A Curated Cancer Clinical Outcomes Database (C3OD) for accelerating patient recruitment in cancer clinical trials

The usage of population and disease registries as pre-screening tools for clinical trials, a systematic review

OBJECTIVE

This systematic review aims to outline the use of population and disease registries for clinical trial pre-screening.

MATERIALS AND METHODS

The search was conducted in the time period of January 2014 to December 2022 in three databases: MEDLINE, Embase, and Web of Science Core Collection. References were screened using the Rayyan software, firstly based on titles and abstracts only, and secondly through full text review. Quality of the included studies was assessed using the List of Included Studies and quality Assurance in Review tool, enabling inclusion of publications of only moderate to high quality.

RESULTS

The search originally identified 1430 citations, but only 24 studies were included, reporting the use of population and/or disease registries for trial pre-screening. Nine disease domains were represented, with 54% of studies using registries based in the USA, and 62.5% of the studies using national registries. Half of the studies reported usage for drug trials, and over 478,679 patients were identified through registries in this review. Main advantages of the pre-screening methodology were reduced financial burden and time reduction.

DISCUSSION AND CONCLUSION

The use of registries for trial pre-screening increases reproducibility of the pre-screening process across trials and sites, allowing for implementation and improvement of a quality assurance process. Pre-screening strategies seem under-reported, and we encourage more trials to use and describe their pre-screening processes, as there is a need for standardized methodological guidelines.

Using Bayesian hierarchical modeling for performance evaluation of clinical trial accrual for a cancer center

Introduction

Slow patient accrual in cancer clinical trials is always a concern. In 2021, the University of Kansas Comprehensive Cancer Center (KUCC), an NCI-designated comprehensive cancer center, implemented the Curated Cancer Clinical Outcomes Database (C3OD) to perform trial feasibility analyses using real-time electronic medical record data. In this study, we proposed a Bayesian hierarchical model to evaluate annual cancer clinical trial accrual performance.

Methods

The Bayesian hierarchical model uses Poisson models to describe the accrual performance of individual cancer clinical trials and a hierarchical component to describe the variation in performance across studies. Additionally, this model evaluates the impacts of the C3OD and the COVID-19 pandemic using posterior probabilities across evaluation years. The performance metric is the ratio of the observed accrual rate to the target accrual rate.

Results

Posterior medians of the annual accrual performance at the KUCC from 2018 to 2023 are 0.233, 0.246, 0.197, 0.150, 0.254, and 0.340. The COVID-19 pandemic partly explains the drop in performance in 2020 and 2021. The posterior probability that annual accrual performance is better with C3OD in 2023 than pre-pandemic (2019) is 0.935.

Conclusions

This study comprehensively evaluates the annual performance of clinical trial accrual at the KUCC, revealing a negative impact of COVID-19 and an ongoing positive impact of C3OD implementation. Two sensitivity analyses further validate the robustness of our model. Evaluating annual accrual performance across clinical trials is essential for a cancer center. The performance evaluation tools described in this paper are highly recommended for monitoring clinical trial accrual.

An Updated Overview of Existing Cancer Databases and Identified Needs

Our search of existing cancer databases aimed to assess the current landscape and identify key needs. We analyzed 71 databases, focusing on genomics, proteomics, lipidomics, and glycomics. We found a lack of cancer-related lipidomic and glycomic databases, indicating a need for further development in these areas. Proteomic databases dedicated to cancer research were also limited. To assess overall progress, we included human non-cancer databases in proteomics, lipidomics, and glycomics for comparison. This provided insights into advancements in these fields over the past eight years. We also analyzed other types of cancer databases, such as clinical trial databases and web servers. Evaluating user-friendliness, we used the FAIRness principle to assess findability, accessibility, interoperability, and reusability. This ensured databases were easily accessible and usable. Our search summary highlights significant growth in cancer databases while identifying gaps and needs. These insights are valuable for researchers, clinicians, and database developers, guiding efforts to enhance accessibility, integration, and usability. Addressing these needs will support advancements in cancer research and benefit the wider cancer community.

Bolstering the complex study start-up process at NCI cancer centers using technology

Background

The study startup process for interventional clinical trials is a complex process that involves the efforts of many different teams. Each team is responsible for their startup checklist in which they verify that the necessary tasks are done before a study can move on to the next team. This regulatory process provides quality assurance and is vital for ensuring patient safety [10]. However, without having this startup process centralized and optimized, study approval can take longer than necessary as time is lost when it passes through many different hands.

