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ScienceDirect journal homepage: www.JournalofSurgicalResearch.com
Cost-utility analysis of negative pressure wound therapy in high-risk cesarean section wounds Haitham W. Tuffaha,a,b,c,* Brigid M. Gillespie, PhD,a,c Wendy Chaboyer,a,c Louisa G. Gordon, PhD,a,b and Paul A. Scuffhama,b a
Griffith Health Institute, Griffith University, Gold Coast, Queensland, Australia Centre for Applied Health Economics, School of Medicine, Griffith University, Meadowbrook, Queensland, Australia c NHMRC Centre of Research Excellence in Nursing Interventions for Hospitalised Patients, Research Centre for Health Practice Innovation, Griffith University, Gold Coast, Queensland, Australia b
article info
abstract
Article history:
Background: Obese women undergoing cesarean section are at increased risk of post-
Received 29 October 2014
operative infection. There is growing interest in negative pressure wound therapy (NPWT)
Received in revised form
to prevent closed surgical incision complications including surgical site infection; however,
14 January 2015
the evidence on the effectiveness and cost-effectiveness of this technology is limited. The
Accepted 6 February 2015
objective of this study was to evaluate the cost-effectiveness of NPWT compared with that
Available online xxx
of standard dressing in preventing surgical site infection in obese women undergoing elective cesarean section based on current evidence and to estimate the value and optimal
Keywords:
design of additional research to study this technology.
Cost-effectiveness
Methods: The analysis was from the perspective of Queensland Health, Australia, using a
Negative pressure wound therapy
decision model. Parameters were obtained from the published literature, a pilot clinical
Cesarean section
trial, and expert opinion. Monte Carlo simulation was performed to calculate the net
Value of information
monetary benefit, characterize decision uncertainty, and estimate the value of additional research. Comparing the expected monetary benefits and costs of alternative trial sample sizes informed the optimal future study design. Results: The incremental net monetary benefit of NPWT was Australian dollars 70, indicating that NPWT is cost-effective compared with that of standard dressing. The probability of NPWT being cost-effective was 65%. The estimated value of additional research to resolve decision uncertainty would be Australian dollars 2.7 million. The optimal sample size of a future trial investigating the relative effectiveness of NPWT would be 200 patients per arm. Conclusions: Based on the current evidence, NPWT is cost-effective; however, there is high uncertainty surrounding the decision to adopt this technology. Additional research is worthwhile before implementation. ª 2015 Elsevier Inc. All rights reserved.
1.
Introduction
The increasing prevalence of obesity in women of childbearing age is a major health problem. Studies from the United States,
England, and Australia reported around 25% of women of childbearing age are obese with a body mass index (BMI) of 30 kg/m2 [1e4]. Maternal obesity poses serious complications during and after pregnancy to both the affected mothers and
* Corresponding author. Centre for Applied Health Economics, School of Medicine, Griffith Health Institute, Griffith University, Queensland 4131, Australia. Tel.: þ61 7 338 21510; fax: þ61 7 338 21338. E-mail address: [email protected] (H.W. Tuffaha). 0022-4804/$ e see front matter ª 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jss.2015.02.008
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their babies, including gestational diabetes, hypertensive disorders of pregnancy, and stillbirth [5]. Obesity also increases the need for cesarean delivery with the risk of a cesarean section (CS) being two to three times higher among obese compared with pregnant women of normal weight [6e8]. Obese women undergoing CS are at increased risk of complications particularly postoperative infection [5,9]. In a metaanalysis of six studies, the pooled odds ratio for obese CS women having an infection was three times higher compared with that for nonoverweight women [5]. A common postoperative complication is surgical site infection (SSI), which occurs after surgery in the area of the body where the surgery took place [10]. Controlling SSI is a health-care quality indicator because it results in significant morbidity, reduced quality of life, occasional death, and increased costs [11e13]. One case of SSI may cost up to $30,000, depending on its severity [12,14,15]. Despite the advances in infection control practices, ventilation systems in the operating rooms, sterilization methods, surgical technique, preoperative antimicrobial prophylaxis, and wound dressings, SSI remains common in obese women undergoing CS with an estimated incidence between 16 and 30% [11,16,17]. Since its introduction two decades ago, negative pressure wound therapy (NPWT) has been used to promote the healing of acute and chronic wounds as well as skin grafts (Table 1) [19e21]. It is based on a closed sealed system that applies negative pressure to the wound surface resulting in increased blood circulation, decreased edema, enhanced granulation tissue formation, and reduced bacterial colonization [21,22]. There is growing interest in extending the use of NPWT to closed surgical incision to prevent wound complications including SSI [21,22]. Unfortunately, the available evidence on the effectiveness and cost-effectiveness of NPWT in surgical incisions is limited [23]. This is expected in surgical practice, where innovations in technologies and equipment often outpace supporting evidence. Recent systematic reviews have identified three small randomized controlled trials (RCTs) that investigated the incidence of SSI in NPWT compared with that of standard wound dressing [22,23]. Those trials showed a reduction in SSI with NPWT although all trials reported that the reductions were not statistically significant [23e26]. None of the trials involved patients undergoing CS.
Given the cost of NPWT can reach $100 a day, it is essential to evaluate the cost-effectiveness of this technology before its wide implementation. Nevertheless, with the limitations in the available evidence, the results of a cost-effectiveness analysis may not be certain enough to inform a decision. Clearly, conducting additional research would reduce this uncertainty and better inform decisions. But, there is a cost associated with obtaining further evidence in terms of the direct costs of conducting clinical trials and the opportunity cost of delaying the implementation of an effective intervention awaiting research results. An analytical approach known as value of information analysis has been developed and used in health-care interventions to inform whether the available evidence is sufficient to support a decision on a given technology or if additional research study is worthwhile [27,28]. It is based on the notion that information is valuable because it reduces the uncertainty surrounding the available evidence and subsequently the potential cost of making wrong decisions based on that uncertain evidence [27,28]. In other words, the expected value of information is the expected cost of error. Furthermore, value of information analysis has been proposed as an alternative to the standard hypothesis testing approach, which is based on type 1 and type 2 error and the minimum clinically important difference, in determining sample sizes for RCTs [29e31]. Under this economic approach, researchers consider the sample sizes that maximize the expected net benefit of research, which is the difference between the expected monetary benefit of a given trial design and its expected cost [29,30]. The aim of this study was to conduct a cost-effectiveness analysis of NPWT in preventing SSI in obese women undergoing CS compared with that of standard dressing based on currently available evidence and to perform a value of information analysis to estimate the value and optimal sample size of a larger RCT to support this technology.
2.
Methods
The approach to achieve the study aim was to: 1) conduct a cost-effectiveness analysis of NPWT compared with standard dressing using a decision analytic model and; 2) perform
Table 1 e Description of commonly used negative pressure devices [18]. Productname Manufacturer Clinical indications
Pressure settings Therapy duration, d Cost
Prevena Kinetic Concepts Inc Chronic wounds Acute wounds Traumatic wounds Subacute wounds Dehisced wounds Partialthickness burns Flaps and grafts 75 to 125 mm Hg 7 AUD875
VAC-VIA Kinetic Concepts Inc Clean, closed incisions that continue to drain after closure.
