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Utilizing Clinical Trial Data to Assess Timing of Surgical Treatment for Emphysema Patients

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  • Maryam Alimohammadi

    (Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA)

  • W. Art Chaovalitwongse

    (Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA)

  • Hubert J. Vesselle

    (Department of Radiology, University of Washington, Seattle, WA, USA)

  • Shengfan Zhang

    (Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA)

Abstract

Background Lung volume reduction surgery (LVRS) and medical therapy are 2 available treatment options in dealing with severe emphysema, which is a chronic lung disease. However, or there are currently limited guidelines on the timing of LVRS for patients with different characteristics. Objective The objective of this study is to assess the timing of receiving LVRS in terms of patient outcomes, taking into consideration a patient’s characteristics. Methods A finite-horizon Markov decision process model for patients with severe emphysema was developed to determine the short-term (5 y) and long-term timing of emphysema treatment. Maximizing the expected life expectancy, expected quality-adjusted life-years, and total expected cost of each treatment option were applied as the objective functions of the model. To estimate parameters in the model, the data provided by the National Emphysema Treatment Trial were used. Results The results indicate that the treatment timing strategy for patients with upper-lobe predominant emphysema is to receive LVRS regardless of their specific characteristics. However, for patients with non–upper-lobe–predominant emphysema, the optimal strategy depends on the age, maximum workload level, and forced expiratory volume in 1 second level. Conclusion This study demonstrates the utilization of clinical trial data to gain insights into the timing of surgical treatment for patients with emphysema, considering patient age, observable health condition, and location of emphysema. Highlights Both short-term and long-term Markov decision process models were developed to assess the timing of receiving lung volume reduction surgery in patients with severe emphysema. How clinical trial data can be used to estimate the parameters and obtain short-term results from the Markov decision process model is demonstrated. The results provide insights into the timing of receiving lung volume reduction surgery as a function of a patient’s characteristics, including age, emphysema location, maximum workload, and forced expiratory volume in 1 second level.

Suggested Citation

  • Maryam Alimohammadi & W. Art Chaovalitwongse & Hubert J. Vesselle & Shengfan Zhang, 2023. "Utilizing Clinical Trial Data to Assess Timing of Surgical Treatment for Emphysema Patients," Medical Decision Making, , vol. 43(1), pages 110-124, January.
    • Handle: RePEc:sae:medema:v:43:y:2023:i:1:p:110-124
    DOI: 10.1177/0272989X221132256
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    References listed on IDEAS

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    1. Steven M. Shechter & Matthew D. Bailey & Andrew J. Schaefer & Mark S. Roberts, 2008. "The Optimal Time to Initiate HIV Therapy Under Ordered Health States," Operations Research, INFORMS, vol. 56(1), pages 20-33, February.
    2. Brian T. Denton & Murat Kurt & Nilay D. Shah & Sandra C. Bryant & Steven A. Smith, 2009. "Optimizing the Start Time of Statin Therapy for Patients with Diabetes," Medical Decision Making, , vol. 29(3), pages 351-367, May.
    3. Bruce A. Craig & Peter P. Sendi, 2002. "Estimation of the transition matrix of a discrete‐time Markov chain," Health Economics, John Wiley & Sons, Ltd., vol. 11(1), pages 33-42, January.
    4. Oguzhan Alagoz & Heather Hsu & Andrew J. Schaefer & Mark S. Roberts, 2010. "Markov Decision Processes: A Tool for Sequential Decision Making under Uncertainty," Medical Decision Making, , vol. 30(4), pages 474-483, July.
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