CSPH Works-in-Progress

CSPH Works-in-Progress
Cost-Effectiveness Analysis of Thromboprophylaxis for the Prevention of Venous Thromboembolism Associated with Major Urologic Cancer Surgery Ye Wang, PhD Center for Surgery and Public Health Thank you for being here today. It’s always nice to be able to get feedback from both within and without CSPH. Today I’ll be presenting about our work on evaluating the cost- effectiveness of thromboprophylaxis for the prevention of venous thromboembolism associated with major urologic cancer surgery. August, 2014

Presentation Overview
Background PhD Dissertation Project Current Project at the Center First of all, we will go through a little background of prophylaxis for venous thromboembolism and cost-effectiveness analysis. Then, I will touch upon my PhD dissertation project, because the techniques and model are related to the current projects I am working on at the Center for Surgery and Public Health. I will also present one of the projects I am doing at the Center at the moment, followed by the Q & A. Q & A

Background – Disease Burden in the US
Venous thromboembolism (VTE): Deep vein thrombosis (DVT) Pulmonary embolism (PE) Annual incidence: > 250,000 clinically evident cases ≈ 25,000 deaths per year Annual VTE-associated health care expenses: $1.9 to 4.2 billion > $ 5.0 billion in patients with cancers Prevention of post-surgical VTE in patients with cancers 1/3 deaths occur in patients undergoing invasive procedures Today, we will focus on venous thromboembolism, or VTE for short, which is composed of deep vein thrombosis (or DVT for short), and pulmonary embolism (or PE for short). Studies have shown that there is an annual incidence of more than 250,000 clinically evident cases of VTE in the US, leading to approximatedly 25,000 deaths per year. One-third of these deaths occur in patients undergoing invasive procedures. The annual VTE-associated health care expenses range from 1.9 to 4.2 billion, and increase to more than 5 billion in patients with cancers. All these data call for the prevention of post-surgical VTE in patients with cancers. ≤4cm Heit et al., Arch Intern Med 2008 Spyropoulos et al., J Manag Care Pharm 2007

Thromboprophylaxis for VTE
Thromboprophylaxis advocated for VTE according to the ACCP: ACCP = American College of Chest Physicians Mechanical device i.e., intermittent pneumatic compression Pharmacological agents i.e., injectable anticoagulants According to the ACCP, which is the American College of Chest Physicians, the thromboprophylaxis for VTE includes the mechanical device (i.e., intermittent pneumatic compression), and pharmacological agents (i.e., injectable anticoagulants). ≤4cm Gould et al., Chest 2012

Cost-Effectiveness of Thromboprophylaxis
Reduced incidence of VTE Increased costs While thromboprophylaxis may reduce the incidence of VTE, additional costs occur. For example, costs due to the thromboprophylaxis treatment itself and prophylaxis-induced complications. The question is: is it worthwhile to use thromboprophylaxis in order to reduce the incidence of VTE at the cost of increased consumption of limited health care resources. In other words, is it cost-effective to use thromboprophylaxis in patients at risk of VTE? However, the answer remains unknown. ≤4cm Thromboprophylaxis Complications

Cost-Effectiveness Analysis
The economic, clinical and humanistic outcomes model: Any disease management should aim to achieve balanced outcomes so that gains in one outcome would not sacrifice the opportunity gains in other outcomes and that the overall gains can be maximized and optimized. To answer this question, cost-effectiveness analysis is required. The basic concept underlying the cost-effectiveness analysis is based on the economic, clinical and humanistic outcomes model, which advocates that “Any disease management… [Read]”. ≤4cm Gunter, Am J Manag Care 1999

Cost-Effectiveness Analysis (continued)
Identify Measure Compare Costs (e.g., resource consumption) Consequences (e.g., clinical or humanistic outcomes) Cost-effectiveness analysis plays its role by identifying, measuring and comparing the costs (e.g., resource consumption) and consequences (e.g., clinical and humanistic outcomes) of different interventions in order to optimize the allocation of limited health care resources. Optimize the allocation of limited health care resources Gunter, Am J Manag Care 1999

