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Mohamadkhani N, Nahvijou A, Hadian M. Optimal age to stop prostate cancer screening and early detection. J Cancer Policy 2023; 38:100443. [PMID: 37598870 DOI: 10.1016/j.jcpo.2023.100443] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 08/13/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND Prostate Cancer screening should be discontinued at older ages because competing mortality risks eventually dominate the risk of Prostate Cancer and harms exceed benefits. We explored the Prostate Cancer screening stopping age from the patient, healthcare system, and social perspectives in Iran. METHODS We applied Bellman Equations to formulate the net benefits biopsy and "do nothing". Using difference between the net benefits of two alternatives, we calculated the stopping age. The cancer states were without cancer, undetected cancer, detected cancer, metastatic cancer, and death. To move between states, we applied Markov property. Transition probabilities, rewards, and costs were inferred from the medical literature. The base-case scenario estimated the stopping age from the patient, healthcare system, and social perspectives. A one-way sensitivity used to find the most influential parameters on the stopping age. RESULTS Our results suggested that Prostate Cancer screening stopping ages from the patient, healthcare system, and social were 70, 68, and 68 respectively. The univariate sensitivity analysis showed that the stopping ages were sensitive to the disutility of treatment, discount factor, the disutility of metastasis, the annual probability of death from other causes, and the annual probability of developing metastasis from the hidden cancer state. CONCLUSIONS Men should not be screened for Prostate Cancer beyond 70 years old, as this results in the net benefit of "do nothing" above the biopsy. Nevertheless, this finding needs to be further studied with more detailed cancer progression models (considering re-biopsy, comorbidities, and more complicated states transition) and using local utility and willingness to pay value information.
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Affiliation(s)
- Naser Mohamadkhani
- Department of Health Economics, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Azin Nahvijou
- Cancer Research Center of Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
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Cho DD, Bretthauer KM, Schoenfelder J. Patient-to-nurse ratios: Balancing quality, nurse turnover, and cost. Health Care Manag Sci 2023; 26:807-826. [PMID: 38019329 DOI: 10.1007/s10729-023-09659-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 10/04/2023] [Indexed: 11/30/2023]
Abstract
We consider the problem of setting appropriate patient-to-nurse ratios in a hospital, an issue that is both complex and widely debated. There has been only limited effort to take advantage of the extensive empirical results from the medical literature to help construct analytical decision models for developing upper limits on patient-to-nurse ratios that are more patient- and nurse-oriented. For example, empirical studies have shown that each additional patient assigned per nurse in a hospital is associated with increases in mortality rates, length-of-stay, and nurse burnout. Failure to consider these effects leads to disregarded potential cost savings resulting from providing higher quality of care and fewer nurse turnovers. Thus, we present a nurse staffing model that incorporates patient length-of-stay, nurse turnover, and costs related to patient-to-nurse ratios. We present results based on data collected from three participating hospitals, the American Hospital Association (AHA), and the California Office of Statewide Health Planning and Development (OSHPD). By incorporating patient and nurse outcomes, we show that lower patient-to-nurse ratios can potentially provide hospitals with financial benefits in addition to improving the quality of care. Furthermore, our results show that higher policy patient-to-nurse ratio upper limits may not be as harmful in smaller hospitals, but lower policy patient-to-nurse ratios may be necessary for larger hospitals. These results suggest that a "one ratio fits all" patient-to-nurse ratio is not optimal. A preferable policy would be to allow the ratio to be hospital-dependent.
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Affiliation(s)
- David D Cho
- Department of Management, College of Business and Economics, California State University, Fullerton, Fullerton, CA, 92831, USA.
| | - Kurt M Bretthauer
- Operations and Decision Technologies Department, Kelley School of Business, Indiana University, Bloomington, IN, 47405, USA
| | - Jan Schoenfelder
- Health Care Operations / Health Information Management, University of Augsburg, 86159, Augsburg, Germany
- School of Management, Lancaster University Leipzig, 04109, Leipzig, Germany
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Holder SS, Malvan-Iyalla AS, Arfan S, Basani V, Tiesenga F. Keloid Development After Fine Needle Aspiration of the Thyroid: A Rare Case and Review of Management Strategies. Cureus 2023; 15:e42359. [PMID: 37621840 PMCID: PMC10445297 DOI: 10.7759/cureus.42359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/24/2023] [Indexed: 08/26/2023] Open
Abstract
Keloids are pathological scars characterized by abnormal proliferation of tissue as a result of cutaneous injury. There is a high prevalence of keloid development in certain ethnicities. Individuals from African, Hispanic, and Asian backgrounds have a higher likelihood of developing keloids when compared to Caucasians. Keloids are known to lack spontaneous regression and have a high rate of recurrence after removal, thereby causing a cosmetic problem that affects people physically and emotionally. Keloids commonly occur after burns, tattoos, piercings, and deep wounds; however, in rare cases, they may develop after minimally invasive procedures. This case describes the experience of a 48-year-old African American male who underwent a thyroid fine needle aspiration biopsy and subsequently developed a keloid in the neck region. This report aims to explore this unique occurrence, highlight the interplay between epidemiology, race, and genetics in influencing the development of keloids, and review the management strategies for neck keloids.
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Affiliation(s)
- Shaniah S Holder
- Medicine, American University of Barbados School of Medicine, Bridgetown, BRB
| | | | - Sara Arfan
- General Surgery, Windsor University School of Medicine, Chicago, USA
| | - Vimal Basani
- Medicine, St. George's University School of Medicine, True Blue, GRD
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Mračko A, Vanovčanová L, Cimrák I. Mammography Datasets for Neural Networks-Survey. J Imaging 2023; 9:jimaging9050095. [PMID: 37233314 DOI: 10.3390/jimaging9050095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/02/2023] [Accepted: 05/05/2023] [Indexed: 05/27/2023] Open
Abstract
Deep neural networks have gained popularity in the field of mammography. Data play an integral role in training these models, as training algorithms requires a large amount of data to capture the general relationship between the model's input and output. Open-access databases are the most accessible source of mammography data for training neural networks. Our work focuses on conducting a comprehensive survey of mammography databases that contain images with defined abnormal areas of interest. The survey includes databases such as INbreast, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), the OPTIMAM Medical Image Database (OMI-DB), and The Mammographic Image Analysis Society Digital Mammogram Database (MIAS). Additionally, we surveyed recent studies that have utilized these databases in conjunction with neural networks and the results they have achieved. From these databases, it is possible to obtain at least 3801 unique images with 4125 described findings from approximately 1842 patients. The number of patients with important findings can be increased to approximately 14,474, depending on the type of agreement with the OPTIMAM team. Furthermore, we provide a description of the annotation process for mammography images to enhance the understanding of the information gained from these datasets.