Objective

This manuscript highlights the process and the systems that were developed at The University of Kansas Comprehensive Cancer Center regarding the study startup process. To facilitate this process the regulatory management, site development, cancer center administration, and the Biostatistics & Informatics Shared Resources (BISR) teams came together to build a platform aimed at streamlining the startup process and providing a transparent view of where a study is in the startup process.

Process

Ensuring the guidelines are clearly articulated for the review criteria of each of the three review boards, i.e., Disease Working Group (DWG), Executive Resourcing Committee (ERC), and Protocol Review and Monitoring Committee (PRMC) along with a system that can track every step and its history throughout the review process.

Results

Well-defined processes and tracking methodologies have allowed the operations teams to track each study closely and ensure the 90-day and 120-day deadlines are met, this allows the operational team to dynamically prioritize their work daily. It also provides Principal investigators a transparent view of where their study stands within the study startup process and allows them to prepare for the next steps accordingly.

Conclusion/future work

The current process and technology deployment has been a significant improvement to expedite the review process and minimize study startup delays. There are still a few opportunities to fine-tune the study startup process; an example of which includes automatically informing the operational managers or the study teams to act upon deadlines regarding study review rather than the current manual communication process which involves them looking it up in the system which can add delays.

The need to study rural cancer outcome disparities at the local level: a retrospective cohort study in Kansas and Missouri

Background

Rural residence is commonly thought to be a risk factor for poor cancer outcomes. However, a number of studies have reported seemingly conflicting information regarding cancer outcome disparities with respect to rural residence, with some suggesting that the disparity is not present and others providing inconsistent evidence that either urban or rural residence is associated with poorer outcomes. We suggest a simple explanation for these seeming contradictions: namely that rural cancer outcome disparities are related to factors that occur differentially at a local level, such as environmental exposures, lack of access to care or screening, and socioeconomic factors, which differ by type of cancer.

Methods

We conducted a retrospective cohort study examining ten cancers treated at the University of Kansas Medical Center from 2011 to 2018, with individuals from either rural or urban residences. We defined urban residences as those in a county with a U.S. Department of Agriculture Urban Influence Code (UIC) of 1 or 2, with all other residences defines a rural. Inverse probability of treatment weighting was used to create a pseudo-sample balanced for covariates deemed likely to affect the outcomes modeled with cumulative link and weighted Cox-proportional hazards models.

Results

We found that rural residence is not a simple risk factor but rather appears to play a complex role in cancer outcome disparities. Specifically, rural residence is associated with higher stage at diagnosis and increased survival hazards for colon cancer but decreased risk for lung cancer compared to urban residence.

Conclusion

Many cancers are affected by unique social and environmental factors that may vary between rural and urban residents, such as access to care, diet, and lifestyle. Our results show that rurality can increase or decrease risk, depending on cancer site, which suggests the need to consider the factors connected to rurality that influence this complex pattern. Thus, we argue that such disparities must be studied at the local level to identify and design appropriate interventions to improve cancer outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12190-w.

Prescreening to Increase Therapeutic Oncology Trial Enrollment at the Largest Public Hospital in the United States

PURPOSE

The recruitment of underserved patients into therapeutic oncology trials is imperative. The National Institutes of Health mandates the inclusion of minorities in clinical research, although their participation remains under-represented. Institutions have used data mining to match patients to clinical trials. In a public health care system, such expensive tools are unavailable.

METHODS

The NYU Clinical Trials Office implemented a quality improvement program at Bellevue Hospital Cancer Center to increase therapeutic trial enrollment. Patients are screened through the electronic medical record, tumor board conferences, and the cancer registry. Our analysis evaluated two variables: number of patients identified and those enrolled into clinical trials.

RESULTS

Two years before the program, there were 31 patients enrolled. For a period of 24 months (July 2017 to July 2019), we identified 255 patients, of whom 143 (56.1%) were enrolled. Of those enrolled, 121 (84.6%) received treatment, and 22 (15%) were screen failures. Fifty-five (38.5%) were referred to NYU Perlmutter Cancer Center for therapy. Of the total enrollees, 64% were female, 56% were non-White, and overall median age was 55 years (range: 33-88 years). Our participants spoke 16 different languages, and 57% were non-English-speaking. We enrolled patients into eight different disease categories, with 38% recruited to breast cancer trials. Eighty-three percent of our patients reside in low-income areas, with 62% in both low-income and Health Professional Shortage Areas.

CONCLUSION

Prescreening at Bellevue has led to a 4.6-fold increase in patient enrollment to clinical trials. Future research into using prescreening programs at public institutions may improve access to clinical trials for underserved populations.