Cost-Effectiveness Analysis (continued)
Four types of cost-effectiveness analysis: Cost-minimization analysis Cost-effectiveness analysis Cost-benefit analysis Cost-utility analysis Recommended: Quality-adjusted life years (QALYs) Comparisons across various diseases and health interventions Under the broad term of cost-effectiveness analysis, there are four types of such analysis, which are cost-minimization analysis, cost-effectiveness analysis, cost-benefit analysis and cost-utility analysis. The cost-utility analysis has been recommended, as it incorporates patient-reported utilities such as quality adjusted life years (or QALYs for short) and allows for comparison across different diseases and health interventions. Siegel et al., JAMA 1996

Incremental Cost-Effectiveness Ratio (ICER)
Direct Costs Procedures Hospitalization Follow-up Visits/Tests Complications Indirect Costs Lost Wages Lost Productivity Caregiving Non-Societal Costs Societal Costs $ (CostsIntervention B – CostsIntervention A) (EffectivenessIntervention B – EffectivenessIntervention A) The cost-effectiveness of health interventions can be represented by the incremental cost-effectiveness ratio (or ICER for short), which equals the difference in costs between two interventions divided by the difference in effectiveness between the two interventions. The costs can be direct and indirect costs. Direct costs include costs for procedures, hospitalization, follow-up visits/tests and complications. Indirect costs include costs due to lost wages, lost productivity and caregiving. Which costs to include in a cost-effectiveness analysis depends on the perspective of that analysis. If the analysis is conducted from the societal perspective, both direct and indirect costs should be included. If the analysis is conducted from other perspectives, such as that of a health care system which only reimburses direct costs, only direct costs should be accounted. Costs are always counted in the dollar form. Effectiveness refers to benefits gained. For example, improvement in physical health and mental health. In cost-utility analysis, effectiveness is measured by utilities, which are used to calculate QALYs. Utilities are the preference for health states. Usually, a utility value ranges from 1 to 0, with 1 and 0 representing the best possible health state and death, respectively. Utilities can be below zero to represent health states worse than death. QALYs (calculated by utilities) Benefits Physical Health Mental Health Siegel et al., JAMA 1996

Quality of Life (Utility)
Best possible health state 1 0.4 Symptomatic Metastatic Prostate Cancer 0.2 Above the Knee Amputation 0.6 Severe Congestive Heart Failure 0.8 Disability after Hip Fracture Now I’m going to use this scale to illustrate utilities. Usually, utilities range from 1 to 0, with 1 referring to the best possible health state, and 0 referring to death. Other health states rank along the scale in order of their utilities. Here are a few examples. There are situations where utilities for a health state is worse than death, i.e., the patient would rather die than stay in the health state. For these health states, utilities are below zero. Death Worst possible health state

QALYs Quality of Life (utilities) 0.5 One QALY 2 years
Then I will briefly illustrate the calculation of QALYs. The Y-axis is the quality of life, represented by utilities. The X-axis is time in terms of years. Let’s say, an individual stays in a health state with a one-year utility of 0.5 for 2 years. The QALY over the 2 years equals the 1-year utility multiplied by 2 years, i.e., 1 QALY – the area under the curve. One QALY 2 years

QALYs (continued) QALYs Quality of Life Gained (utilities) Death Death
Intervention B QALYs Gained Quality of Life (utilities) In this way, the QALYs from starting intervention A to death is the area under the green curve. The QALYs from starting intervention B to death is the area under the blue curve. The difference in the areas under the curve between the two interventions is the QALYs gained, which is the denominator of the ICER equation in a cost-utility analysis. Intervention A Death Death Time

Cost-Effectiveness Analysis
Cost-Effectiveness Plane ✗ Increased Cost ? Costs Money Worsens Health Cost Money Improves Health QALYs Gained ✔ Here is the cost-effectiveness plane. The horizontal line shows increased QALYs gained as it extends to the right and increased QALYs lost as it extends to the left. The vertical line shows increased cost as it extends to the upper end and decreased cost as it extends to the lower end. When a new intervention has lower cost and higher QALYs than its comparator [pointing to quadrant 4], the new intervention is cost-effective, as it not only saves money, but also improves health. We should adopt the new intervention. When a new intervention has higher cost and lower QALYs than its comparator [pointing to quadrant 2], the new intervention is not cost-effective, as it costs more money, but worsens health compared to its comparator. We should not adopt the new intervention. In some situations, a new intervention saves money [pointing to quadrant 3], however, it does not have better health outcomes or even worse health outcomes compared to its comparator. In most situations, a new intervention has better health outcomes [pointing to quadrant 1]; however, it also has higher costs than its comparator. In these two quadrants [pointing to quadrants 1 and 3], ICERs need to be calculated to determine if a new intervention should be adopted or not. QALYs Lost Saves Money Worsens Health Saves Money Improves Health ? Decreased Cost