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Affiliation(s)
- Adam Mračko
- Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
- Research Centre, University of Žilina, 010 26 Žilina, Slovakia
| | - Lucia Vanovčanová
- 2nd Radiology Department, Faculty of Medicine, Comenius University in Bratislava, 813 72 Bratislava, Slovakia
- St. Elizabeth Cancer Institute, 812 50 Bratislava, Slovakia
| | - Ivan Cimrák
- Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
- Research Centre, University of Žilina, 010 26 Žilina, Slovakia
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Yang CY, Shiranthika C, Wang CY, Chen KW, Sumathipala S. Reinforcement learning strategies in cancer chemotherapy treatments: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107280. [PMID: 36529000 DOI: 10.1016/j.cmpb.2022.107280] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 11/20/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Cancer is one of the major causes of death worldwide and chemotherapies are the most significant anti-cancer therapy, in spite of the emerging precision cancer medicines in the last 2 decades. The growing interest in developing the effective chemotherapy regimen with optimal drug dosing schedule to benefit the clinical cancer patients has spawned innovative solutions involving mathematical modeling since the chemotherapy regimens are administered cyclically until the futility or the occurrence of intolerable adverse events. Thus, in this present work, we reviewed the emerging trends involved in forming a computational solution from the aspect of reinforcement learning. METHODS Initially, this survey in-depth focused on the details of the dynamic treatment regimens from a broad perspective and then narrowed down to inspirations from reinforcement learning that were advantageous to chemotherapy dosing, including both offline reinforcement learning and supervised reinforcement learning. RESULTS The insights established in the chemotherapy-planning problem associated with the Reinforcement Learning (RL) has been discussed in this study. It showed that the researchers were able to widen their perspectives in comprehending the theoretical basis, dynamic treatment regimens (DTR), use of the adaptive control on DTR, and the associated RL techniques. CONCLUSIONS This study reviewed the recent researches relevant to the topic, and highlighted the challenges, open questions, possible solutions, and future steps in inventing a realistic solution for the aforementioned problem.
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Affiliation(s)
- Chan-Yun Yang
- Department of Electrical Engineering, National Taipei University, New Taipei City, Taiwan
| | - Chamani Shiranthika
- Department of Electrical Engineering, National Taipei University, New Taipei City, Taiwan
| | - Chung-Yih Wang
- Department of Radiation Oncology, Cheng Hsin General Hospital, Taipei City, Taiwan
| | - Kuo-Wei Chen
- Section of Hematology and Oncology, Department of Internal Medicine, Cheng Hsin General Hospital, Taipei City, Taiwan.
| | - Sagara Sumathipala
- Faculty of Information Technology, University of Moratuwa, Katubedda, Moratuwa, Sri Lanka
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Ergun MA, Hajjar A, Alagoz O, Rampurwala M. Optimal breast cancer risk reduction policies tailored to personal risk level. Health Care Manag Sci 2022; 25:363-388. [PMID: 35687269 PMCID: PMC10445480 DOI: 10.1007/s10729-022-09596-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 03/17/2022] [Indexed: 11/04/2022]
Abstract
Depending on personal and hereditary factors, each woman has a different risk of developing breast cancer, one of the leading causes of death for women. For women with a high-risk of breast cancer, their risk can be reduced by two main therapeutic approaches: 1) preventive treatments such as hormonal therapies (i.e., tamoxifen, raloxifene, exemestane); or 2) a risk reduction surgery (i.e., mastectomy). Existing national clinical guidelines either fail to incorporate or have limited use of the personal risk of developing breast cancer in their proposed risk reduction strategies. As a result, they do not provide enough resolution on the benefit-risk trade-off of an intervention policy as personal risk changes. In addressing this problem, we develop a discrete-time, finite-horizon Markov decision process (MDP) model with the objective of maximizing the patient's total expected quality-adjusted life years. We find several useful insights some of which contradict the existing national breast cancer risk reduction recommendations. For example, we find that mastectomy is the optimal choice for the border-line high-risk women who are between ages 22 and 38. Additionally, in contrast to the National Comprehensive Cancer Network recommendations, we find that exemestane is a plausible, in fact, the best, option for high-risk postmenopausal women.
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Affiliation(s)
- Mehmet A Ergun
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, 3242 Mechanical Engineering Building, 1513 University Avenue, Madison, WI, 53706, USA
- Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Ali Hajjar
- Harvard Medical School, Boston, Massachusetts, Boston, USA
- Massachusetts General Hospital Institute for Technology Assessment, Boston, USA
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, 3242 Mechanical Engineering Building, 1513 University Avenue, Madison, WI, 53706, USA.
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Tunç S, Alagoz O, Burnside ES. A new perspective on breast cancer diagnostic guidelines to reduce overdiagnosis. PRODUCTION AND OPERATIONS MANAGEMENT 2022; 31:2361-2378. [PMID: 35915601 PMCID: PMC9313854 DOI: 10.1111/poms.13691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 01/19/2022] [Indexed: 06/15/2023]
Abstract
Overdiagnosis of breast cancer, defined as diagnosing a cancer that would otherwise not cause symptoms or death in a patient's lifetime, costs U.S. health care system over $1.2 billion annually. Overdiagnosis rates, estimated to be around 10%-40%, may be reduced if indolent breast findings can be identified and followed with noninvasive imaging rather than biopsy. However, there are no validated guidelines for radiologists to decide when to choose imaging options recognizing cancer grades and types. The aim of this study is to optimize breast cancer diagnostic decisions based on cancer types using a large-scale finite-horizon Markov decision process (MDP) model with 4.6 million states to help reduce overdiagnosis. We prove the optimality of a divide-and-search algorithm that relies on tight upper bounds on the optimal decision thresholds to find an exact optimal solution. We project the high-dimensional MDP onto two lower dimensional MDPs and obtain feasible upper bounds on the optimal decision thresholds. We use real data from two private mammography databases and demonstrate our model performance through a previously validated simulation model that has been used by the policy makers to set the national screening guidelines in the United States. We find that a decision-analytical framework optimizing diagnostic decisions while accounting for breast cancer types has a strong potential to improve the quality of life and alleviate the immense costs of overdiagnosis. Our model leads to a 20 % reduction in overdiagnosis on the screening population, which translates into an annual savings of approximately $300 million for the U.S. health care system.
