in situ, and of unspecified behavior), all new incidence neoplasm patients, exclusively malignant neoplasm patients, and new incidence malignant neoplasm patients. Data on the initial cancer stage were available for calendar year 2020 from about one third of the study's organizations. RESULTS: Although COVID-19 cases fluctuated through 2021, newly diagnosed cancers closely paralleled the prepandemic base year 2019. Similarly, screening for breast, colorectal, and cervical cancers quickly recovered beginning in May 2020 to prepandemic numbers. Preliminary data for the initial cancer stage showed no significant difference (P > .10) in distribution for breast or colon cancers between 2019 and 2020. CONCLUSION: Although the number of COVID-19 cases fluctuated, the steep declines observed during March and April 2020 in screening for breast and colon cancer and patients with newly diagnosed cancer did not continue through the rest of 2020 and into April 2021. Screening and new incidence cancer numbers quickly rose compared with prepandemic levels. The concern that more patients with advanced-stage cancer would be seen in the months following the drastic dips of March-April 2020 was not realized as the major disruption to normal cancer care was limited to these 2 months.

Assessing real-world medication data completeness.

OBJECTIVE: Analysis of healthcare Real-World Data (RWD) provides an opportunity to observe actual patient diagnostic, treatment and outcomes events. However, researchers should understand the possible limitations of RWD. In particular, these data may be incomplete, which would affect the validity of study conclusions. MATERIALS AND METHODS: The completeness of medication RWD was investigated by analyzing the incidence of various diagnosis-medication couplets: the occurrence of a certain medication in the RWD for a patient having a certain diagnosis. Diagnosis and medication data were obtained from 61 U.S. medical data provider organizations, members of the TriNetX global research network. The number of patients having 22 diagnoses and expected medications were obtained at each institution, and the percent completion of each diagnosis-medication couplet calculated. The study hypothesis is that the degree of couplet completeness can serve as a proxy for overall completeness of medication data for a given organization. RESULTS: Five diagnosis-medication couplets were found to be reliable proxies, having at least a peak 87% observed completeness for the organizations studied: Type 1 diabetes mellitus and insulin; asthma and albuterol; congestive heart failure and diuretics; cardiovascular disease and aspirin; hypothyroidism and levothyroxine. DISCUSSION: These couplets were validated as reliable indicators by determining their status as standards of care. The degree to which patients with these five diagnoses had the specified associated medication was consistent within an organization data set. CONCLUSION: The overall degree of medication data completeness for an organization can be assessed by measuring the completeness of certain indicator diagnosis-medication couplets.

Effects of the COVID-19 Pandemic on Cancer-Related Patient Encounters.

PURPOSE: While there are studies under way to characterize the direct effects of the COVID-19 pandemic on the care of patients with cancer, there have been few quantitative reports of the impact that efforts to control the pandemic have had on the normal course of cancer diagnosis and treatment encounters. METHODS: We used the TriNetX platform to analyze 20 health care institutions that have relevant, up-to-date encounter data. Using this COVID and Cancer Research Network (CCRN), we compared cancer cohorts identified by querying encounter data pre-COVID (January 2019-April 2019) and current (January 2020-April 2020). Cohorts were generated for all patients with neoplasms (malignant, benign, in situ, and of unspecified behavior), with new incidence neoplasms (first encounter), with exclusively malignant neoplasms, and with new incidence malignant neoplasms. Data from a UK institution were similarly analyzed. Additional analyses were performed on patients with selected cancers, as well as on those having had cancer screening. RESULTS: Clear trends were identified that suggest a significant decline in all current cohorts explored, with April 2020 displaying the largest decrease in the number of patients with cancer having encounters. Of the cancer types analyzed, lung, colorectal, and hematologic cancer cohorts exhibited smaller decreases in size in April 2020 versus 2019 (-39.1%, -39.9%, -39.1%, respectively) compared with cohort size decreases for breast cancer, prostate cancer, and melanoma (-47.7%, -49.1%, -51.8%, respectively). In addition, cancer screenings declined drastically, with breast cancer screenings dropping by -89.2% and colorectal cancer screenings by -84.5%. CONCLUSION: Trends seen in the CCRN clearly suggest a significant decrease in all cancer-related patient encounters as a result of the pandemic. The steep decreases in cancer screening and patients with a new incidence of cancer suggest the possibility of a future increase in patients with later-stage cancer being seen initially as well as an increased demand for cancer screening procedures as delayed tests are rescheduled.

Design-phase prediction of potential cancer clinical trial accrual success using a research data mart.

BACKGROUND: Many cancer interventional clinical trials are not completed because the required number of eligible patients are not enrolled. OBJECTIVE: To assess the value of using a research data mart (RDM) during the design of cancer clinical trials as a predictor of potential patient accrual, so that less trials fail to meet enrollment requirements. MATERIALS AND METHODS: The eligibility criteria for 90 interventional cancer trials were translated into i2b2 RDM queries and cohort sizes obtained for the 2 years prior to the trial initiation. These RDM cohort numbers were compared to the trial accrual requirements, generating predictions of accrual success. These predictions were then compared to the actual accrual performance to evaluate the ability of this methodology to predict the trials' likelihood of enrolling sufficient patients. RESULTS: Our methodology predicted successful accrual (specificity) with 0.969 (=31/32 trials) accuracy (95% CI 0.908 to 1) and predicted failed accrual (sensitivity) with 0.397 (=23/58 trials) accuracy (95% CI 0.271 to 0.522). The positive predictive value, or precision rate, is 0.958 (=23/24) (95% CI 0.878 to 1). DISCUSSION: A prediction of 'failed accrual' by this methodology is very reliable, whereas a prediction of accrual success is less so, as causes of accrual failure other than an insufficient eligible patient pool are not considered. CONCLUSIONS: The application of this methodology to cancer clinical design would significantly improve cancer clinical research by reducing the costly efforts expended initiating trials that predictably will fail to meet accrual requirements.