Application of Information Technology to Clinical Trial Evaluation and Enrollment: A Review

Importance

As cancer treatment has become more individualized, oncologic clinical trials have become more complex. Increasingly numerous and stringent eligibility criteria frequently include tumor molecular or genomic characteristics that may not be readily identified in medical records, rendering it difficult to best match clinical trials with clinical sites and to identify potentially eligible patients once a clinical trial has been selected and activated. Partly because of these factors, enrollment rates for cancer clinical trials remain low, creating delays and increased costs for drug development. Information technology (IT) platforms have been applied to the implementation and conduct of clinical trials to improve efficiencies in several medical fields, and these platforms have recently been introduced to oncologic studies.

Observations

This review summarizes cancer and noncancer studies that used IT platforms for assistance with clinical trial site selection, patient recruitment, and patient screening. The review does not address the use of IT in other aspects of clinical research, such as wearable physical activity monitors or telehealth visits. A large number of IT platforms (which may be patient facing, site or investigator facing, or sponsor facing) are now commercially available. These applications use artificial intelligence and/or natural language processing to identify and summarize protocol eligibility criteria, institutional patient populations, and individual electronic health records. Although there is an expanding body of literature examining the role of this technology, relatively few studies to date have been performed in oncologic settings.

Conclusions and Relevance

This review found that an increasing number and variety of IT platforms were available to assist in the planning and conduct of clinical trials. Because oncologic clinical care and clinical trial protocols are particularly complex, nuanced, and individualized, published experience with this technology in other fields may not be fully applicable to cancer settings. The extent to which these services will overcome ongoing and increasing challenges in cancer clinical research remains unclear.

Improving the efficiency of clinical trials by standardizing processes for Investigator Initiated Trials

Early phase clinical trials are the first step in testing new medications and therapeutics developed by clinical and biomedical investigators. These trials aim to find a safe dose of a newly targeted drug (phase I) or find out more about the side effects and early signals of treatment efficacy (phase II). In a research institute, many biomedical investigators in oncology are encouraged to initiate such trials early in their careers as part of developing their research portfolio. These investigator-initiated trials (IITs) are funded internally by the University of Kansas Cancer Center or partially funded by pharmaceutical companies. As financial, administrative, and practical considerations play an essential role in the successful completion of IITs, it is imperative to efficiently allocate resources to plan, design, and execute these studies within the allotted time. This manuscript describes monitoring tools and processes to improve the efficiency, cost-effectivness, and reliability of IITs. The contributions of this team to processes such as: participant recruitment, feasibility analysis, clinical trial design, accrual monitoring, data management, interim analysis support, and final analysis and reporting are described in detail. This manuscript elucidates how, through the aid of technology and dedicated personnel support, the efficiency of IIT-related processes can be improved. Early results of these initiatives look promising, and the Biostatistics and Informatics team intends to continue fostering innovative methodologies to enhance cancer research by improving the efficiency of IITs.

Optimizing Retrieval of Biospecimens Using the Curated Cancer Clinical Outcomes Database (C3OD)

To fully support their role in translational and personalized medicine, biorepositories and biobanks must continue to advance the annotation of their biospecimens with robust clinical and laboratory data. Translational research and personalized medicine require well-documented and up-to-date information, but the infrastructure used to support biorepositories and biobanks can easily be out of sync with the host institution. To assist researchers and provide them with accurate pathological, epidemiological, and bio-molecular data, the Biospecimen Repository Core Facility (BRCF) at the University of Kansas Medical Center (KUMC) merges data from medical records, the tumor registry, and pathology reports using the Curated Cancer Clinical Outcomes Database (C3OD). In this report, we describe the utilization of C3OD to optimally retrieve and dispense biospecimen samples using these 3 data sources and demonstrate how C3OD greatly increases the efficiency of obtaining biospecimen samples for the researchers.

Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Health Records

Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the data contains unstructured text, presenting an obstacle to automated extraction. Natural language processing (NLP) can structure and learn from text, but NLP algorithms were not designed for the unique characteristics of EHR. Here, we propose Relevant Word Order Vectorization (RWOV) to aid with structuring. RWOV is based on finding the positional relationship between the most relevant words to predicting the class of a text. This facilitates machine learning algorithms to use the interaction of not just keywords but positional dependencies (e.g. a relevant word occurs 5 relevant words before some term of interest). As a proof-of-concept, we attempted to classify the hormone receptor status of breast cancer patients treated at the University of Kansas Medical Center, comparing RWOV to other methods using the F1 score and AUC. RWOV performed as well as, or better than other methods in all but one case. For F1 score, RWOV had a clear edge on most tasks. AUC tended to be closer, but for HER2, RWOV was significantly better for most comparisons. These results suggest RWOV should be further developed for EHR-related NLP.