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Affiliation(s)
- Sait Tunç
- Grado Department of Industrial and Systems EngineeringVirginia TechBlacksburgVirginiaUSA
| | - Oguzhan Alagoz
- Department of Industrial and Systems EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
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Advani S, Abraham L, Buist DS, Kerlikowske K, Miglioretti DL, Sprague BL, Henderson LM, Onega T, Schousboe JT, Demb J, Zhang D, Walter LC, Lee CI, Braithwaite D, O’Meara ES. Breast biopsy patterns and findings among older women undergoing screening mammography: The role of age and comorbidity. J Geriatr Oncol 2022; 13:161-169. [PMID: 34896059 PMCID: PMC9450010 DOI: 10.1016/j.jgo.2021.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/06/2021] [Accepted: 11/29/2021] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Limited evidence exists on the impact of age and comorbidity on biopsy rates and findings among older women. MATERIALS AND METHODS We used data from 170,657 women ages 66-94 enrolled in the United States Breast Cancer Surveillance Consortium (BCSC). We estimated one-year rates of biopsy by type (any, fine-needle aspiration (FNA), core or surgical) and yield of the most invasive biopsy finding (benign, ductal carcinoma in situ (DCIS) and invasive breast cancer) by age and comorbidity. Statistical significance was assessed using Wald statistics comparing coefficients estimated from logistic regression models adjusted for age, comorbidity, BCSC registry, and interaction between age and comorbidity. RESULTS Of 524,860 screening mammograms, 9830 biopsies were performed following 7930 exams (1.5%) within one year, specifically 5589 core biopsies (1.1%), 3422 (0.7%) surgical biopsies and 819 FNAs (0.2%). Biopsy rates per 1000 screens decreased with age (66-74:15.7, 95%CI:14.8-16.8), 75-84:14.5(13.5-15.6), 85-94:13.2(11.3,15.4), ptrend < 0.001) and increased with Charlson Comorbidity Score (CCS = 0:14.4 (13.5-15.3), CCS = 1:16.6 (15.2-18.1), CCS ≥2:19.0 (16.9-21.5), ptrend < 0.001).Biopsy rates increased with CCS at ages 66-74 and 75-84 but not 85-94. Core and surgical biopsy rates increased with CCS at ages 66-74 only. For each biopsy type, the yield of invasive breast cancer increased with age irrespective of comorbidity. DISCUSSION Women aged 66-84 with significant comorbidity in a breast cancer screening population had higher breast biopsy rates and similar rates of invasive breast cancer diagnosis than their counterparts with lower comorbidity. A considerable proportion of these diagnoses may represent overdiagnoses, given the high competing risk of death from non-breast-cancer causes among older women.
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Affiliation(s)
- Shailesh Advani
- Department of Oncology, Georgetown University, Washington, DC
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Diana S.M. Buist
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Karla Kerlikowske
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA,Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Diana L. Miglioretti
- Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, CA
| | - Brian L. Sprague
- Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT
| | | | - Tracy Onega
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH
| | | | - Joshua Demb
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Dongyu Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL
| | - Louise C. Walter
- Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine; Department of Health Services, University of Washington School of Public Health, Seattle, WA
| | - Dejana Braithwaite
- Department of Epidemiology, University of Florida, Gainesville, FL, United States of America; University of Florida Health Cancer Center, Gainesville, FL, United States of America; Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, United States of America.
| | - Ellen S. O’Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
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Diagnostic Policies Optimization for Chronic Diseases Based on POMDP Model. Healthcare (Basel) 2022; 10:healthcare10020283. [PMID: 35206897 PMCID: PMC8872177 DOI: 10.3390/healthcare10020283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/22/2022] [Accepted: 01/30/2022] [Indexed: 02/05/2023] Open
Abstract
During the process of disease diagnosis, overdiagnosis can lead to potential health loss and unnecessary anxiety for patients as well as increased medical costs, while underdiagnosis can result in patients not being treated on time. To deal with these problems, we construct a partially observable Markov decision process (POMDP) model of chronic diseases to study optimal diagnostic policies, which takes into account individual characteristics of patients. The objective of our model is to maximize a patient’s total expected quality-adjusted life years (QALYs). We also derive some structural properties, including the existence of the diagnostic threshold and the optimal diagnosis age for chronic diseases. The resulting optimization is applied to the management of coronary heart disease (CHD). Based on clinical data, we validate our model, demonstrate how the quantitative tool can provide actionable insights for physicians and decision makers in health-related fields, and compare optimal policies with actual clinical decisions. The results indicate that the diagnostic threshold first decreases and then increases as the patient’s age increases, which contradicts the intuitive non-decreasing thresholds. Moreover, diagnostic thresholds were higher for women than for men, especially at younger ages.
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Bansal A, Heagerty PJ, Inoue LYT, Veenstra DL, Wolock CJ, Basu A. A Value-of-Information Framework for Personalizing the Timing of Surveillance Testing. Med Decis Making 2021; 42:474-486. [PMID: 34747265 DOI: 10.1177/0272989x211049213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Patient surveillance using repeated biomarker measurements presents an opportunity to detect and treat disease progression early. Frequent surveillance testing using biomarkers is recommended and routinely conducted in several diseases, including cancer and diabetes. However, frequent testing involves tradeoffs. Although surveillance tests provide information about current disease status, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. Predictions based on patients' earlier biomarker values may be used to inform decision making; however, predictions are uncertain, leading to decision uncertainty. METHODS We propose the Personalized Risk-Adaptive Surveillance (PRAISE) framework, a novel method for embedding predictions into a value-of-information (VOI) framework to account for the cost of uncertainty over time and determine the time point at which collection of biomarker data would be most valuable. The proposed sequential decision-making framework is innovative in that it leverages the patient's longitudinal history, considers individual benefits and harms, and allows for dynamic tailoring of surveillance intervals by considering the uncertainty in current information and estimating the probability that new information may change treatment decisions, as well as the impact of this change on patient outcomes. RESULTS When applied to data from cystic fibrosis patients, PRAISE lowers costs by allowing some patients to skip a visit, compared to an "always test" strategy. It does so without compromising expected survival, by recommending less frequent testing among those who are unlikely to be treated at the skipped time point. CONCLUSIONS A VOI-based approach to patient monitoring is feasible and could be applied to several diseases to develop more cost-effective and personalized strategies for ongoing patient care. HIGHLIGHTS In many patient-monitoring settings, the complications and costs of frequent tests are not justified for patients who are at low risk of disease progression. Predictions based on patient history may be used to individualize the timing of patient visits based on evolving risk.We propose Personalized Risk-Adaptive Surveillance (PRAISE), a novel method for personalizing the timing of surveillance testing, where prediction modeling projects the disease trajectory and a value-of-information (VOI)-based pragmatic decision-theoretic framework quantifies patient- and time-specific benefit-harm tradeoffs.A VOI-based approach to patient monitoring could be applied to several diseases to develop more personalized and cost-effective strategies for ongoing patient care.