Willingness-to-Pay (WTP) Thresholds
Interpreting ICER (US Perspective) Less than $50,000 per QALY gained Good Value $50,000 to $100,000 per QALY gained Sometimes Good Value Greater than $100,000 per QALY gained Rarely Good Value The willingness-to-pay (or WTP for short) thresholds can be used to interpret ICERs. The rule of thumb from the US perspective is that an ICER less than USD 50,000 per QALY gained indicates that the new intervention has good value, i.e., highly cost-effective. An ICER between USD 50,000 and 100,000 per QALY gained indicates that the new intervention has some good value, i.e., it may be cost-effective. An ICER greater than USD 100,000 per QALY gained indicates that the new intervention rarely has good value, i.e., it is unlikely to be cost-effective.

Decision Tree Recursive with multiple recurrences
No Event Well Fatal Anticoagulation Dead Embolus Non-Fatal Disabled In order to obtain ICERs, models are used. One type of the models of cost-effectiveness analysis is the decision tree. Here is an example. Patients taking anticoagulants may have no event, embolus or bleed, depending on probabilities. Patients with no event will stay in “well”, while patients with embolus or bleed will be either dead or disabled in the end. The rectangles represent the health states that patients will enter into at the end of the simulation. Decision trees are simple compared to other models. However, they have a few disadvantages. Decision trees become recursive when there are recurrent events. They are only useful to short-term simulations not lifetime simulations. And, it is difficult to assign utilities because utilities depend on time. The 1-year utility can be different from the 10-year utility for the same health state. However, decision trees do not have the time horizon. Fatal Dead Bleed Non-Fatal Disabled Recursive with multiple recurrences Useful to short-term simulations Difficult to assign utilities

t t+1 Markov Modeling Markov States WELL DISABLED DEAD DEAD DISABLED
(Cycle 0) (Cycle 1) To address the disadvantage of decision trees, Markov models are used. The ellipses here are Markov states. Markov models address the problem of recurrent events over a lifetime horizon by dividing the time into equal slots, represented by “cycles”. Patients can transit among the health states during each cycle and enter into a health state at the end of each cycle. DEAD DISABLED WELL t+1

Markov Modeling (continued)
Markov States WELL DISABLED DEAD t (Cycle 0) (Cycle 1) For example, before the simulation begins (i.e., cycle 0) at time t, a cohort of patients stay in the health state “well”. Then the simulation begins. In Cycle 1 at time t+1, a proportion of the cohort transit to “Well”, “disabled” or “dead”, depending on the probability of each path. The arrows show the possible paths. Patients will enter into the health states, which are pointed to by arrows at the end of each cycle. DEAD DISABLED WELL t+1

Markov States WELL DISABLED DEAD t Patients who are in the health state “disabled” in the previous time slot will transit to either “disabled” or “dead” in the next slot, depending on probabilities. DEAD DISABLED WELL t+1

Markov States WELL DISABLED DEAD t Patients who are in the health state “dead” will remain in “dead” for the remaining cycles, and cannot go back to “well” or “disabled”. DEAD DISABLED WELL t+1

Markov States WELL DISABLED DEAD In Markov models, events can recur at multiple times over a lifetime horizon. In addition, utilities can be explicitly elicited according to the cycle length. Events can recur Simulate over a lifetime horizon Utilities dependent on the cycle length

Markov States WELL DISABLED DEAD The major disadvantage of Markov models is the Markovian assumption, i.e., Markov models are memoryless. They do not remember the previous health state that patients transit from. For example, patients transit into “dead”. A Markov model does not tell if the patients transit from “well” or “disabled”. But there are ways to overcome this disadvantage. Markovian Assumption “memory-less”

