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.
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.
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%.
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.
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, the dates in these data may be shifted from their actual values, which might affect the validity of study conclusions.
A methodology for detecting the presence of shifted dates in RWD was developed by considering various approaches to confirming 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 were obtained from 71 U.S. healthcare data provider organizations (HCOs), members of the TriNetX global research network. Synthetic data were 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) are applied. These patterns were compared to 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.
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.
The presence of date shifting for U.S. HCOs may be reliably detected assessing whether routine exams most always occur on weekdays.
Abstract
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.
Lay Summary
This article describes a network—a series of interconnected data repositories—where clinical data about patients is stored after being extracted from electronic health record systems. The data on this network are meant to be used by researchers working in healthcare institutions as well as the life sciences industry. This network aims to make it easier, faster, and cheaper to find patients for recruitment into clinical trials and to conduct research using the clinical data. This network is being developed and maintained by a commercial company TriNetX, LLC. It is growing rapidly, expanding from 55 healthcare organizations and 7 countries in 2017 to over 220 healthcare organizations and 30 countries in 2022. The privacy and security of patient as well as member organizations’ data are of paramount concern. TriNetX takes a very conservative stand with respect to privacy protection and data governance. The data on this network have been used extensively for research and there’s currently over 350 peer-reviewed scientific publications based on the network’s data. The continued growth of the TriNetX network demonstrates that this approach to clinical data sharing is a safe, proven, and sustainable path for supporting the data needs of healthcare and life sciences researchers.
At his peak, about the time this collection was first published in 1910, Jack London was the highest-paid and perhaps the most popular living American writer.Lost Faceconsists of seven short works, ...including the title story and his finest and best-known short story, "To Build a Fire." Now in paperback for the first time, this collection appears as it was originally published.
Jack London grew up in poverty, educated himself through public libraries, and, in addition to writing, devoted his life to promoting socialism (although he eventually resigned from the Socialist Party). Despite his financial and critical success, in the end he succumbed to alcoholism and depression and died of a drug overdose. During the 1898 gold rush, London traveled to the Klondike to seek his fortune. It was this experience that had the most profound effect on his writing. Not only did he mine the far north environment for subject matter (and all the stories inLost Facetake place in the Yukon), but his laconic style drew upon its cold harshness and loneliness, where people and beasts had to work together or against each other for survival. London's stories are treasured for their insights into the psychology of both people and animals-particularly dogs-andLost Faceis a brilliant collection of some of the finest examples of London's craft.
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.
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.
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 (
> .10) in distribution for breast or colon cancers between 2019 and 2020.
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.
Many cancer interventional clinical trials are not completed because the required number of eligible patients are not enrolled.
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.
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.
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).
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.
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.
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