125 mm Hg 2e7 AUD395
mm HG ¼ millimeter mercury.
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PICO Smith and Nephew Acute Flaps and grafts Incision sites Partial thickness burns Subacute wounds Traumatic Ulcers (e.g.,pressure) 80 mm Hg 5 AUD175
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Monte Carlo simulation to characterize decision uncertainty and estimate the expected value of additional research.
2.1.
Cost-effectiveness analysis
The cost-effectiveness analysis was from the perspective of the State Department of Health in Queensland, Australia, using a decision model. The model was probabilistic with prior distributions assigned to input parameters. We used Monte Carlo simulation to sample from the input distributions to estimate the expected costs and effects associated with each intervention [32]. In general, beta distributions were assigned to probabilities and utilities, gamma distributions to costs and disutilities, and lognormal distributions to relative risks (RRs). For this analysis, the efficacy outcome was qualityadjusted life-years (QALYs) gained. The net monetary benefit (NB) was calculated, which is the efficacy multiplied by the willingness-to-pay threshold for additional unit of effect outcome minus the cost [33]. We set the willingness-to-pay threshold at Australian dollars (AUD) 50,000 per QALY [34]. The intervention expected to be cost-effective would be the one with the highest expected monetary net benefit. Costs and net benefits were presented in AUD. The time horizon of the model was 6 mo to allow for sufficient time to capture and treat post-CS complications. Costs and effects were not discounted because the model timeline was 30 and comorbidities) undergoing a range 437 of procedures including abdominal surgeries; 6.8% of the 438 NPWT group and 13.5% of the standard dressing group 439 developed wound infection with an RR of 0.50 (95% CI 440 0.13e1.95). Other trials identified were an RCT by Howell et al. 441 [24] on NPWT in knee surgery that was terminated early due to 442 443 blister formation, and an RCT by Stannard et al. [26] investi444 gating NPWT in 249 patients with lower extremity trauma 445 fractures. In that RCT, around 10% of wounds in the NPWT 446 group had infection compared with 20% in the standard 447 dressing group at an RR of 0.52 (95% CI 0.28e0.96) [26]. It was 448 not appropriate to combine the results from Stannard et al. 449 with Masden et al. because the analysis in the former was per 450 wound and not per patient. Furthermore, patient character451 istics and wound types in the two studies were heteroge452 neous. Given the scarcity in the available evidence and in 453 454 order not to overestimate uncertainty in the relative
effectiveness parameter by relying on the pilot study results alone, the RR from the pilot study was collated with the RR from Masden et al. This was achieved by undertaking a Bayesian approach under which the RR from Masden et al. (i.e., prior information) was updated with the RR from the pilot trial resulting in an updated (i.e., posterior) RR of 0.73 (0.39e1.32) [44]. The effect of RR estimation on the results of the costeffectiveness and value of information analyses was explored in sensitivity analysis. The probability for deep/organ SSI was estimated at 19% from Wilson et al. [13] and Henman et al. [37]. The probability of death from deep/organ SSI was set at 0.07 and for superficial SSI at 0.02, from Astagneau et al. [39] and Kirkland et al. [38].
2.1.2.2. Costs. The cost of NPWT was set at AUD175 for the price of a disposable (one-application) device (PICO). The cost of standard dressing was AUD7.5 for the hydrocolloid dressing (Comfeel Plus). The cost of treating superficial SSI was obtained from Graves et al. [42] and was set at AUD250; this includes the cost of a general practitioner visit, 7 d of oral antibiotic, and the cost of test and/or swab. For the cost of deep/organ SSI, this was obtained from the 2009e2010 Australian Refined Diagnosis Related Groups, item T61 (postoperative and posttrauma infection) at AUD10,000 [43]. This includes the cost of hospitalization, tests and/or swabs, and intravenous antibiotics for 7e14 d [43]. The estimated staff time was 10 min to apply the NPWT and 2 min for the standard dressing at an average wage of AUD33 per hour [45]. Costs obtained in other price years were converted to 2014 AUD using the CCEMG-EPPI-Centre Cost Converter Web-based tool [46]. 2.1.2.3. Utilities. The utilities in the model were based on EuroQoL 5D (EQ-5D-3L) scores, anchored between 0.0 for death and 1.0 for best possible health. Utility weights were based on the preferences of the Australian population. The utility scores for the women undergoing CS and discharged with no complications was set at 0.9 from Clemens et al. [40]. For the
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women who developed SSI, the disutility for superficial and deep/organ SSI was set at 0.2 and 0.4, respectively, from Lipsky et al. [41]. The assumption was that the disutility will take place over 1 wk for superficial SSI and 2 wk for deep/organ SSI.
2.2.
Value of information analysis
The detailed algorithms for value of information calculation are described in the literature [47,48] and are presented in Appendix 1. Briefly, the first measure to calculate was the expected value of perfect information (EVPI). This is the value of the additional information that would resolve all uncertainty surrounding “all” input parameters; therefore, it is the maximum (upper bound) value for conducting further research to resolve this uncertainty [44]. The EVPI is the difference between the expected NB of a decision with perfect information and the decision made based on current information [49]. To estimate the EVPI, 10,000 Monte Carlo iterations were randomly sampled from the prior parameter distributions of all parameters to identify the intervention with the highest expected mean NB based on current information (i.e., the cost-effective intervention). Then the intervention with the highest NB “at each iteration” was identified and the identified values were averaged to calculate the expected “maximum” NB (i.e., NB with perfect information). If the EVPI exceeds the expected cost of future research, the next step would be to calculate the value of information to resolve the uncertainty in the parameter(s) of interest, which is the expected value of perfect parameter information (EVPPI) [50,51]. Since our model (i.e., the decision tree) is linear and assuming no correlation between input parameters because they were obtained from various sources, the same one-level Monte Carlo simulation technique described previously was used to calculate the EVPPI; the sampling would be only from the distribution of the “parameter(s) of interest,” whereas the other parameters were fixed at their prior means [51]. To estimate the value of a future clinical trial with a given sample size (n) that could reduce uncertainty surrounding the parameter of interest, the expected value of sample information (EVSI) was estimated by calculating the difference between the expected value of a decision made after collecting data on the parameter of interest and the expected net benefit with current information [47]. Conceivably, the data collected from additional research are not known at this stage but could be predicted by simulation. Given the linearity of the model, calculating the EVSI required the same one-level Monte Carlo simulation. However, the sampling would be from the posterior distribution of the parameter(s) of interest obtained using Bayesian updating [47]. The value of information measures described previously are per-patient estimates; however, it is necessary to estimate the value of information for the population of patients expected to benefit from the research outcomes. This was calculated by multiplying the per-patient estimates by the estimated number of patients expected to benefit from NPWT over a certain period. Obese women undergoing CS in Queensland represent 20% of the 20,000 CS performed every year in that state [43,52]. Accordingly, we estimated the expected number of obese women undergoing CS over 10 y (with 5% discounting) to be around 35,000.