PhD Dissertation Project
Part I. Patient-Reported Outcomes of Anticoagulants Psychometric properties (validation) of a medication adherence scale Evaluation of patients’ knowledge, satisfaction, and barriers to anticoagulant therapy … In order to better illustrate the cost-effectiveness analysis, I will go through two example, which are two studies of my dissertation project. My dissertation project was composed of two parts: patient-reported outcomes and pharmacoeconomics and anticoagulants. The 1st part had a few studies, including validation of a medication adherence scale, and evaluation of patients’ knowledge, satisfaction, and barriers to anticoagulant therapy. The 2nd part had two studies. I will briefly touch upon these two studies. Part II. Pharmacoeconomics of Anticoagulants Utility evaluation for anticoagulant-related outcomes Cost-effectiveness of oral anticoagulants for stroke prevention in patients with atrial fibrillation

Utility evaluation for anticoagulant-related outcomes
Study 1 Utility evaluation for anticoagulant-related outcomes The 1st study was utility evaluation for anticoagulant-related outcomes.

Study Design Study design: Cross-sectional patient survey Sample size:
Inclusion criteria ≥ 21 years old Taking warfarin Able to comprehend English or Chinese Utility elicitation methods: Standard gamble technique We conducted a cross-sectional patient survey in 100 patients. To be eligible, patients needed to be at least 21 years old, taking warfarin and able to comprehend English or Chinese. Utilities were elicited using the standard gamble technique.

Health States Seven long-term health states Well on warfarin
Well on dabigatran Well on rivaroxaban Major ischemic stroke Minor ischemic stroke Intracranial hemorrhage (ICH) Current health state We elicited the utilities for 7 long-term health states, and 4 short-term health states. These health states were selected based on their relevance to anticoagulation outcomes. Four short-term health states Transient ischemic attack (TIA) Major extracranial hemorrhage (ECH) Minor ECH Myocardial infarction (MI)

Health State Descriptions
Published literature Preference assessment guidelines Medical textbooks Expert opinions To elicit utilities for health states using the standard gamble technique, descriptions of health states are needed. The descriptions were developed based on preference assessment guidelines, published literature, medical textbooks and expert opinions. Gage et al., Arch Intern Med 1996 Torrance, J Health Econ 1986 Warrell et al., Oxford Textbook of Medicine 2003

Standard Gamble Technique
Methods – SG All health states were considered to be better than death: Choice 1: Staying in the health state under evaluation for the rest of the patient’s life Choice 2: p 1 - p Here is an illustration of the standard gamble technique. For long-term health states, if they are considered to be better than death, two choices are given: 1) staying in the health state under evaluation for the rest of the patient’s life, and 2) a gamble in which there was a probability (“p”) of living in perfect health and a probability (“1-p”) of immediate death. Probabilities of perfect health and immediate death are presented in a “ping-pong” approach that begins with a 100% chance of perfect health and a 0% chance of immediate death.

Standard Gamble Technique (continued)
All health states were considered to be better than death: p 1 - p Then, the probabilities are shifted to a 5% chance of perfect health and a 95% chance of immediate death.

Standard Gamble Technique (continued)
All health states were considered to be better than death: Indifferent – utility value p 1 - p Then the probabilities shifted again to a 95% chance of perfect health and a 5% chance of immediate death. The probabilities are shifted back and forth in this way until the patient feels indifferent to the two choices. The value of “p” derived at that point is the respondent’s utility value for that particular health state.

Results – A Brief Summary
Three best health states (mean ± SD) Well on rivaroxaban (0.90 ± 0.15) Well on warfarin (0.86 ± 0.17) Well on dabigatran (0.83 ± 0.18) Two health states worse than death (mean ± SD) ICH (-0.09± 0.51) Major ischemic stroke (-0.01 ± 0.53) Here is a brief summary of the results. We found that the three health states with the highest mean utility value were “well on rivaroxaban”, “well on warfairn” and “well on dabigatran”. There were two health states with mean utility values below zero, which were ICH and major ischemic stroke, indicating great impact of these health states on patients’ quality of life. ICH = intracranial hemorrhage; SD = standard deviation.

Study 2 Cost-effectiveness of oral anticoagulants for stroke prevention in patients with atrial fibrillation The 2nd study was a cost-utility analysis.

Methods – Treatment Options
Dabigatran 150 mg twice daily Dabigatran 110 mg twice daily Rivaroxaban once daily Adjusted-dose warfarin Four treatment options were included in this cost-effectiveness study.

