Background and Objectives
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.
Our study aims to apply a dynamic decision-making framework to 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. The framework will be implemented on electronic health record data from Kaiser Permanente Southern California (KPSC), utilizing rich patient-level information, such as disease history, lab results, and comorbidities in order to inform risk and guide clinical decision-making.
Project Aims
Aim 1: Develop and evaluate a sequential decision-making framework by merging statistical methods for prediction modeling with economics concepts for value of information (VOI) analysis to guide individualized decisions about testing and treatment for recurrence using serial biomarker testing, with the goal of optimizing long-term patient outcomes.
Aim 2: Apply this framework to existing electronic health record (EHR) and existing cohort study data to identify a risk-adaptive surveillance strategy for detecting Colorectal Cancer (CRC ) recurrence that targets high-risk patients for frequent follow-up and treatment, and recommends less frequent follow-up for low-risk patients.
Aim 3: Assess the comparative effectiveness of the proposed risk-adaptive surveillance strategy versus guideline-based surveillance in CRC.
Aim 4: Use existing data to evaluate the generalizability of the framework by addressing the optimal frequency of follow-up among (a) low-risk men with recurrent Prostate Cancer (PrCA), for whom treatment may be safely delayed for a prolonged period, and (b) long-term survivors of Chronic Myeloid Leukemia (CML), who achieve long-term remission but currently continue to be monitored frequently.
Investigators
Aasthaa Bansal, PhD
Anirban Basu, PhD
Erin Hahn, PhD, MPH
Patrick Heagerty, PhD
Lurdes Inoue, PhD
Veena Shankaran, MD
David Veenstra, PharmD, PhD
Graduate Students
Yilin Chen, MPH
Samantha Clark, MS
Sara Khor, MASc
Tricia Rodriguez, MPH
Staff
Connor Henry, MPH
Publications (Selected)