To determine the optimal sample size of a future trial, the population-EVSI and the expected total cost were estimated for a range of possible trial sample sizes. The difference between the expected monetary benefit of research and the total cost of a particular study design is the expected net benefit of sampling (ENBS) [31,53]. The total cost of a future trial design included fixed costs (e.g., set-up cost, salaries), variable costs per patient, and the opportunity costs expected to be incurred by patients who would receive the inferior intervention during the trial [47,53]. We based our estimates for the cost of a future trial design on a research grant application for an RCT on NPWT in four institutions with a recruitment rate of 200 patients per site each year (Table 3). The estimated fixed project cost was AUD125,000 per year for project management and data analysis, plus an annual cost of AUD 100,000 per site for recruitment and data collection. The cost per patient was set at AUD250. If the ENBS is negative, additional research would not be cost-effective because the expected costs of the study would exceed its expected benefits. Conversely, a positive ENBS indicates that future research would be worthwhile. The optimal sample size is determined when the ENBS reaches a maximum [54,55].
3.
Results
3.1.
Cost-effectiveness analysis
Compared with standard dressing, NPWT resulted in an average additional cost of AUD30 (AUD600 versus AUD570) and additional 0.002 QALYs (Table 4). At a willingness-to-pay threshold of AUD50,000 per QALY, the incremental NB was 70AUD, indicating that NPWT is cost-effective. The probability of NPWT being cost-effective was 65%. Figure 3 shows the probability of NPWT being cost-effective over a range of willingness-to-pay thresholds.
3.2.
Value of information analysis
The EVPI for the decision of adopting NPWT is AUD76 per patient, which is AUD2.7 million (AUD76 35,000) for the population expected to benefit from this technology over the coming 10 y. The parameter with the highest value of information was the RR of SSI with NPWT at AUD75 per patient and population value of AUD2.6 million. The value of a future RCT exploring the relative effectiveness of NPWT over a range of
Table 3 e Research cost breakdown. Item Fixed costs Data management/y Project management/y Office supplies/y Field expenses (i.e., site visits and monitoring)/site/y Recruitment and data collection salaries/site/y Blinded outcome assessor/site/y Variable cost (i.e., per patient) Equipment Randomization services
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Cost (AUD) 30,000 85,000 10,000 15,000 70,000 15,000 200 50
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Table 4 e Cost-effectiveness analysis results. Analysis results
Standard dressing
NPWT
Difference
Cost (AUD) Effect (QALY) NB* (AUD)
570 0.446 21,730
600 0.448 21,800
30 0.002 70
*
For a willingness-to-pay threshold of AUD50,000 per QALY.
sample sizes per arm is depicted in Figure 3. As the sample size increases more uncertainty is expected to resolve and the value of additional research increases. Comparing the expected monetary benefits and costs of the suggested sample sizes, the optimal sample size would be 200 patients in each arm with an ENBS of AUD1.2 million at a total cost of AUD900,000 (Table 5). The expected return on investment (i.e., net benefit/cost ratio) would be 133% (AUD1.2million/ AUD900,000). The initial design with 400 patients per arm would provide a return on investment of 66% (AUD935/ AUD1.4million). In a sensitivity analysis, increasing the price of NPWT, varying willingness-to-pay threshold, extending the timeline of the technology, or estimating the RR based on the pilot trial alone resulted in an estimated optimal sample size between 200 and 300 patients in each arm (Table 6). On the other hand, with reduced NPWT price and shorter technology life-time the estimated sample sizes ranged between100 and 200 patients in each arm.
4.
Discussion
The use of NPWT in closed surgical wounds to enhance healing by primary intention and to prevent wound
complications is a new field of application for this technology. This article presents a cost-effectiveness analysis of NPWT in preventing SSI in obese women undergoing CS. Based on the current evidence, NPWT appears to be cost-effective compared with standard dressing with an expected incremental NB of AUD70. Nevertheless, the probability of NPWT being cost-effective is only 65%, indicating high decision uncertainty and thus high chance of error in a decision based on this cost-effectiveness analysis. Given the high cost of NPWT and the high uncertainty in the cost-effectiveness results, it would be reasonable to conduct additional research before implementing this technology. The expected value of information to resolve the uncertainty in the available evidence would be around AUD2.7 million, suggesting that additional research is potentially worthwhile. Our analysis estimates the optimal sample size for a future trial investigating the relative effectiveness of NPWT compared with standard dressing in reducing SSI. By calculating the expected monetary benefit (i.e., the expected reduction in uncertainty) of additional sampling and the expected cost of conducting this future trial, the sample size with the highest benefit-to-cost ratio would be 200 patients in each arm. This sample size is lower than the sample size of 400 patients in each arm initially calculated based on hypothesis testing and this smaller sample size would be more economical providing higher return on investment (133% versus 66%). The results demonstrate how value of information analysis can provide an alternative to the standard hypothesis testing approach, which relies on arbitrary chosen error probabilities where type 1 and type 2 error receive the same weight (e.g., 5% and 20%, respectively), regardless of the consequences of making an error [30]. Under value of information analysis, an economic approach is applied to sample size estimation. This approach considers a number of factors
Fig. 2 e The probability of each intervention being cost-effective over a range of willingness-to-pay thresholds. (Color version of figure is available online.) 5.2.0 DTD YJSRE13128_proof 28 February 2015 1:25 pm ce
Q9
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Fig. 3 e The expected value of information and expected cost across future trial sample sizes. (Color version of figure is available online.)