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Affiliation(s)
- Aasthaa Bansal
- The Comparative Health Outcomes, Policy, and Economics Institute, School of Pharmacy, University of Washington, Seattle WA, USA
| | | | - Lurdes Y T Inoue
- Department of Biostatistics, University of Washington, Seattle WA, USA
| | - David L Veenstra
- The Comparative Health Outcomes, Policy, and Economics Institute, School of Pharmacy, University of Washington, Seattle WA, USA
| | - Charles J Wolock
- Department of Biostatistics, University of Washington, Seattle WA, USA
| | - Anirban Basu
- The Comparative Health Outcomes, Policy, and Economics Institute, School of Pharmacy, University of Washington, Seattle WA, USA
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Choi M, Ishizawa S, Kraemer D, Sasson A, Feinberg E. Perioperative chemotherapy versus adjuvant chemotherapy strategies in resectable gastric and gastroesophageal cancer: A Markov decision analysis. Eur J Surg Oncol 2021; 48:403-410. [PMID: 34446344 DOI: 10.1016/j.ejso.2021.08.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/22/2021] [Accepted: 08/09/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Perioperative chemotherapy has been shown to improve overall survival (OS) for operable gastric and gastroesophageal cancer. However, optimal sequence of surgery and chemotherapy has not been clearly identified. Markov models are useful for analyzing the outcomes of different treatment strategies in the absence of adequately powered randomized clinical trials. In this study, we use Markov decision analysis models to compare median OS (mOS), quality-adjusted mOS, life expectancy (LE), and quality-adjusted life expectancy (QALE) of perioperative chemotherapy with adjuvant chemotherapy strategies in resectable gastric and gastroesophageal cancer patients. METHODS Markov models are constructed to compare two strategies: adjuvant chemotherapy after surgery and preoperative chemotherapy followed by cancer resection and postoperative chemotherapy. LE and QALE are calculated analytically, and mOS are obtained by simulation. Parameters used in the models are computed from prospective clinical trial data published in PUBMED from January 2000 to July 2020. RESULTS Total of 8088 patients from 25 prospective studies were included in this analysis. Regardless of R0 resection ratio, the analyses of the models show a higher mOS for patients in the perioperative therapy arm compared to adjuvant chemotherapy. For R0 resected patients, the perioperative therapy arm provided an additional 11.0 mOS months (61.3 months vs. 50.3 months). For R1 resected patients, the perioperative therapy arm had mOS of 17.0 months vs. 10.7 months in adjuvant therapy. CONCLUSIONS The Markov models indicate that perioperative chemotherapy improves mOS, quality-adjusted mOS, LE, and QALE for resectable gastric and gastroesophageal cancer patients compared to adjuvant chemotherapy strategies.
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Affiliation(s)
- Minsig Choi
- Department of Medicine, Stony Brook University, USA.
| | - Sayaka Ishizawa
- Department of Applied Mathematics and Statistics, Stony Brook University, USA
| | - David Kraemer
- Department of Applied Mathematics and Statistics, Stony Brook University, USA
| | - Aaron Sasson
- Department of Surgery, Stony Brook University, Stony Brook, NY, 11794-3600, USA
| | - Eugene Feinberg
- Department of Applied Mathematics and Statistics, Stony Brook University, USA
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Bai Y, Berg BP. Mitigating Nonattendance Using Clinic-Resourced Incentives Can Be Mutually Beneficial: A Contingency Management-Inspired Partially Observable Markov Decision Process Model. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2021; 24:1102-1110. [PMID: 34372975 DOI: 10.1016/j.jval.2021.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 03/22/2021] [Accepted: 03/29/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVES Nonattendance of appointments in outpatient clinics results in many adverse effects including inefficient use of valuable resources, wasted capacity, increased delays, and gaps in patient care. This research presents a modeling framework for designing positive incentives aimed at decreasing patient nonattendance. METHODS We develop a partially observable Markov decision process (POMDP) model to identify optimal adaptive reinforcement schedules with which financial incentives are disbursed. The POMDP model is conceptually motivated based on contingency management evidence and practices. We compare the expected net profit and trade-offs for a clinic using data from the literature for a base case and the optimal positive incentive design resulting from the POMDP model. To accommodate a less technical audience, we summarize guidelines for reinforcement schedules from a simplified Markov decision process model. RESULTS The results of the POMDP model show that a clinic can increase its net profit per recurrent patient while simultaneously increasing patient attendance. An increase in net profit of 6.10% was observed compared with a policy with no positive incentive implemented. Underlying this net profit increase is a favorable trade-off for a clinic in investing in a targeted contingency management-based positive incentive structure and an increase in patient attendance rates. CONCLUSIONS Through a strategic positive incentive design, the POMDP model results show that principles from contingency management can support decreasing nonattendance rates and improving outpatient clinic efficiency of its appointment capacity, and improved clinic efficiency can offset the costs of contingency management.
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Affiliation(s)
- Yunxiang Bai
- Division of Biostatistics, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Bjorn P Berg
- Division of Health Policy and Management, University of Minnesota, Twin Cities, Minneapolis, MN, USA.