Base case A hypothetical cohort of patients, who were: 65 years old
Newly diagnosed with atrial fibrillation Having no contraindications to anticoagulation The base case used a hypothetical cohort of patients, who were 65 years old, newly diagnosed with atrial fibrillation, and having no contraindications to anticoagulation.

Model Information Model type: Markov model Perspective:
The Singapore health care system Horizon: Lifetime Cycle length: Monthly We conducted a Markov model, from the perspective of the Singapore health care system, over lifetime with monthly cycles.

Model Information Outcomes: Direct medical costs QALYs ICERs
Willingness-to-pay (WTP) threshold: Singapore’s 2012 per-capita gross domestic product (SGD 65,000/QALY) Software: TreeAge Pro Suite 2013 (TreeAge Software, Inc., Williamstown, MA) The outcomes included direct medical costs, QALYs and ICERs. The willingness-to-pay (or WTP for short) threshold was set at SGD 65,000 per QALY gained, which is equal to Singapore’s 2012 per-capita gross domestic product. An ICER below the threshold would indicate that the treatment is cost-effective compared to its comparator. TreeAge was used for the model construction and data analysis.

Model Inputs Clinical inputs: Published clinical trials
Utility inputs: Patient survey Cost inputs: Hospital databases Clinical inputs were obtained from published clinical trials. Utility inputs were from patient-survey. Cost inputs were retrieved from hospital databases.

Markov Model This is the Markov model we built. There are four treatments [pointing to the left column]. These are the 7 health states that patients will transit to [pointing to the middle column]. All patients start from “Well with AF” and remain in this health state until one of the other six events occurs. Here is the branch from “well with AF” [pointing to the right column]. Other branches have a similar structure. The green circles are chance nodes, which indicate that the simulated cohort of patients will enter one of the following paths, depending on the probability of each path. The red triangles are terminal nodes, which indicate the end of a cycle. After each cycle, simulated patients enter into the next corresponding health state. AF = atrial fibrillation, ECH = extracranial hemorrhage, ICH = intracranial hemorrhage, MI = myocardial infarction, RIND = reversible ischemic neurological deficit, TIA = transient ischemic attack.

Markov Model (continued)
Here is an overview of the transition among the 7 health states, showing the possible transitions between health states. Patients entering “Death” will remain in this health state for the remaining cycles. AF = atrial fibrillation, ICH = intracranial hemorrhage, RIND = reversible ischemic neurological deficit.

Results – Base-Case Analysis
Rivaroxaban versus Warfarin: ICER = SGD 36,231/QALY Dominated Eliminated by extended dominance We found that the ICER of rivaroxaban versus warfarin was below the WTP threshold, indicating that rivaroxaban was cost-effective compared to warfarin. Rivaroxaban and warfarin had extended dominance over dabigatran 150 mg, which means that a combination of rivaroxaban and warfarin will either gain more QALYs with same or lower costs, or save money with same or higher QALYs gained compared to dabigatran 150 mg alone. Dabigatran 110 mg was dominated by warfarin, as it costs more money, but generates fewer QALYs than warfarin.

Results – One-Way Sensitivity Analysis
WTP threshold This is the tornado diagram showing the results of the one-way sensitivity analysis. The dotted line is the WTP threshold. Each bar shows the changes in the ICER according to the changes in the corresponding parameter values. The results are most sensitive to the bar on the top.

Results – Two-Way Sensitivity Analysis
Here is an example of the two-way sensitivity analyses we conducted. It shows that, as the cost of rivaroxaban increased, warfarin was the optimal therapy for patients with a low rate of ischemic stroke on warfarin, and dabigatran 150 mg was the optimal therapy for patients with a high rate of ischemic stroke on warfarin.

Results – Probabilistic Sensitivity Analysis
WTP threshold We run a probabilistic sensitivity analysis, using 10,000 Monte Carlo simulations. The results showed that Rivaroxaban and warfarin were cost-effective in 91.29% and 8.05% of the 10,000 iterations, respectively. Rivaroxaban and warfarin were cost-effective in 91.29% and 8.05% of the 10,000 iterations, respectively.