such as the relative effectiveness and costs of the evaluated technologies, the decision maker’s willingness-to-pay for the additional effectiveness, the probability and consequences of making a suboptimal decision, the population expected to benefit from research findings, the level of implementation (i.e., uptake) of research findings, and the total cost associated with the intended research [54]. Of note, the total cost of research does not only include the direct cost of research in terms of fixed and variable costs but also the opportunity cost from delaying the implementation of the technology awaiting the conclusion of the future trial [27,56]. In addition to sample-size calculation, value of information analysis can optimize other aspects of trial design such as the number of comparators and follow-up duration [29,56]. Obviously, more uncertainty is expected to resolve with longer
follow-up and more comparator arms albeit with additional research costs. Accordingly, the preferred design would be the one that optimizes the expected research monetary benefits compared with the expected research costs [53]. The same principle can be extended to quantitatively prioritize research. Under the value of information framework, competing research proposals within a limited budget could be ranked according to their expected return on investment [53]. To our knowledge, there is no published cost-effectiveness analysis of NPWT in preventing wound complications in closed surgical incisions [23]. There is, however, a limited number of published studies evaluating the cost-effectiveness of NPWT in the management of chronic and open wounds [55,57e59]. The lack of robust clinical evidence (i.e., large RCTs) to support NPWT may explain the rarity of relevant
Table 5 e Expected cost, benefits, and return on investment for future trial design. Sample size/arm 100 200 300 400 500 600 700 800 900 1000
EVSI (AUD)
Research sites number
Trial duration (y)*
Total trial costy (AUD)
ENBSz (AUD)
ROI, %x
1,645,000 2,114,000 2,275,000 2,345,000 2,380,000 2,415,000 2,432,500 2,448,250 2,457,350 2,460,500
4 4 4 4 4 4 4 4 4 4
1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50
656,250 907,500 1,158,750 1,410,000 1,661,250 1,912,500 2,163,750 2,415,000 2,666,250 2,917,500
988,750 1,206,500 1,116,250 935,000 718,750 502,500 268,750 33,250 208,900 457,000
151 133 96 66 43 26 12 1 8 16
ROI ¼ return on investment. * Based on recruitment rate of 200 patients per site per year and additional 1 y for data analysis. y Total trial cost ¼ fixed þ variable costs þ opportunity cost. z ENBS ¼ the difference between EVSI and total trial cost. x ROI ¼ ENBS/total cost.
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Table 6 e Sensitivity analysis of assumptions effect on the cost-effectiveness and value of additional analyses results. Assumption NPWT price Baseline 25% increase 25% reduction Willingness-to-pay threshold/QALY Baseline 50% increase 50% reduction RR Baseline (Masden et al. [25] and pilot trial) [36]. Masden et al. [25] alone Pilot trial alone [36]. Technology lifetime Baseline 50% increase 50% reduction
Estimate
Incremental net benefit
Value of information
Optimal sample size/arm
AUD175 AUD220 AUD130
AUD70 AUD30 AUD125
AUD2.7 million AUD3.2 million AUD2.0 million
200 250 150
AUD50,000 AUD75,000 AUD25,000
AUD70 AUD125 AUD30
AUD2.7 million AUD2.8 million AUD2.7 million
200 250 200
0.73
AUD70
AUD2.7 million
200
0.5 0.81
AUD200 AUD -35
AUD3.2 million AUD3.8 million
250 300
10 y 15 y 5y
AUD70 AUD70 AUD70
AUD2.7 million AUD3.4 million AUD1.6 million
200 300 100
QALY ¼ quality adjusted life year.
economic evaluations. Nevertheless, this should not pose a problem because when evidence is scarce, information could be sought from various sources such as pilot trials, observational studies, and expert opinion [55]. Ideally, this should be also accompanied by an appropriate value of information analysis to inform whether that evidence is sufficient to guide decisions or if additional research is required. For instance, Soares et al. [55] conducted cost-effectiveness and value of information analyses on NPWT in patients with severe pressure ulcers. They demonstrated how combining information from the existing evidence with a pilot trial and elicited expert views resulted in a better informed decision compared with using a single source of evidence when information is scarce. Moreover, they used value of information analysis to optimize future trial design [55]. In this article, we populated our model with the best available evidence in the literature combined with the results of our pilot trial and expert opinion when necessary. As expected for any economic evaluation, the results of our cost-effectiveness analysis are dependent on the assumptions made for the model structure and input parameters. We used hydrocolloid dressing, which is the standard of care for this procedure in Australia, as the comparator in our analysis; however, there are other types of surgical dressings in clinical practice at various prices. Importantly, it is essential not to limit the comparison to the unit prices of the products but also to consider the efficacy and overall cost of use. For instance, NPWT was less costly than saline soaked gauze (US $14,546 compared with US $23,465) in healing pressure ulcers because wounds healed 61% faster with NPWT compared with the standard gauze and saline [60]. Furthermore, our model focused on SSI as an outcome and did not include other outcomes such as healing rate or other wound complications. However, the model did not include healing as an outcome because, unlike chronic wounds, most clean incision wounds will completely heal in a relatively short time [21]. Additionally, compared with other wound complications expected
with CS (e.g., seroma), SSIs are more associated with mortality, morbidity, and cost. In addition, our model was probabilistic and Monte Carlo sampling allowed for simultaneous characterization of uncertainty in all model parameters. Finally, we tested the effect of various assumptions made to the value of information analysis on the results. In the sensitivity analysis presented, the optimal sample size remained between 100 and 300 patients in most of the scenarios.
5.
Conclusions
Based on the best available evidence, NPWT appears costeffective compared with standard dressing in preventing SSI in obese women undergoing CS. But, there is high uncertainty surrounding a decision to implement this technology and further research to explore the relative effect of NPWT in this population would be worthwhile before implementation.
Uncited figure Figure 2.
Acknowledgments H.W.T. is supported by a National Health and Medical Q6 Research Council PhD scholarship through the Centre for Research Excellence in Nursing Interventions for Hospitalised Patients. Authors’ contributions: H.W.T., L.G.G., and P.A.S. performed the economic analysis. W.C. and B.M.G. provided the clinical data. All authors contributed substantially to the preparation of the article.
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Disclosure [22]
The authors declare no conflict of interest.
Q11
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3. Expected value of sample information
Appendix
EVSI is the difference between the expected value of a decision made after collecting data (D) on the parameter of interest and the expected NB with current information [47].
Appendix 1.
The NB for an intervention i informed by the set of input parameters q was calculated as follows: NB ði; qÞ ¼ l Effect ði; qÞ Cost ði; qÞ
(1)
1. Expected value of perfect information The EVPI is the difference between the expected NB of a decision with perfect information and the decision made based on current information. [49]. EVPI ¼ Eq Maxi NBði; qÞ Maxi Eq NBði; qÞ
(2)
1. Assigned probability distributions to the input parameters in the mode as summarized in Table 2 in the main text. 2. Sampled random values k times (e.g., k ¼ 10,000) from the distributions described previously for each intervention. 3. Calculated the mean NB for each intervention across all simulations and identified the preferred baseline decision that is, the intervention with the maximum expected mean NB (maxi Eq NBði; qÞ). 4. Calculated the NB for each intervention and identified the optimal intervention at each simulation. 5. Averaged the NBs from the identified optimal interventions in step 4 (Eq maxi NBði; qÞ). 6. EVPI per patient is the difference between the average NBs in steps 5 and 3.
2. Expected value of perfect parameter information Because the model used was linear and assuming no correlation between input parameters, the same one-level Monte Carlo simulation technique described previously was used to calculate the EVPPI; the sampling would be only from the distribution of the parameter(s) of interest qI , whereas the other parameters qC were fixed at their prior means [51]. EVPPIqI ¼ EqI maxi NBði; qI ; EðqC ÞÞ maxi Eq NBði; qÞ
(3)
Steps 1e3 are as described in EVPI algorithm detailed previously. Steps 4e9 are as follows: 4. Sample qI once from its prior distribution (one-level simulation). 5. Fix qI at their sampled values, and fix the remaining uncertain parameters qC at their prior mean value. 6. Calculate the average NB of each intervention given these parameter values. 7. Identify the intervention that has the highest estimated expected NB given the sampled value for the parameters of interest (qIK ). 8. Repeat steps 4e7 k times (e.g., k ¼ 10,000), and calculate the average NB of the preferred interventions identified in step 7. 9. EVPPI is the difference between the average NBs in steps 8 and 3.