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Zhuo Y, Solak S, Harmanli O, Jones KA. Optimal treatment policies for pelvic organ prolapse in women. DECISION SCIENCES 2021. [DOI: 10.1111/deci.12521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yueran Zhuo
- Isenberg School of Management University of Massachusetts Amherst Amherst Massachusetts USA
| | - Senay Solak
- Isenberg School of Management University of Massachusetts Amherst Amherst Massachusetts USA
| | - Oz Harmanli
- Department of Obstetrics, Gynecology, and Reproductive Sciences Yale School of Medicine New Haven Connecticut USA
| | - Keisha A. Jones
- Department of Obstetrics and Gynecology University of Massachusetts Medical School Baystate Medical Center Springfield Massachusetts USA
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Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases. Health Care Manag Sci 2021; 24:1-25. [PMID: 33483911 DOI: 10.1007/s10729-020-09537-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 11/30/2020] [Indexed: 12/25/2022]
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is among the leading causes of death in the US. Although research has shown that ASCVD has genetic elements, the understanding of how genetic testing influences its prevention and treatment has been limited. To this end, we model the health trajectory of patients stochastically and determine treatment and testing decisions simultaneously. Since the cholesterol level of patients is one controllable risk factor for ASCVD events, we model cholesterol treatment plans as Markov decision processes. We determine whether and when patients should receive a genetic test using value of information analysis. By simulating the health trajectory of over 64 million adult patients, we find that 6.73 million patients undergo genetic testing. The optimal treatment plans informed with clinical and genetic information save 5,487 more quality-adjusted life-years while costing $1.18 billion less than the optimal treatment plans informed with clinical information only. As precision medicine becomes increasingly important, understanding the impact of genetic information becomes essential.
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15
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Kazemian P, Helm JE, Lavieri MS, Stein JD, Van Oyen MP. Dynamic Monitoring and Control of Irreversible Chronic Diseases with Application to Glaucoma. PRODUCTION AND OPERATIONS MANAGEMENT 2019; 28:1082-1107. [PMID: 31485154 PMCID: PMC6724710 DOI: 10.1111/poms.12975] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
To manage chronic disease patients effectively, clinicians must know (1) how to monitor each patient (i.e., when to schedule the next visit and which tests to take), and (2) how to control the disease (i.e., what levels of controllable risk factors will sufficiently slow progression). Our research addresses these questions simultaneously and provides the optimal solution to a novel linear quadratic Gaussian state space model. For the objective of minimizing the relative change in state over time (i.e., disease progression), which is necessary for managing irreversible chronic diseases while also considering the cost of tests and treatment, we show that the classical two-way separation of estimation and control holds. This makes a previously intractable problem solvable by decomposition into two separate, tractable problems while maintaining optimality. The resulting optimization is applied to the management of glaucoma. Based on data from two large randomized clinical trials, we validate our model and demonstrate how our decision support tool can provide actionable insights to the clinician caring for a patient with glaucoma. This methodology can be applied to a broad range of irreversible chronic diseases to devise patient-specific monitoring and treatment plans optimally.
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Affiliation(s)
- Pooyan Kazemian
- Medical Practice Evaluation Center, Division of General Internal Medicine, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114
| | - Jonathan E Helm
- Operations and Decision Technologies, Kelley School of Business, Indiana University, 1309 E 10th St, Bloomington, IN 47405
| | - Mariel S Lavieri
- Department of Industrial and Operations Engineering, University of Michigan, 1205 Beal Ave, Ann Arbor, MI 48109
| | - Joshua D Stein
- Department of Ophthalmology and Visual Sciences, University of Michigan Medical School, 1000 Wall St, Ann Arbor, MI 48105
| | - Mark P Van Oyen
- Department of Industrial and Operations Engineering, University of Michigan, 1205 Beal Ave, Ann Arbor, MI 48109
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16
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Ayvaci MUS, Alagoz O, Ahsen ME, Burnside ES. Preference-Sensitive Management of Post-Mammography Decisions in Breast Cancer Diagnosis. PRODUCTION AND OPERATIONS MANAGEMENT 2018; 27:2313-2338. [PMID: 31031555 PMCID: PMC6481963 DOI: 10.1111/poms.12897] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Decision models representing the clinical situations where treatment options entail a significant risk of morbidity or mortality should consider the variations in risk preferences of individuals. In this study, we develop a stochastic modeling framework that optimizes risk-sensitive diagnostic decisions after a mammography exam. For a given patient, our objective is to find the utility maximizing diagnostic decisions where we define the utility over quality-adjusted survival duration. We use real data from a private mammography database to numerically solve our model for various utility functions. Our choice of utility functions for the numerical analysis is driven by actual patient behavior encountered in clinical practice. We find that invasive diagnostic procedures such as biopsies are more aggressively used than what the optimal risk-neutral policy would suggest, implying a far-sighted (or equivalently risk-seeking) behavior. When risk preferences are incorporated into the clinical practice, policy makers should bear in mind that a welfare loss in terms of survival duration is inevitable as evidenced by our structural and empirical results.
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Affiliation(s)
- Mehmet Ulvi Saygi Ayvaci
- Information Systems, Naveen Jindal School of Management, University of Texas at Dallas, 800 W Campbell Rd SM33, Richardson, Texas 75080, USA,
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin, Madison, Wisconsin 53705, USA,
| | - Mehmet Eren Ahsen
- Icahn School of Medicine at Mount Sinai, San Francisco, California 94108, USA,
| | - Elizabeth S Burnside
- Department of Radiology, University of Wisconsin, Madison, Wisconsin 53792, USA,
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17
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Crown W, Buyukkaramikli N, Sir MY, Thokala P, Morton A, Marshall DA, Tosh JC, Ijzerman MJ, Padula WV, Pasupathy KS. Application of Constrained Optimization Methods in Health Services Research: Report 2 of the ISPOR Optimization Methods Emerging Good Practices Task Force. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2018; 21:1019-1028. [PMID: 30224103 DOI: 10.1016/j.jval.2018.05.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 05/13/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity. OBJECTIVES In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available. CONCLUSIONS Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force's first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods.
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Affiliation(s)
| | - Nasuh Buyukkaramikli
- Institute of Medical Technology Assessment (iMTA), Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Mustafa Y Sir
- Health Care Policy and Research, Information & Decision Engineering, Mayo Clinic Kern Center for the Science of Health Care Delivery, Rochester, MN, USA
| | | | - Alec Morton
- Department of Management Science, Strathclyde Business School, University of Strathclyde, Glasgow, Scotland, UK
| | - Deborah A Marshall
- Health Services & Systems Research, University of Calgary, Calgary, Alberta, Canada; Alberta Bone & Joint Health Institute, Department Community Health Sciences, Faculty of Sciences, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada; Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | | | - Maarten J Ijzerman
- University of Twente, Department Health Technology & Services Research, Enschede, The Netherlands; Luxembourg Institute of Health, Health Economics and Evidence Synthesis Unit, Strassen, Luxembourg
| | - William V Padula
- Department of Health Policy & Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kalyan S Pasupathy
- Health Care Policy and Research, Information & Decision Engineering, Mayo Clinic Kern Center for the Science of Health Care Delivery, Rochester, MN, USA.