Results – A Brief Summary
Base-case analysis Rivaroxaban was the optimal choice compared to warfarin. The ICER of dabigatran 150 mg versus warfarin exceeded the WTP threshold. Dabigatran 110 mg was dominated by warfarin and rivaroxaban. Here is a brief summary of the results. We found that rivaroxaban was the optimal choice compared to warfarin, with an ICER below the WTP threshold. The results were further confirmed in the probabilistic sensitivity analysis. Probabilistic sensitivity analysis Using a WTP threshold of SGD 65,000/QALY, rivaroxaban and warfarin were cost-effective in 91.29% and 8.05% of the 10,000 iterations, respectively.

Current Project at the CSPH
Cost-Effectiveness Analysis of Thromboprophylaxis for the Prevention of Venous Thromboembolism Associated with Major Urologic Cancer Surgery After the background and the examples, here comes the project I am working on at the Center.

Urologic Cancer in the US
% Ranking 14.00 1st 4.50 6th 3.80 8th 0.50 25th 77.20 - The figure shows the percentage of new cancer cases in the US in 2014 from the SEER. Here are the four types of cancer that comprise urologic cancer. Prostate cancer ranks the 1st, accounting for 14% of all new cancer cases. Bladder, kidney and testicles cancers rank the 6th, 8th, and 25th, respectively. It shows that urologic cancer is quite common in the US. ≤4cm National Caner Institute, Surveillance, Epidemiology, and End Results (SEER) Program 2014

Effect of VTE in patients with urologic cancer
Prevention of post-surgical VTE in patients with urologic cancer This table summarizes the effect of VTE on 1-year mortality in patients with different types of cancer. It shows that the hazard ratios for patients with prostate, bladder and kidney cancers are relatively high across different stages compared to other cancers. Therefore, it is important to prevent post-surgical VTE in patients with urologic cancer. ≤4cm Lyman, Cancer 2011

Thromboprophylaxis for VTE (continued)
Paucity of studies on VTE in the urologic literature The ACCP recommendations for major urologic cancer surgery are extrapolated from General Surgery Ideal use of VTE prophylaxis remains unclear ACCP = American College of Chest Physicians However, due to the paucity of studies on VTE in the urologic literature, the ACCP recommendations for major urologic surgery are extrapolated from General Surgery. Ideal use of VTE prophylaxis remains unclear, as urologic procedures carry a unique postoperative VTE risk profile. ≤4cm Gould et al., Chest 2012

Effectiveness of Thromboprophylaxis for VTE
Study design: Retrospective data analysis (the Premier) Major urologic cancer surgery: Radical prostatectomy Radical nephrectomy Partial nephrectomy Radical cystectomy Therefore, recently, the research group led by Dr Chang conducted a study to investigate the effectiveness of thromboprophylaxis for the prevention of VTE associated with major urologic cancer surgery. It was a retrospective data analysis, using data from the Premier, which is a nationally representative dataset capturing 15% of inpatient hospital discharges (including billing and clinical data) from over 600 acute care hospitals in the US. This study evaluated effectiveness of the four major urologic cancer surgery [pointing at RP to RC], in adults admitted to hospital. Currently, we are updating the data for this study. If there is a trend that thromboprophylaxis may reduce the incidence of VTE, additional costs occur. For example, costs due to the thromboprophylaxis treatment itself and prophylaxis-induced complications. Therefore, a question will come into mind, i.e., is it worthwhile to use thromboprophylaxis in order to reduce the incidence of VTE at the cost of increased consumption of limited health care resources. In other words, is it cost-effective to use thromboprophylaxis in patients undergoing major urologic cancer surgery. However, the answer remains unknown. ≤4cm Inclusion criteria: Adults (≥18 years old) Admitted due to major urologic cancer surgery

Research Question Are thromboprophylaxis strategies cost-effective for the prevention of post-surgical VTE in patients with urologic cancer? Therefore, the research question is: Are thromboprophylaxis strategies cost-effective for the prevention of post-surgical VTE in patients with urologic cancer?

Methods – Treatment Options
Mechanical prophylaxis: Intermittent pneumatic compression (IPC) Pharmacological prophylaxis (injectable anticoagulants): Low dose unfractionated heparin (LDUH) Enoxaparin Dalteparin Tinzaparin Fondaparinux Argatroban Comparator: No prophylaxis In this project, we are going to evaluate the cost-effectiveness of a mechaniacal prophylaxis and 6 pharmacological prophylaxis, all of which are injectable anticoagulants, versus the comparator, i.e., no prophylaxis.