EVSIn ¼ ED maxi NBði; EðqI jDÞ; EðqC ÞÞ maxi Eq NBði; qI ; qC Þ
(4)
Given the linearity of the model, calculating the EVSI required the same one-level Monte Carlo sampling; however, the sampling would be from the posterior distribution of the parameter of interest obtained using Bayesian updating [47]. To estimate the EVSI for the RR of NPWT compared with that of standard dressing, we assumed that parameters qNP and qSD represent the probability of SSI with NPWT and standard dressing, respectively. We followed the algorithm adapted from the algorithm reported in Ades et al. [47]. Steps 0e3 are as described in EVPI algorithm mentioned previously. Steps 4e9 are given as follows: 4. Simulate the variety of possible results of proposed data collection by the following steps. 4.1. Draw a sample from the prior distribution of the RR. The logRR w normal (m0 , s0 ) wherem0 is logRR in the metaanalysis and s0 is its variance. 4.2. Draw a sample baseline parameter qSD from its prior distribution: qSD w beta (a,b), where a is the number of patients who developed SSI and b is the number of patients who did not develop SSI from the combined data of the pilot trial and Masden et al. 4.3. Transform back to obtain an implied prior for qNP : qNP ¼ qSD expðlog RRÞ 5. 5.1 Draw a sample sufficient statistic D, in this case a binominal numerator, for each arm in the future trial with size n, assuming equal size arms: rSD wbinomial ðqSD ; nÞandrNP wbinomial ðqNP ; nÞ 5.2 Convert the sufficient statistics to a mean and variance using the normal approximation: mD ¼ log rNP n rSD n ; 1 sD ¼ ðn rSD Þ rSD n þ ðn rNP Þ rNP n 6. Update the prior with the new simulated data to obtain parameters of the posterior distribution: logRRjDwnormal m0 s0 þ mD ; sD
ðs0 þ sD Þ; s0 þ sD
7. Because the model is linear, we sampled from the expected value of the updated distribution in step 6 and the mean values for qC and identified the intervention with the highest expected NB. 8. Repeat steps 4e7 for 10,000 times, and calculate the average NB of the preferred interventions identified in step 7. 9. The EVSI is the difference between the average NBs in steps 8 and 3.
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Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.JournalofSurgicalResearch.com
Cost-utility analysis of negative pressure wound therapy in high-risk cesarean section wounds Haitham W. Tuffaha,a,b,c,* Brigid M. Gillespie, PhD,a,c Wendy Chaboyer,a,c Louisa G. Gordon, PhD,a,b and Paul A. Scuffhama,b a
Griffith Health Institute, Griffith University, Gold Coast, Queensland, Australia Centre for Applied Health Economics, School of Medicine, Griffith University, Meadowbrook, Queensland, Australia c NHMRC Centre of Research Excellence in Nursing Interventions for Hospitalised Patients, Research Centre for Health Practice Innovation, Griffith University, Gold Coast, Queensland, Australia b
article info
abstract
Article history:
Background: Obese women undergoing cesarean section are at increased risk of post-
Received 29 October 2014
operative infection. There is growing interest in negative pressure wound therapy (NPWT)
Received in revised form
to prevent closed surgical incision complications including surgical site infection; however,
14 January 2015
the evidence on the effectiveness and cost-effectiveness of this technology is limited. The
Accepted 6 February 2015
objective of this study was to evaluate the cost-effectiveness of NPWT compared with that
Available online xxx
of standard dressing in preventing surgical site infection in obese women undergoing elective cesarean section based on current evidence and to estimate the value and optimal
Keywords:
design of additional research to study this technology.
Cost-effectiveness
Methods: The analysis was from the perspective of Queensland Health, Australia, using a
Negative pressure wound therapy
decision model. Parameters were obtained from the published literature, a pilot clinical
Cesarean section
trial, and expert opinion. Monte Carlo simulation was performed to calculate the net
Value of information
monetary benefit, characterize decision uncertainty, and estimate the value of additional research. Comparing the expected monetary benefits and costs of alternative trial sample sizes informed the optimal future study design. Results: The incremental net monetary benefit of NPWT was Australian dollars 70, indicating that NPWT is cost-effective compared with that of standard dressing. The probability of NPWT being cost-effective was 65%. The estimated value of additional research to resolve decision uncertainty would be Australian dollars 2.7 million. The optimal sample size of a future trial investigating the relative effectiveness of NPWT would be 200 patients per arm. Conclusions: Based on the current evidence, NPWT is cost-effective; however, there is high uncertainty surrounding the decision to adopt this technology. Additional research is worthwhile before implementation. ª 2015 Elsevier Inc. All rights reserved.
1.
Introduction
The increasing prevalence of obesity in women of childbearing age is a major health problem. Studies from the United States,
England, and Australia reported around 25% of women of childbearing age are obese with a body mass index (BMI) of 30 kg/m2 [1e4]. Maternal obesity poses serious complications during and after pregnancy to both the affected mothers and
* Corresponding author. Centre for Applied Health Economics, School of Medicine, Griffith Health Institute, Griffith University, Queensland 4131, Australia. Tel.: þ61 7 338 21510; fax: þ61 7 338 21338. E-mail address: [email protected] (H.W. Tuffaha). 0022-4804/$ e see front matter ª 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jss.2015.02.008
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2
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their babies, including gestational diabetes, hypertensive disorders of pregnancy, and stillbirth [5]. Obesity also increases the need for cesarean delivery with the risk of a cesarean section (CS) being two to three times higher among obese compared with pregnant women of normal weight [6e8]. Obese women undergoing CS are at increased risk of complications particularly postoperative infection [5,9]. In a metaanalysis of six studies, the pooled odds ratio for obese CS women having an infection was three times higher compared with that for nonoverweight women [5]. A common postoperative complication is surgical site infection (SSI), which occurs after surgery in the area of the body where the surgery took place [10]. Controlling SSI is a health-care quality indicator because it results in significant morbidity, reduced quality of life, occasional death, and increased costs [11e13]. One case of SSI may cost up to $30,000, depending on its severity [12,14,15]. Despite the advances in infection control practices, ventilation systems in the operating rooms, sterilization methods, surgical technique, preoperative antimicrobial prophylaxis, and wound dressings, SSI remains common in obese women undergoing CS with an estimated incidence between 16 and 30% [11,16,17]. Since its introduction two decades ago, negative pressure wound therapy (NPWT) has been used to promote the healing of acute and chronic wounds as well as skin grafts (Table 1) [19e21]. It is based on a closed sealed system that applies negative pressure to the wound surface resulting in increased blood circulation, decreased edema, enhanced granulation tissue formation, and reduced bacterial colonization [21,22]. There is growing interest in extending the use of NPWT to closed surgical incision to prevent wound complications including SSI [21,22]. Unfortunately, the available evidence on the effectiveness and cost-effectiveness of NPWT in surgical incisions is limited [23]. This is expected in surgical practice, where innovations in technologies and equipment often outpace supporting evidence. Recent systematic reviews have identified three small randomized controlled trials (RCTs) that investigated the incidence of SSI in NPWT compared with that of standard wound dressing [22,23]. Those trials showed a reduction in SSI with NPWT although all trials reported that the reductions were not statistically significant [23e26]. None of the trials involved patients undergoing CS.