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Saville CE, Smith HK, Bijak K. Operational research techniques applied throughout cancer care services: a review. Health Syst (Basingstoke) 2018; 8:52-73. [PMID: 31214354 PMCID: PMC6507866 DOI: 10.1080/20476965.2017.1414741] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Revised: 12/01/2017] [Accepted: 12/05/2017] [Indexed: 01/22/2023] Open
Abstract
Cancer is a disease affecting increasing numbers of people. In the UK, the proportion of people affected by cancer is projected to increase from 1 in 3 in 1992, to nearly 1 in 2 by 2020. Health services to tackle cancer can be grouped broadly into prevention, diagnosis, staging, and treatment. We review examples of Operational Research (OR) papers addressing decisions encountered in each of these areas. In conclusion, we find many examples of OR research on screening strategies, as well as on treatment planning and scheduling. On the other hand, our search strategy uncovered comparatively few examples of OR models applied to reducing cancer risks, optimising diagnostic procedures, and staging. Improvements to cancer care services have been made as a result of successful OR modelling. There is potential for closer working with clinicians to enable the impact of other OR studies to be of greater benefit to cancer sufferers.
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Affiliation(s)
| | - Honora K Smith
- Mathematical Sciences, University of Southampton, Southampton, UK
| | - Katarzyna Bijak
- Southampton Business School, University of Southampton, Southampton, UK
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Güneş ED, Örmeci EL. OR Applications in Disease Screening. INTERNATIONAL SERIES IN OPERATIONS RESEARCH & MANAGEMENT SCIENCE 2018. [DOI: 10.1007/978-3-319-65455-3_12] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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20
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Nohdurft E, Long E, Spinler S. Was Angelina Jolie Right? Optimizing Cancer Prevention Strategies Among BRCA Mutation Carriers. DECISION ANALYSIS 2017. [DOI: 10.1287/deca.2017.0352] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Eike Nohdurft
- Kühne Institute for Logistics Management, WHU–Otto Beisheim School of Management, 56179 Vallendar, Germany
| | - Elisa Long
- UCLA Anderson School of Management, Los Angeles, California 90095
| | - Stefan Spinler
- Kühne Institute for Logistics Management, WHU–Otto Beisheim School of Management, 56179 Vallendar, Germany
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21
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Markov Decision Processes for Screening and Treatment of Chronic Diseases. INTERNATIONAL SERIES IN OPERATIONS RESEARCH & MANAGEMENT SCIENCE 2017. [DOI: 10.1007/978-3-319-47766-4_6] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
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22
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Gordon LG, James R, Tuffaha HW, Lowe A, Yaxley J. Cost‐effectiveness analysis of multiparametric MRI with increased active surveillance for low‐risk prostate cancer in Australia. J Magn Reson Imaging 2016; 45:1304-1315. [DOI: 10.1002/jmri.25504] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 09/21/2016] [Indexed: 12/31/2022] Open
Affiliation(s)
| | - Robbie James
- Griffith University, Centre for Applied Health Economics, Nathan CampusBrisbane Australia
| | - Haitham W. Tuffaha
- Griffith University, Centre for Applied Health Economics, Nathan CampusBrisbane Australia
| | - Anthony Lowe
- Prostate Cancer Foundation of Australia, St LeonardsSydney Australia
- Griffith University, Menzies Health Institute QueenslandSouthport Australia
| | - John Yaxley
- The Wesley Hospital, AuchenflowerBrisbane Australia
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Abstract
BACKGROUND Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. METHOD In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. RESULTS First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. CONCLUSIONS We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.
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Affiliation(s)
- Qiushi Chen
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA (QC, TA)
| | - Turgay Ayer
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA (QC, TA)
| | - Jagpreet Chhatwal
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, USA (JC).,Harvard Medical School, Boston, MA, USA (JC)
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Chhatwal J, Jayasuriya S, Elbasha EH. Changing Cycle Lengths in State-Transition Models: Challenges and Solutions. Med Decis Making 2016; 36:952-64. [PMID: 27369084 DOI: 10.1177/0272989x16656165] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2015] [Accepted: 05/09/2016] [Indexed: 01/30/2023]
Abstract
The choice of a cycle length in state-transition models should be determined by the frequency of clinical events and interventions. Sometimes there is need to decrease the cycle length of an existing state-transition model to reduce error in outcomes resulting from discretization of the underlying continuous-time phenomena or to increase the cycle length to gain computational efficiency. Cycle length conversion is also frequently required if a new state-transition model is built using observational data that have a different measurement interval than the model's cycle length. We show that a commonly used method of converting transition probabilities to different cycle lengths is incorrect and can provide imprecise estimates of model outcomes. We present an accurate approach that is based on finding the root of a transition probability matrix using eigendecomposition. We present underlying mathematical challenges of converting cycle length in state-transition models and provide numerical approximation methods when the eigendecomposition method fails. Several examples and analytical proofs show that our approach is more general and leads to more accurate estimates of model outcomes than the commonly used approach. MATLAB codes and a user-friendly online toolkit are made available for the implementation of the proposed methods.
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Affiliation(s)
- Jagpreet Chhatwal
- Institute for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA (JC)
| | - Suren Jayasuriya
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA (SJ)
| | - Elamin H Elbasha
- Merck Research Laboratories, Merck & Co., North Wales, PA, USA (EHE)
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25
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Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity. Health Care Manag Sci 2014; 18:363-75. [DOI: 10.1007/s10729-014-9304-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Accepted: 09/29/2014] [Indexed: 12/26/2022]
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26
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Tunc S, Alagoz O, Burnside E. Opportunities for Operations Research in Medical Decision Making. IEEE INTELLIGENT SYSTEMS 2014; 29:59-62. [PMID: 25598748 PMCID: PMC4296668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
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27
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Alagoz O, Chhatwal J, Burnside ES. Optimal Policies for Reducing Unnecessary Follow-up Mammography Exams in Breast Cancer Diagnosis. DECISION ANALYSIS 2013; 10:200-224. [PMID: 24501588 PMCID: PMC3910299 DOI: 10.1287/deca.2013.0272] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Mammography is the most effective screening tool for early diagnosis of breast cancer. Based on the mammography findings, radiologists need to choose from one of the following three alternatives: 1) take immediate diagnostic actions including prompt biopsy to confirm breast cancer; 2) recommend a follow-up mammogram; 3) recommend routine annual mammography. There are no validated structured guidelines based on a decision-analytical framework to aid radiologists in making such patient management decisions. Surprisingly, only 15-45% of the breast biopsies and less than 1% of short-interval follow-up recommendations are found to be malignant, resulting in unnecessary tests and patient-anxiety. We develop a finite-horizon discrete-time Markov decision process (MDP) model that may help radiologists make patient-management decisions to maximize a patient's total expected quality-adjusted life years. We use clinical data to find the policies recommended by the MDP model and also compare them to decisions made by radiologists at a large mammography practice. We also derive the structural properties of the MDP model, including sufficiency conditions that ensure the existence of a double control-limit type policy.
