Document text
Principal Investigator: Jason Ken Hou
Organization: MICHAEL E DEBAKEY VA MEDICAL CENTER
Fiscal Year: 2024
Funding agency: Veterans Affairs
Background: Biologic medications (biologics) are highly effective for diseases of the immune system, cancers,
and other conditions; however, their high expense is a barrier to care and a burden to the healthcare system.
Biologics cannot be exactly copied as “generic” medications. Biosimilars- similar, but not identical versions of
biologic medications- are approved with large potential cost savings. However, VA providers and patients have
concerns regarding biosimilar switching safety and effectiveness as disease-specific randomized controlled
trials are not required for approval. Significance/Impact: Antagonists to tumor necrosis factor-α (Anti-TNFs)
are the largest class of biologics with biosimilars where switching may be feasible to reduce costs; however
how to safely and effectively integrate their use in a manner acceptable to patients is unknown. This proposal
addresses the VA HSR priority of veteran safety, the ORD-wide research priority of increasing substantial real-
world impact of VA research, and uses cross-cutting HSR methods of health systems engineering through a
learning healthcare system. Innovation: Crohn’s disease (CD) and ulcerative colitis (UC) are the 1st and 2nd
most common indications for Anti-TNFs in the VA and can serve as a model for a learning healthcare system
approach for mitigation of adverse events related to biosimilar switching. Specific Aims: Aim 1a: To compare
rates of adverse events in CD and UC patients continued on Anti-TNF originator to those switched to
biosimilar. Aim 1b: To compare rates of CD or UC exacerbation in patients continued on Anti-TNF originator
to those switched to biosimilar. Aim 2: To compare the accuracy and calibration of 2a) traditional regression
models vs. 2b) machine learning models for predicting medication related adverse event related to Anti-TNF in
VA users with CD and UC. Aim 3: To use deliberative democracy methods to engage Veterans, to elicit their
preference regarding “like" medication switch programs with and without their knowledge and to develop
consensus around treatment approaches. Methodology: Aim 1 will be achieved through a retrospective
cohort study of CD and UC patients who received Anti-TNF from the national VA datasets from 2017-2019.
Adverse events and exacerbations will be determined using a combination of administrative data and manual
chart review. Analyses for Aim 1 will proceed by Poisson regression using GEE. Adjusted event rate ratios of
patients switched to biosimilar compared to those who continued on originator biosimilar will be calculated with
95% confidence intervals and Wald p-values will be derived from the regression model estimates. Prediction of
patients who have adverse events to Anti-TNF will inform selection of appropriate therapy, and guidance of
patients for biosimilar switching. For Aim 2, both traditional regression models and machine learning models
will be constructed to identify which model will be better for predicting Anti-TNF related adverse events.
Developing the best possible risk stratification tool by comparing these models will allow us to identify veterans
that are at risk of adverse events to improve both the quality and efficiency of veteran care. It is critically
important that VA policies incorporate the opinions of Veterans on ethically controversial issues that impact
their health. Aim 3 will employ deliberative democratic methods that offer a practical and reliable approach to
soliciting informed and considered opinions in complex policy issues. Democratic deliberation uses education
by experts and carefully structured deliberation among peers to deliver informed opinions and policy
suggestions from concerned stakeholders. Next Steps/ Implementation: This proposal is supported with
clinical partners: the national VA Inflammatory Bowel Disease Technical Advisory Group and Pharmacy
Benefits Management who will disseminate findings from this study through VA-specific biosimilar switch
clinical guidelines and VA-pharmacy prescription policy. Future studies will include pragmatic clinical trials of
other biosimilar biologics using the learning healthcare platform created in this proposal.
Terms: <(TNF)-α><Address><Adverse Experience><Adverse event><Ailmentary System><Alimentary System><Biological><Biological Agent><Biological Products><Cachectin><Calibration><Cancers><Caring><Chronic><Clinical><Collaborations><Complex><Confidence Intervals><Consensus><Cost Savings><Crohn disease><Crohn's><Crohn's disease><Crohn's disorder><Data><Data Set><Digestive System><Disease><Disorder><Drugs><Education><Educational aspects><Effectiveness><Engineering><Ethics><Event><Flare><Food and Drug Administration><Future><Gastrointestinal Body System><Gastrointestinal Organ System><Granulomatous Enteritis><Guidelines><Health><Health Care Systems><Health system><Healthcare><Healthcare Systems><Hospital Admission><Hospitalization><Immune><Immune Diseases><Immune Disorders><Immune Dysfunction><Immune System Diseases><Immune System Disorder><Immune System Dysfunction><Immune System and Related Disorders><Immunes><Immunodeficiency and Immunosuppression Disorders><Immunologic Diseases><Immunological Diseases><Immunological Dysfunction><Immunological System Dysfunction><Incidence><Inflammatory><Inflammatory Bowel Diseases><Inflammatory Bowel Disorder><Knowledge><Learning><Legal patent><MAb cA2><Macrophage-Derived TNF><Malignant Neoplasms><Malignant Tumor><Manuals><Mediating><Medication><Methodology><Methods><Modeling><Monocyte-Derived TNF><Operative Procedures><Operative Surgical Procedures><Organism><Patents><Patient Preferences><Patients><Pharmaceutical Preparations><Pharmacies><Pharmacy facility><Policies><Pragmatic clinical trial><Prevalence><Provider><Randomized, Controlled Trials><Remicade><Research><Research Priority><Retrospective cohort study><Safety><Structure><Suggestion><Surgical><Surgical Interventions><Surgical Procedure><System><TNF><TNF A><TNF Alpha><TNF gene><TNF-α><TNFA><TNFα><Techniques><Tumor Necrosis Factor><Tumor Necrosis Factor-alpha><USFDA><Ulcerated Colitis><Ulcerative Colitis><United States Food and Drug Administration><Veterans><adverse event risk><antagonism><antagonist><barrier to care><barrier to health care><barrier to healthcare><barrier to treatment><biologic><biologics><biopharmaceutical><biotherapeutic agent><cost><cost effective><cost shifting><deliberative democracy><drug/agent><eleocolitis><ethical><gastrointestinal system><health care><improved><inflammatory disease of the intestine><inflammatory disorder of the intestine><infliximab><innovate><innovation><innovative><intestinal autoinflammation><living system><machine learning based model><machine learning based prediction model><machine learning based predictive model><machine learning model><machine learning prediction><machine learning prediction model><malignancy><military member><military service><monoclonal antibody cA2><neoplasm/cancer><obstacle to care><obstacle to healthcare><peer><pharmacy benefit><preference><programs><randomized control trial><regional enteritis><risk stratification><safety study><service member><stratify risk><surgery><tool>