Given the cost of NPWT can reach $100 a day, it is essential to evaluate the cost-effectiveness of this technology before its wide implementation. Nevertheless, with the limitations in the available evidence, the results of a cost-effectiveness analysis may not be certain enough to inform a decision. Clearly, conducting additional research would reduce this uncertainty and better inform decisions. But, there is a cost associated with obtaining further evidence in terms of the direct costs of conducting clinical trials and the opportunity cost of delaying the implementation of an effective intervention awaiting research results. An analytical approach known as value of information analysis has been developed and used in health-care interventions to inform whether the available evidence is sufficient to support a decision on a given technology or if additional research study is worthwhile [27,28]. It is based on the notion that information is valuable because it reduces the uncertainty surrounding the available evidence and subsequently the potential cost of making wrong decisions based on that uncertain evidence [27,28]. In other words, the expected value of information is the expected cost of error. Furthermore, value of information analysis has been proposed as an alternative to the standard hypothesis testing approach, which is based on type 1 and type 2 error and the minimum clinically important difference, in determining sample sizes for RCTs [29e31]. Under this economic approach, researchers consider the sample sizes that maximize the expected net benefit of research, which is the difference between the expected monetary benefit of a given trial design and its expected cost [29,30]. The aim of this study was to conduct a cost-effectiveness analysis of NPWT in preventing SSI in obese women undergoing CS compared with that of standard dressing based on currently available evidence and to perform a value of information analysis to estimate the value and optimal sample size of a larger RCT to support this technology.
2.
Methods
The approach to achieve the study aim was to: 1) conduct a cost-effectiveness analysis of NPWT compared with standard dressing using a decision analytic model and; 2) perform
Table 1 e Description of commonly used negative pressure devices [18]. Productname Manufacturer Clinical indications
Pressure settings Therapy duration, d Cost
Prevena Kinetic Concepts Inc Chronic wounds Acute wounds Traumatic wounds Subacute wounds Dehisced wounds Partialthickness burns Flaps and grafts 75 to 125 mm Hg 7 AUD875
VAC-VIA Kinetic Concepts Inc Clean, closed incisions that continue to drain after closure.
125 mm Hg 2e7 AUD395
mm HG ¼ millimeter mercury.
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PICO Smith and Nephew Acute Flaps and grafts Incision sites Partial thickness burns Subacute wounds Traumatic Ulcers (e.g.,pressure) 80 mm Hg 5 AUD175
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Monte Carlo simulation to characterize decision uncertainty and estimate the expected value of additional research.
2.1.
Cost-effectiveness analysis
The cost-effectiveness analysis was from the perspective of the State Department of Health in Queensland, Australia, using a decision model. The model was probabilistic with prior distributions assigned to input parameters. We used Monte Carlo simulation to sample from the input distributions to estimate the expected costs and effects associated with each intervention [32]. In general, beta distributions were assigned to probabilities and utilities, gamma distributions to costs and disutilities, and lognormal distributions to relative risks (RRs). For this analysis, the efficacy outcome was qualityadjusted life-years (QALYs) gained. The net monetary benefit (NB) was calculated, which is the efficacy multiplied by the willingness-to-pay threshold for additional unit of effect outcome minus the cost [33]. We set the willingness-to-pay threshold at Australian dollars (AUD) 50,000 per QALY [34]. The intervention expected to be cost-effective would be the one with the highest expected monetary net benefit. Costs and net benefits were presented in AUD. The time horizon of the model was 6 mo to allow for sufficient time to capture and treat post-CS complications. Costs and effects were not discounted because the model timeline was 30 and comorbidities) undergoing a range 437 of procedures including abdominal surgeries; 6.8% of the 438 NPWT group and 13.5% of the standard dressing group 439 developed wound infection with an RR of 0.50 (95% CI 440 0.13e1.95). Other trials identified were an RCT by Howell et al. 441 [24] on NPWT in knee surgery that was terminated early due to 442 443 blister formation, and an RCT by Stannard et al. [26] investi444 gating NPWT in 249 patients with lower extremity trauma 445 fractures. In that RCT, around 10% of wounds in the NPWT 446 group had infection compared with 20% in the standard 447 dressing group at an RR of 0.52 (95% CI 0.28e0.96) [26]. It was 448 not appropriate to combine the results from Stannard et al. 449 with Masden et al. because the analysis in the former was per 450 wound and not per patient. Furthermore, patient character451 istics and wound types in the two studies were heteroge452 neous. Given the scarcity in the available evidence and in 453 454 order not to overestimate uncertainty in the relative
effectiveness parameter by relying on the pilot study results alone, the RR from the pilot study was collated with the RR from Masden et al. This was achieved by undertaking a Bayesian approach under which the RR from Masden et al. (i.e., prior information) was updated with the RR from the pilot trial resulting in an updated (i.e., posterior) RR of 0.73 (0.39e1.32) [44]. The effect of RR estimation on the results of the costeffectiveness and value of information analyses was explored in sensitivity analysis. The probability for deep/organ SSI was estimated at 19% from Wilson et al. [13] and Henman et al. [37]. The probability of death from deep/organ SSI was set at 0.07 and for superficial SSI at 0.02, from Astagneau et al. [39] and Kirkland et al. [38].
2.1.2.2. Costs. The cost of NPWT was set at AUD175 for the price of a disposable (one-application) device (PICO). The cost of standard dressing was AUD7.5 for the hydrocolloid dressing (Comfeel Plus). The cost of treating superficial SSI was obtained from Graves et al. [42] and was set at AUD250; this includes the cost of a general practitioner visit, 7 d of oral antibiotic, and the cost of test and/or swab. For the cost of deep/organ SSI, this was obtained from the 2009e2010 Australian Refined Diagnosis Related Groups, item T61 (postoperative and posttrauma infection) at AUD10,000 [43]. This includes the cost of hospitalization, tests and/or swabs, and intravenous antibiotics for 7e14 d [43]. The estimated staff time was 10 min to apply the NPWT and 2 min for the standard dressing at an average wage of AUD33 per hour [45]. Costs obtained in other price years were converted to 2014 AUD using the CCEMG-EPPI-Centre Cost Converter Web-based tool [46]. 2.1.2.3. Utilities. The utilities in the model were based on EuroQoL 5D (EQ-5D-3L) scores, anchored between 0.0 for death and 1.0 for best possible health. Utility weights were based on the preferences of the Australian population. The utility scores for the women undergoing CS and discharged with no complications was set at 0.9 from Clemens et al. [40]. For the
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5
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women who developed SSI, the disutility for superficial and deep/organ SSI was set at 0.2 and 0.4, respectively, from Lipsky et al. [41]. The assumption was that the disutility will take place over 1 wk for superficial SSI and 2 wk for deep/organ SSI.
