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IIR 16-024 – HSR&D Study

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IIR 16-024
Advanced Prediction Models to Optimize Treatment and Access for Veterans with Hepatitis C
Akbar K Waljee MD MSc
VA Ann Arbor Healthcare System, Ann Arbor, MI
Ann Arbor, MI
Funding Period: April 2017 - March 2021

BACKGROUND/RATIONALE:
Chronic hepatitis C (CHC) continues to present challenges in the treatment of Veterans. Direct acting antiviral agents (DAAs) for the treatment of CHC are highly effective, have a favorable safety profile, and are well tolerated, eliciting significant consumer demand. However, DAAs are costly, making it difficult for some healthcare systems to meet this growing demand from patients.

OBJECTIVE(S):
The purpose of this study is to lay the groundwork for risk-based treatment of CHC among non-cirrhotic Veterans in the Veterans Health Administration (VHA) by: (Aim 1) developing accurate, clinically relevant, and implementable risk prediction models; (Aim 2) engaging Veterans to develop consensus on how to implement risk-based treatment; and (Aim 3) evaluating the clinical and economic effects of risk-based treatment.

METHODS:
Preliminary work demonstrated the feasibility of using a machine-learning (ML) risk prediction model to identify patients at high risk and low risk for disease progression in a clinical trial cohort. In this 4-year study, we will use VA electronic data from 2004-2014 to adapt, validate and refine this model among Veterans with CHC. We will also engage Veterans with and without CHC, eliciting their preferences and values regarding risk-based treatment of CHC by applying consensus techniques (e.g., deliberative democracy). Finally, to estimate the incremental benefit of risk-based treatment over current treatment, we will use simulation modeling.

FINDINGS/RESULTS:
Aim 1: Of the 150K Veterans in our cohort, an initial evaluation of the clinical trial cohort model was conducted on a subset of 15K Veterans and found an AuROC curve of 0.6 to predict cirrhosis. Given this relatively poor performance, we developed a new model on our 150K cohort using a 70% training, 30% testing cohort. We used a cox model to build a longitudinal model with a concordance of 0.76. We will continue to refine this model to predict both cirrhosis and fibrosis using both APRI and FIB4 and in addition, test other machine learning and deep learning models.

Aim 2: Out of 15 Veterans consented, 11 interviews were completed to assess knowledge of hepatitis C and provide insight into policy decisions surrounding treatment of HCV. It was found that Veteran knowledge is variable in terms of how you contract HCV and its symptoms. Additionally, most Veterans agreed that those with HCV that were the sickest should be treated first. The interviews provided insight that helped develop materials for the Deliberative Democracy session (Aim 2) that will take place in 2018.

IMPACT:
Despite highly effective treatment for CHC, we cannot treat all infected patients immediately. A risk-based, systematic approach will maximize treatment benefit while limiting clinical and economic harms. This research will produce a strategy to systematically identify high-risk patients for early treatment and lay the groundwork for future implementation of this strategy. The approach used in this study can also serve as a template for future efforts to implement risk-based management strategies in other clinical contexts. The DD session will allow Veterans to enhance our understanding of what Veterans believe is important when policy makers establish guidelines surrounding such treatments for high-cost medications like those for HCV.

PUBLICATIONS:

Journal Articles

  1. Ioannou GN, Tang W, Beste LA, Tincopa MA, Su GL, Van T, Tapper EB, Singal AG, Zhu J, Waljee AK. Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis. JAMA Network Open. 2020 Sep 1; 3(9):e2015626.
  2. Tapper EB, Zhao L, Nikirk S, Baki J, Parikh ND, Lok AS, Waljee AK. Incidence and Bedside Predictors of the First Episode of Overt Hepatic Encephalopathy in Patients With Cirrhosis. The American journal of gastroenterology. 2020 Aug 6.
  3. Waljee AK, Ryan KA, Krenz CD, Ioannou GN, Beste LA, Tincopa MA, Saini SD, Su GL, Arasim ME, Roman PT, Nallamothu BK, De Vries R. Eliciting patient views on the allocation of limited healthcare resources: a deliberation on hepatitis C treatment in the Veterans Health Administration. BMC health services research. 2020 May 1; 20(1):369.
  4. Liu W, Stansbury C, Singh K, Ryan AM, Sukul D, Mahmoudi E, Waljee A, Zhu J, Nallamothu BK. Predicting 30-day hospital readmissions using artificial neural networks with medical code embedding. PLoS ONE. 2020 Apr 15; 15(4):e0221606.
  5. Higashi RT, Jain MK, Quirk L, Rich NE, Waljee AK, Turner BJ, Lee SC, Singal AG. Patient and provider-level barriers to hepatitis C screening and linkage to care: A mixed-methods evaluation. Journal of Viral Hepatitis. 2020 Jul 1; 27(7):680-689.
  6. Cron DC, Tincopa MA, Lee JS, Waljee AK, Hammoud A, Brummett CM, Waljee JF, Englesbe MJ, Sonnenday CJ. Prevalence and patterns of opioid use before and after liver transplantation. Transplantation. 2020 Feb 4.
  7. Cohen-Mekelburg S, Waljee AK, Kenney BC, Tapper EB. Coordination of Care Associated With Survival and Health Care Utilization in a Population-Based Study of Patients With Cirrhosis. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. 2020 Jan 9.
  8. Tapper EB, Korovaichuk S, Baki J, Williams S, Nikirk S, Waljee AK, Parikh ND. Identifying Patients With Hepatic Encephalopathy Using Administrative Data in the ICD-10 Era. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. 2019 Dec 27.
  9. Fogel EL, Lehman GA, Tarnasky P, Cote GA, Schmidt SE, Waljee AK, Higgins PDR, Watkins JL, Sherman S, Kwon RSY, Elta GH, Easler JJ, Pleskow DK, Scheiman JM, El Hajj II, Guda NM, Gromski MA, McHenry L, Arol S, Korsnes S, Suarez AL, Spitzer R, Miller M, Hofbauer M, Elmunzer BJ, US Cooperative for Outcomes Research in Endoscopy (USCORE). Rectal indometacin dose escalation for prevention of pancreatitis after endoscopic retrograde cholangiopancreatography in high-risk patients: a double-blind, randomised controlled trial. The lancet. Gastroenterology & hepatology. 2020 Feb 1; 5(2):132-141.


DRA: Infectious Diseases
DRE: Treatment - Implementation, TRL - Applied/Translational, Technology Development and Assessment
Keywords: Care Management Tools, Decision Support, Healthcare Algorithms
MeSH Terms: none

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