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Affiliation(s)
- Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, 1513 University Avenue, Madison, WI, 53705,
| | - Jagpreet Chhatwal
- Department of Health Policy and Management and Industrial Engineering, University of Pittsburgh, 130 De Soto Street Pittsburgh, PA, 15261,
| | - Elizabeth S Burnside
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792,
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28
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Sun JG, Adie SG, Chaney EJ, Boppart SA. SEGMENTATION AND CORRELATION OF OPTICAL COHERENCE TOMOGRAPHY AND X-RAY IMAGES FOR BREAST CANCER DIAGNOSTICS. JOURNAL OF INNOVATIVE OPTICAL HEALTH SCIENCES 2013; 6:1350015. [PMID: 24533035 PMCID: PMC3922042 DOI: 10.1142/s1793545813500156] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Pre-operative X-ray mammography and intraoperative X-ray specimen radiography are routinely used to identify breast cancer pathology. Recent advances in optical coherence tomography (OCT) have enabled its use for the intraoperative assessment of surgical margins during breast cancer surgery. While each modality offers distinct contrast of normal and pathological features, there is an essential need to correlate image-based features between the two modalities to take advantage of the diagnostic capabilities of each technique. We compare OCT to X-ray images of resected human breast tissue and correlate different tissue features between modalities for future use in real-time intraoperative OCT imaging. X-ray imaging (specimen radiography) is currently used during surgical breast cancer procedures to verify tumor margins, but cannot image tissue in situ. OCT has the potential to solve this problem by providing intraoperative imaging of the resected specimen as well as the in situ tumor cavity. OCT and micro-CT (X-ray) images are automatically segmented using different computational approaches, and quantitatively compared to determine the ability of these algorithms to automatically differentiate regions of adipose tissue from tumor. Furthermore, two-dimensional (2D) and three-dimensional (3D) results are compared. These correlations, combined with real-time intraoperative OCT, have the potential to identify possible regions of tumor within breast tissue which correlate to tumor regions identified previously on X-ray imaging (mammography or specimen radiography).
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Affiliation(s)
- Jonathan G Sun
- Beckman Institute for Advanced Science and Technology 405 North Mathews Avenue, Urbana, Illinois 61801, USA ; Department of Bioengineering, Beckman Institute for Advanced Science and Technology 405 North Mathews Avenue, Urbana, Illinois 61801, USA
| | - Steven G Adie
- Beckman Institute for Advanced Science and Technology 405 North Mathews Avenue, Urbana, Illinois 61801, USA
| | - Eric J Chaney
- Beckman Institute for Advanced Science and Technology 405 North Mathews Avenue, Urbana, Illinois 61801, USA
| | - Stephen A Boppart
- Beckman Institute for Advanced Science and Technology 405 North Mathews Avenue, Urbana, Illinois 61801, USA ; Department of Bioengineering, Beckman Institute for Advanced Science and Technology 405 North Mathews Avenue, Urbana, Illinois 61801, USA ; Departments of Electrical and Computer Engineering and Internal Medicine University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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29
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Armstrong K, Handorf EA, Chen J, Bristol Demeter MN. Breast cancer risk prediction and mammography biopsy decisions: a model-based study. Am J Prev Med 2013; 44:15-22. [PMID: 23253645 PMCID: PMC3527848 DOI: 10.1016/j.amepre.2012.10.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2012] [Revised: 09/27/2012] [Accepted: 10/02/2012] [Indexed: 01/25/2023]
Abstract
BACKGROUND Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening. PURPOSE To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and Data System]) above and below the threshold for breast biopsy. METHODS The current study modeled 1-year breast cancer risk in women with abnormal screening mammograms using existing data on breast cancer risk factors, 12 validated breast cancer single-nucleotide polymorphisms (SNPs), and probability of cancer given the BI-RADS category. Examination was made of reclassification of women above and below biopsy thresholds of 1%, 2%, and 3% risk. The Breast Cancer Surveillance Consortium data were collected from 1996 to 2002. Data analysis was conducted in 2010 and 2011. RESULTS Using a biopsy risk threshold of 2% and the standard risk factor model, 5% of women with a BI-RADS3 mammogram had a risk above the threshold, and 3% of women with BI-RADS4A mammograms had a risk below the threshold. The addition of 12 SNPs in the model resulted in 8% of women with a BI-RADS3 mammogram above the threshold for biopsy and 7% of women with BI-RADS4A mammograms below the threshold. CONCLUSIONS The incorporation of pre-test breast cancer risk factors could change biopsy decisions for a small proportion of women with abnormal mammograms. The greatest impact comes from standard breast cancer risk factors.
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Affiliation(s)
- Katrina Armstrong
- Department of Medicine, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA.
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30
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Underwood DJ, Zhang J, Denton BT, Shah ND, Inman BA. Simulation optimization of PSA-threshold based prostate cancer screening policies. Health Care Manag Sci 2012; 15:293-309. [PMID: 22302420 DOI: 10.1007/s10729-012-9195-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2011] [Accepted: 01/23/2012] [Indexed: 10/14/2022]
Abstract
We describe a simulation optimization method to design PSA screening policies based on expected quality adjusted life years (QALYs). Our method integrates a simulation model in a genetic algorithm which uses a probabilistic method for selection of the best policy. We present computational results about the efficiency of our algorithm. The best policy generated by our algorithm is compared to previously recommended screening policies. Using the policies determined by our model, we present evidence that patients should be screened more aggressively but for a shorter length of time than previously published guidelines recommended.
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Affiliation(s)
- Daniel J Underwood
- Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC 27695, USA.