2.2.
Value of information analysis
The detailed algorithms for value of information calculation are described in the literature [47,48] and are presented in Appendix 1. Briefly, the first measure to calculate was the expected value of perfect information (EVPI). This is the value of the additional information that would resolve all uncertainty surrounding “all” input parameters; therefore, it is the maximum (upper bound) value for conducting further research to resolve this uncertainty [44]. The EVPI is the difference between the expected NB of a decision with perfect information and the decision made based on current information [49]. To estimate the EVPI, 10,000 Monte Carlo iterations were randomly sampled from the prior parameter distributions of all parameters to identify the intervention with the highest expected mean NB based on current information (i.e., the cost-effective intervention). Then the intervention with the highest NB “at each iteration” was identified and the identified values were averaged to calculate the expected “maximum” NB (i.e., NB with perfect information). If the EVPI exceeds the expected cost of future research, the next step would be to calculate the value of information to resolve the uncertainty in the parameter(s) of interest, which is the expected value of perfect parameter information (EVPPI) [50,51]. Since our model (i.e., the decision tree) is linear and assuming no correlation between input parameters because they were obtained from various sources, the same one-level Monte Carlo simulation technique described previously was used to calculate the EVPPI; the sampling would be only from the distribution of the “parameter(s) of interest,” whereas the other parameters were fixed at their prior means [51]. To estimate the value of a future clinical trial with a given sample size (n) that could reduce uncertainty surrounding the parameter of interest, the expected value of sample information (EVSI) was estimated by calculating the difference between the expected value of a decision made after collecting data on the parameter of interest and the expected net benefit with current information [47]. Conceivably, the data collected from additional research are not known at this stage but could be predicted by simulation. Given the linearity of the model, calculating the EVSI required the same one-level Monte Carlo simulation. However, the sampling would be from the posterior distribution of the parameter(s) of interest obtained using Bayesian updating [47]. The value of information measures described previously are per-patient estimates; however, it is necessary to estimate the value of information for the population of patients expected to benefit from the research outcomes. This was calculated by multiplying the per-patient estimates by the estimated number of patients expected to benefit from NPWT over a certain period. Obese women undergoing CS in Queensland represent 20% of the 20,000 CS performed every year in that state [43,52]. Accordingly, we estimated the expected number of obese women undergoing CS over 10 y (with 5% discounting) to be around 35,000.
To determine the optimal sample size of a future trial, the population-EVSI and the expected total cost were estimated for a range of possible trial sample sizes. The difference between the expected monetary benefit of research and the total cost of a particular study design is the expected net benefit of sampling (ENBS) [31,53]. The total cost of a future trial design included fixed costs (e.g., set-up cost, salaries), variable costs per patient, and the opportunity costs expected to be incurred by patients who would receive the inferior intervention during the trial [47,53]. We based our estimates for the cost of a future trial design on a research grant application for an RCT on NPWT in four institutions with a recruitment rate of 200 patients per site each year (Table 3). The estimated fixed project cost was AUD125,000 per year for project management and data analysis, plus an annual cost of AUD 100,000 per site for recruitment and data collection. The cost per patient was set at AUD250. If the ENBS is negative, additional research would not be cost-effective because the expected costs of the study would exceed its expected benefits. Conversely, a positive ENBS indicates that future research would be worthwhile. The optimal sample size is determined when the ENBS reaches a maximum [54,55].
3.
Results
3.1.
Cost-effectiveness analysis
Compared with standard dressing, NPWT resulted in an average additional cost of AUD30 (AUD600 versus AUD570) and additional 0.002 QALYs (Table 4). At a willingness-to-pay threshold of AUD50,000 per QALY, the incremental NB was 70AUD, indicating that NPWT is cost-effective. The probability of NPWT being cost-effective was 65%. Figure 3 shows the probability of NPWT being cost-effective over a range of willingness-to-pay thresholds.
3.2.
Value of information analysis
The EVPI for the decision of adopting NPWT is AUD76 per patient, which is AUD2.7 million (AUD76 35,000) for the population expected to benefit from this technology over the coming 10 y. The parameter with the highest value of information was the RR of SSI with NPWT at AUD75 per patient and population value of AUD2.6 million. The value of a future RCT exploring the relative effectiveness of NPWT over a range of
Table 3 e Research cost breakdown. Item Fixed costs Data management/y Project management/y Office supplies/y Field expenses (i.e., site visits and monitoring)/site/y Recruitment and data collection salaries/site/y Blinded outcome assessor/site/y Variable cost (i.e., per patient) Equipment Randomization services
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Cost (AUD) 30,000 85,000 10,000 15,000 70,000 15,000 200 50
585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649
6
650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714
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Table 4 e Cost-effectiveness analysis results. Analysis results
Standard dressing
NPWT
Difference
Cost (AUD) Effect (QALY) NB* (AUD)
570 0.446 21,730
600 0.448 21,800
30 0.002 70
*
For a willingness-to-pay threshold of AUD50,000 per QALY.
sample sizes per arm is depicted in Figure 3. As the sample size increases more uncertainty is expected to resolve and the value of additional research increases. Comparing the expected monetary benefits and costs of the suggested sample sizes, the optimal sample size would be 200 patients in each arm with an ENBS of AUD1.2 million at a total cost of AUD900,000 (Table 5). The expected return on investment (i.e., net benefit/cost ratio) would be 133% (AUD1.2million/ AUD900,000). The initial design with 400 patients per arm would provide a return on investment of 66% (AUD935/ AUD1.4million). In a sensitivity analysis, increasing the price of NPWT, varying willingness-to-pay threshold, extending the timeline of the technology, or estimating the RR based on the pilot trial alone resulted in an estimated optimal sample size between 200 and 300 patients in each arm (Table 6). On the other hand, with reduced NPWT price and shorter technology life-time the estimated sample sizes ranged between100 and 200 patients in each arm.
4.