Base case A hypothetical cohort of patients, who are: 65 years old
Undergoing major urologic cancer surgery Radical prostatectomy Radical cystectomy Radical nephrectomy Partial nephrectomy In the base case, we use a hypothetical cohort of patients who are 65 years old, and undergoing major urologic surgery, including the 4 surgeries here.

Model Information Model type: Markov model Perspective: Societal
Horizon: Lifetime Cycle length: Monthly We have built a Markov model. And, we are going to perform the analysis from the societal perspective, over a lifetime horizon, using monthly cycles.

Model Information Outcomes: Direct & indirect medical costs QALYs
ICERs WTP threshold: US$50,000/QALY-gained Software: TreeAge Pro Suite 2014 (TreeAge Software, Inc., Williamstown, MA) The outcomes include direct and indirect medical costs and QALYs. ICERs will be calculated. The WTP threshold is set at USD50,000/QALY-gained. An ICER below the threshold would indicate that the prophylaxis strategy is cost-effective compared to no prophylaxis.

Model Inputs Clinical inputs: Published literature Utility inputs:
Cost inputs: The Perspective Database (Premier, Inc, Charlotte, NC) Clinical inputs will be obtained from the Perspective Database from the Premier. Utility inputs will be obtained from published literature. Cost inputs will also be retrieved from the Premier database. Direct medical costs include costs for each prophylaxis strategy and prophylaxis-induced minor and major complications. Indirect medical costs are the costs for health care for recovery. Direct medical costs: Prophylaxis strategy Complications (minor & major) Indirect medical costs: Health care for recovery

Markov Model Here is the model we have build so far. These are the 7 prophylaxis strategies that we are going to compared with the comparator, i.e., no prophylaxis [pointing to the left column]. There are 8 health states [pointing to the right column], representing the commonly seen events that may happen during the prophylaxis. All simulated patients start from the health state “well” (i.e., there is no VTE or bleeding), and remain in “well” cycle by cycle until one of the other events occurs. If so, a proportion of the simulated cohort will transit to the next corresponding health state in the next cycle. CTPH = chronic thromboembolic pulmonary hypertension; DVT = deep vein thrombosis; IPC = intermittent pneumatic compression; LDUH = low dose unfractionated heparin; PE = pulmonary embolism; PTS = postthrombotic syndrome.

Markov Model (continued)
The bubble diagram here gives us an overview of the transitions among health states. All patients start from “Well”. During the 1st cycle, a proportion of simulated patients will transit to “DVT”, “PE” or “Non-Fatal Major Bleeding”. The rest of the cohort will transit to “Dead”. During the 2nd cycle, the cohort will further transit to different health states, depending on the probabilities of each path. For example, patients in the health state “DVT” will stay in “DVT”, propagate to “PTS”, “PE”, or die, depending on the probability of each path. The transitions continue until all simulated patients enter into “Dead”. CTPH = chronic thromboembolic pulmonary hypertension; DVT = deep vein thrombosis; PE = pulmonary embolism; PTS = postthrombotic syndrome.

Further work (specific to this project)
Collect Data: Costs Utilities Probabilities Perform data analyses: Base-case analysis Sensitivity analyses (one-way, multiple-way and probabilistic sensitivity analyses) Further work specific to this project includes data collection and analysis. We will collect the cost utility and probability data to populate the model. Then, we will conduct both base-case and sensitivity analyses, including one-way, multiple-way and probabilistic sensitivity analyses.

Thanks to Mentor & Other Staff at the CSPH
Thank you! Questions and Comments Lastly, I would like to thank my mentor for giving me the opportunity to be here. I would also like to thank all the other staff at the Center for their support. Also, many thanks to everyone for being here and listening to me presentation. THANK YOU!!!

An Example of health state descriptions
Methods – Health state descriptions (cont’d) One side of your body is totally paralyzed and/or one side of your face droops. You are not able to walk or take care of yourself (e.g., bathing, dressing and feeding) without help. You are not able to perform most of your usual activities. Your speech is unclear, and people have difficulty understanding you. You find it hard to write, but you may think clearly. Major ischemic stroke The descriptions for each health state consisted of one to six bullet points that described the health state’s important attributes.

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