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31
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What is the optimal threshold at which to recommend breast biopsy? PLoS One 2012; 7:e48820. [PMID: 23144986 PMCID: PMC3492229 DOI: 10.1371/journal.pone.0048820] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2012] [Accepted: 10/05/2012] [Indexed: 11/29/2022] Open
Abstract
Background A 2% threshold, traditionally used as a level above which breast biopsy recommended, has been generalized to all patients from several specific situations analyzed in the literature. We use a sequential decision analytic model considering clinical and mammography features to determine the optimal general threshold for image guided breast biopsy and the sensitivity of this threshold to variation of these features. Methodology/Principal Findings We built a decision analytical model called a Markov Decision Process (MDP) model, which determines the optimal threshold of breast cancer risk to perform breast biopsy in order to maximize a patient’s total quality-adjusted life years (QALYs). The optimal biopsy threshold is determined based on a patient’s probability of breast cancer estimated by a logistic regression model (LRM) which uses demographic risk factors (age, family history, and hormone use) and mammographic findings (described using the established lexicon–BI-RADS). We estimate the MDP model's parameters using SEER data (prevalence of invasive vs. in situ disease, stage at diagnosis, and survival), US life tables (all cause mortality), and the medical literature (biopsy disutility and treatment efficacy) to determine the optimal “base case” risk threshold for breast biopsy and perform sensitivity analysis. The base case MDP model reveals that 2% is the optimal threshold for breast biopsy for patients between 42 and 75 however the thresholds below age 42 is lower (1%) and above age 75 is higher (range of 3–5%). Our sensitivity analysis reveals that the optimal biopsy threshold varies most notably with changes in age and disutility of biopsy. Conclusions/Significance Our MDP model validates the 2% threshold currently used for biopsy but shows this optimal threshold varies substantially with patient age and biopsy disutility.
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32
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Chen Q, Ayer T, Nastoupil LJ, Seward M, Zhang H, Sinha R, Flowers CR. Initial management strategies for follicular lymphoma. Int J Hematol Oncol 2012; 1:35-45. [PMID: 23476737 PMCID: PMC3587762 DOI: 10.2217/ijh.12.7] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Follicular lymphoma (FL) can vary markedly in its initial presentation, and no single standard approach for its initial management has been adopted. Available options for the initial management of FL include watchful waiting, radiation, single-agent rituximab and combination of rituximab and chemotherapy with strategies segregated for patients who have low and high tumor burden disease based on established criteria. However, marked debate occurs regarding the role of watchful waiting in the modern era for low tumor burden, asymptomatic patients, the optimal timing of rituximab, the selection of chemotherapy regimen to partner with rituximab in high tumor burden patients, and strategies for the management of relapsed disease. We provide an evidence-based discussion on these and other issues regarding the management of FL, and propose a mathematical modeling approach for addressing some of these questions.
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Affiliation(s)
- Qiushi Chen
- H Milton Stewart School of Industrial & Systems Engineering , Georgia Institute of Technology, Atlanta, GA 30332, USA
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Ayvaci MUS, Alagoz O, Burnside ES. The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions. MANUFACTURING & SERVICE OPERATIONS MANAGEMENT : M & SOM 2012; 14:600-617. [PMID: 24027436 PMCID: PMC3767197 DOI: 10.1287/msom.1110.0371] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We develop a finite-horizon discrete-time constrained Markov decision process (MDP) to model diagnostic decisions after mammography where we maximize the total expected quality-adjusted life years (QALYs) of a patient under resource constraints. We use clinical data to estimate the parameters of the MDP model and solve it as a mixed-integer program. By repeating optimization for a sequence of budget levels, we calculate incremental cost-effectiveness ratios attributable to consecutive levels of funding and compare actual clinical practice with optimal decisions. We prove that the optimal value function is concave in the allocated budget. Comparing to actual clinical practice, using optimal thresholds for decision making may result in approximately 22% cost savings without sacrificing QALYs. Our analysis indicates short-term follow-ups are the immediate target for elimination when budget becomes a concern. Policy change is more drastic in the older age group with the increasing budget, yet the gains in total expected QALYs related to larger budgets are predominantly seen in younger women along with modest gains for older women.
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Affiliation(s)
- Mehmet U. S. Ayvaci
- Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, Wisconsin 53706
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, Wisconsin 53706
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Zhang J, Denton BT, Balasubramanian H, Shah ND, Inman BA. Optimization of PSA screening policies: a comparison of the patient and societal perspectives. Med Decis Making 2011; 32:337-49. [PMID: 21933990 DOI: 10.1177/0272989x11416513] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
OBJECTIVE To estimate the benefit of PSA-based screening for prostate cancer from the patient and societal perspectives. METHOD A partially observable Markov decision process model was used to optimize PSA screening decisions. Age-specific prostate cancer incidence rates and the mortality rates from prostate cancer and competing causes were considered. The model trades off the potential benefit of early detection with the cost of screening and loss of patient quality of life due to screening and treatment. PSA testing and biopsy decisions are made based on the patient's probability of having prostate cancer. Probabilities are inferred based on the patient's complete PSA history using Bayesian updating. DATA SOURCES The results of all PSA tests and biopsies done in Olmsted County, Minnesota, from 1993 to 2005 (11,872 men and 50,589 PSA test results). OUTCOME MEASURES Patients' perspective: to maximize expected quality-adjusted life years (QALYs); societal perspective: to maximize the expected monetary value based on societal willingness to pay for QALYs and the cost of PSA testing, prostate biopsies, and treatment. RESULTS From the patient perspective, the optimal policy recommends stopping PSA testing and biopsy at age 76. From the societal perspective, the stopping age is 71. The expected incremental benefit of optimal screening over the traditional guideline of annual PSA screening with threshold 4.0 ng/mL for biopsy is estimated to be 0.165 QALYs per person from the patient perspective and 0.161 QALYs per person from the societal perspective. PSA screening based on traditional guidelines is found to be worse than no screening at all. CONCLUSIONS PSA testing done with traditional guidelines underperforms and therefore underestimates the potential benefit of screening. Optimal screening guidelines differ significantly depending on the perspective of the decision maker.
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Affiliation(s)
- Jingyu Zhang
- Philips Research North America, Briarcliff Manor, NY (JZ)
| | - Brian T Denton
- Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC (BTD)
| | - Hari Balasubramanian
- Department of Mechanical & Industrial Engineering, University of Massachusetts, Amherst, MA (HB)
| | - Nilay D Shah
- Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN (NDS)
| | - Brant A Inman
- Department of Surgery, School of Medicine, Duke University, Durham, NC (BAI)
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Keren B, Pliskin JS. Optimal timing of joint replacement using mathematical programming and stochastic programming models. Health Care Manag Sci 2011; 14:361-9. [DOI: 10.1007/s10729-011-9172-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2011] [Accepted: 06/23/2011] [Indexed: 10/18/2022]
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