Use case and application requirements for a protocol lifecycle tracking tool, with a focus on the trial initiation phase.

The time required to initiate clinical trials, from declaration of the investigator's intent to opening of the study for participant accrual, is cited as often being so long that clinical research is seriously impeded. Efforts to improve operational efficiency of trial initiation are confounded by the work flow complexity and the variations encountered with different types of trials and institutional environments. A computer Protocol Lifecycle Tracking (PLT) tool would enable study initiation staff to manage the process, and the various clinical research stakeholders to monitor the progress of a study's initiation, as well as obtain data on the work flow to identify those activities that are in need of operational efficiency improvement. The objective of our work was to develop use cases and system requirements for a PLT tool. The result of our study is a use case document that can serve as the specifications for developing a PLT application.

The automation of clinical trial serious adverse event reporting workflow.

BACKGROUND: The reporting of serious adverse events is a requirement when conducting a clinical trial involving human subjects, necessary for the protection of the participants. The reporting process is a multi-step procedure, involving a number of individuals from initiation to final review, and must be completed in a timely fashion. PURPOSE: The purpose of this project was to automate the adverse event reporting process, replacing paper-based processes with computer-based processes, so that personnel effort and time required for serious adverse event reporting was reduced, and the monitoring of reporting performance and adverse event characteristics was facilitated. METHODS: Use case analysis was employed to understand the reporting workflow and generate software requirements. The automation of the workflow was then implemented, employing computer databases, web-based forms, electronic signatures, and email communication. RESULTS: In the initial year (2007) of full deployment, 588 SAE reports were processed by the automated system, eSAEy. The median time from initiation to Principal Investigator electronic signature was <2 days (mean 7 +/- 0.7 days). This was a significant reduction from the prior paper-based system, which had a median time for signature of 24 days (mean of 45 +/- 5.7 days). With eSAEy, reports on adverse event characteristics (type, grade, etc.) were easily obtained and had consistent values based on standard terminologies.Limitation The automated system described was designed specifically for the workflow at Thomas Jefferson University. While the methodology for system design, and the system requirements derived from common clinical trials adverse reporting procedures are applicable in general, specific workflow details may not be relevant at other institutions. CONCLUSION: The system facilitated analysis of individual investigator reporting performance, as well as the aggregation and analysis of the nature of reported adverse events.

A novel cross-disciplinary multi-institute approach to translational cancer research: lessons learned from Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC).

BACKGROUND: The Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC, http://www.pcabc.upmc.edu) is one of the first major project-based initiatives stemming from the Pennsylvania Cancer Alliance that was funded for four years by the Department of Health of the Commonwealth of Pennsylvania. The objective of this was to initiate a prototype biorepository and bioinformatics infrastructure with a robust data warehouse by developing a statewide data model (1) for bioinformatics and a repository of serum and tissue samples; (2) a data model for biomarker data storage; and (3) a public access website for disseminating research results and bioinformatics tools. The members of the Consortium cooperate closely, exploring the opportunity for sharing clinical, genomic and other bioinformatics data on patient samples in oncology, for the purpose of developing collaborative research programs across cancer research institutions in Pennsylvania. The Consortium's intention was to establish a virtual repository of many clinical specimens residing in various centers across the state, in order to make them available for research. One of our primary goals was to facilitate the identification of cancer-specific biomarkers and encourage collaborative research efforts among the participating centers. METHODS: The PCABC has developed unique partnerships so that every region of the state can effectively contribute and participate. It includes over 80 individuals from 14 organizations, and plans to expand to partners outside the State. This has created a network of researchers, clinicians, bioinformaticians, cancer registrars, program directors, and executives from academic and community health systems, as well as external corporate partners - all working together to accomplish a common mission. The various sub-committees have developed a common IRB protocol template, common data elements for standardizing data collections for three organ sites, intellectual property/tech transfer agreements, and material transfer agreements that have been approved by each of the member institutions. This was the foundational work that has led to the development of a centralized data warehouse that has met each of the institutions' IRB/HIPAA standards. RESULTS: Currently, this "virtual biorepository" has over 58,000 annotated samples from 11,467 cancer patients available for research purposes. The clinical annotation of tissue samples is either done manually over the internet or semi-automated batch modes through mapping of local data elements with PCABC common data elements. The database currently holds information on 7188 cases (associated with 9278 specimens and 46,666 annotated blocks and blood samples) of prostate cancer, 2736 cases (associated with 3796 specimens and 9336 annotated blocks and blood samples) of breast cancer and 1543 cases (including 1334 specimens and 2671 annotated blocks and blood samples) of melanoma. These numbers continue to grow, and plans to integrate new tumor sites are in progress. Furthermore, the group has also developed a central web-based tool that allows investigators to share their translational (genomics/proteomics) experiment data on research evaluating potential biomarkers via a central location on the Consortium's web site. CONCLUSIONS: The technological achievements and the statewide informatics infrastructure that have been established by the Consortium will enable robust and efficient studies of biomarkers and their relevance to the clinical course of cancer. Studies resulting from the creation of the Consortium may allow for better classification of cancer types, more accurate assessment of disease prognosis, a better ability to identify the most appropriate individuals for clinical trial participation, and better surrogate markers of disease progression and/or response to therapy.