Aasthaa Bansal leads an NIH MERIT award-winning team to develop decision-making frameworks to guide post-treatment care for cancer survivors
Despite receiving effective primary treatment, many cancer survivors remain at risk of relapse and associated morbidity and mortality. Surveillance testing using biomarkers and imaging may detect disease recurrence before clinical symptoms manifest and allow for early treatment.
Current guidelines recommend frequent testing in most cancer settings. However, frequent testing involves trade-offs. Many patients are at low risk of experiencing recurrence, and the complications and costs of frequent tests are not justified for these patients. Experts have cautioned against over-testing and advocated for risk-based strategies using the personal clinical history of each patient to provide better tailored care to cancer survivors. Although there is growing recognition that a one-size-fits-all strategy of frequent patient visits may be suboptimal, there is ambiguity regarding how best to tailor surveillance to individual cancer survivors.
“As more people beat cancer, providers need better tools to help them understand the relative costs and benefits of alternative surveillance strategies and provide more personalized follow-up care to cancer survivors.”—Assistant Professor Aasthaa Bansal, The CHOICE Institute
Assistant Professor Aasthaa Bansal is leading an interdisciplinary team to carry out research and create tools that seek to shift the paradigm for how routinely-collected patient information is used for clinical management, by innovatively coupling data-adaptive prediction modeling with statistical decision theory. The decision-making framework will evaluate the value of future clinical information and guide patient-level decisions about the optimal frequency of surveillance testing, while taking into account a patient’s evolving risk of recurrence. “As a patient’s information changes, if they go from low to high risk, for example, their follow-up recommendations will also be updated in this dynamic decision-making framework,” said Aasthaa.
Her team includes CHOICE Professors Anirban Basu and David Veenstra, as well as researchers from UW Biostatistics and Medicine, Fred Hutch, and Kaiser Permanente Southern California (KPSC). The framework will be implemented on electronic health record data from KPSC, utilizing rich patient-level information, such as disease history, lab results, and comorbidities in order to inform risk and guide decision-making, which could lead to major advances in personalized medical decision-making. The focus of this grant will be to apply the framework to colorectal cancer, prostate cancer and chronic myeloid leukemia. However, the general methodology developed by the research team could be applied to any disease setting where ongoing surveillance is a major component of patient care. “We are creating an approach that we plan to extend to other diseases, including pediatric cancers, where similar open questions exist,” reflected Aasthaa.
The NIH deemed her project so significant, they granted the team $2.2M for 5 years and up to 2 additional years of funding through a Method to Extend Research in Time (MERIT) Award. MERIT Awards provide long-term grant support to investigators whose research competence and productivity are distinctly superior and who are highly likely to continue to perform in an outstanding manner, according to the NIH. In 2016 and 2017, only eight MERIT awards were granted each year across all of NIH.
The National Cancer Institute of the National Institutes of Health funded this R37 grant (1R37CA218413-01A1) for $2.2M.