Discussion
The use of NPWT in closed surgical wounds to enhance healing by primary intention and to prevent wound
complications is a new field of application for this technology. This article presents a cost-effectiveness analysis of NPWT in preventing SSI in obese women undergoing CS. Based on the current evidence, NPWT appears to be cost-effective compared with standard dressing with an expected incremental NB of AUD70. Nevertheless, the probability of NPWT being cost-effective is only 65%, indicating high decision uncertainty and thus high chance of error in a decision based on this cost-effectiveness analysis. Given the high cost of NPWT and the high uncertainty in the cost-effectiveness results, it would be reasonable to conduct additional research before implementing this technology. The expected value of information to resolve the uncertainty in the available evidence would be around AUD2.7 million, suggesting that additional research is potentially worthwhile. Our analysis estimates the optimal sample size for a future trial investigating the relative effectiveness of NPWT compared with standard dressing in reducing SSI. By calculating the expected monetary benefit (i.e., the expected reduction in uncertainty) of additional sampling and the expected cost of conducting this future trial, the sample size with the highest benefit-to-cost ratio would be 200 patients in each arm. This sample size is lower than the sample size of 400 patients in each arm initially calculated based on hypothesis testing and this smaller sample size would be more economical providing higher return on investment (133% versus 66%). The results demonstrate how value of information analysis can provide an alternative to the standard hypothesis testing approach, which relies on arbitrary chosen error probabilities where type 1 and type 2 error receive the same weight (e.g., 5% and 20%, respectively), regardless of the consequences of making an error [30]. Under value of information analysis, an economic approach is applied to sample size estimation. This approach considers a number of factors
Fig. 2 e The probability of each intervention being cost-effective over a range of willingness-to-pay thresholds. (Color version of figure is available online.) 5.2.0 DTD YJSRE13128_proof 28 February 2015 1:25 pm ce
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715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779
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Fig. 3 e The expected value of information and expected cost across future trial sample sizes. (Color version of figure is available online.)
such as the relative effectiveness and costs of the evaluated technologies, the decision maker’s willingness-to-pay for the additional effectiveness, the probability and consequences of making a suboptimal decision, the population expected to benefit from research findings, the level of implementation (i.e., uptake) of research findings, and the total cost associated with the intended research [54]. Of note, the total cost of research does not only include the direct cost of research in terms of fixed and variable costs but also the opportunity cost from delaying the implementation of the technology awaiting the conclusion of the future trial [27,56]. In addition to sample-size calculation, value of information analysis can optimize other aspects of trial design such as the number of comparators and follow-up duration [29,56]. Obviously, more uncertainty is expected to resolve with longer
follow-up and more comparator arms albeit with additional research costs. Accordingly, the preferred design would be the one that optimizes the expected research monetary benefits compared with the expected research costs [53]. The same principle can be extended to quantitatively prioritize research. Under the value of information framework, competing research proposals within a limited budget could be ranked according to their expected return on investment [53]. To our knowledge, there is no published cost-effectiveness analysis of NPWT in preventing wound complications in closed surgical incisions [23]. There is, however, a limited number of published studies evaluating the cost-effectiveness of NPWT in the management of chronic and open wounds [55,57e59]. The lack of robust clinical evidence (i.e., large RCTs) to support NPWT may explain the rarity of relevant
Table 5 e Expected cost, benefits, and return on investment for future trial design. Sample size/arm 100 200 300 400 500 600 700 800 900 1000
EVSI (AUD)
Research sites number
Trial duration (y)*
Total trial costy (AUD)
ENBSz (AUD)
ROI, %x
1,645,000 2,114,000 2,275,000 2,345,000 2,380,000 2,415,000 2,432,500 2,448,250 2,457,350 2,460,500
4 4 4 4 4 4 4 4 4 4
1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50
656,250 907,500 1,158,750 1,410,000 1,661,250 1,912,500 2,163,750 2,415,000 2,666,250 2,917,500
988,750 1,206,500 1,116,250 935,000 718,750 502,500 268,750 33,250 208,900 457,000
151 133 96 66 43 26 12 1 8 16
ROI ¼ return on investment. * Based on recruitment rate of 200 patients per site per year and additional 1 y for data analysis. y Total trial cost ¼ fixed þ variable costs þ opportunity cost. z ENBS ¼ the difference between EVSI and total trial cost. x ROI ¼ ENBS/total cost.
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845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909
8
910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974
j o u r n a l o f s u r g i c a l r e s e a r c h x x x ( 2 0 1 5 ) 1 e1 1
Table 6 e Sensitivity analysis of assumptions effect on the cost-effectiveness and value of additional analyses results. Assumption NPWT price Baseline 25% increase 25% reduction Willingness-to-pay threshold/QALY Baseline 50% increase 50% reduction RR Baseline (Masden et al. [25] and pilot trial) [36]. Masden et al. [25] alone Pilot trial alone [36]. Technology lifetime Baseline 50% increase 50% reduction
Estimate
Incremental net benefit
Value of information
Optimal sample size/arm
AUD175 AUD220 AUD130
AUD70 AUD30 AUD125
AUD2.7 million AUD3.2 million AUD2.0 million
200 250 150
AUD50,000 AUD75,000 AUD25,000
AUD70 AUD125 AUD30
AUD2.7 million AUD2.8 million AUD2.7 million
200 250 200
0.73
AUD70
AUD2.7 million
200
0.5 0.81
AUD200 AUD -35
AUD3.2 million AUD3.8 million
250 300
10 y 15 y 5y
AUD70 AUD70 AUD70
AUD2.7 million AUD3.4 million AUD1.6 million
200 300 100
QALY ¼ quality adjusted life year.
economic evaluations. Nevertheless, this should not pose a problem because when evidence is scarce, information could be sought from various sources such as pilot trials, observational studies, and expert opinion [55]. Ideally, this should be also accompanied by an appropriate value of information analysis to inform whether that evidence is sufficient to guide decisions or if additional research is required. For instance, Soares et al. [55] conducted cost-effectiveness and value of information analyses on NPWT in patients with severe pressure ulcers. They demonstrated how combining information from the existing evidence with a pilot trial and elicited expert views resulted in a better informed decision compared with using a single source of evidence when information is scarce. Moreover, they used value of information analysis to optimize future trial design [55]. In this article, we populated our model with the best available evidence in the literature combined with the results of our pilot trial and expert opinion when necessary. As expected for any economic evaluation, the results of our cost-effectiveness analysis are dependent on the assumptions made for the model structure and input parameters. We used hydrocolloid dressing, which is the standard of care for this procedure in Australia, as the comparator in our analysis; however, there are other types of surgical dressings in clinical practice at various prices. Importantly, it is essential not to limit the comparison to the unit prices of the products but also to consider the efficacy and overall cost of use. For instance, NPWT was less costly than saline soaked gauze (US $14,546 compared with US $23,465) in healing pressure ulcers because wounds healed 61% faster with NPWT compared with the standard gauze and saline [60]. Furthermore, our model focused on SSI as an outcome and did not include other outcomes such as healing rate or other wound complications. However, the model did not include healing as an outcome because, unlike chronic wounds, most clean incision wounds will completely heal in a relatively short time [21]. Additionally, compared with other wound complications expected
with CS (e.g., seroma), SSIs are more associated with mortality, morbidity, and cost. In addition, our model was probabilistic and Monte Carlo sampling allowed for simultaneous characterization of uncertainty in all model parameters. Finally, we tested the effect of various assumptions made to the value of information analysis on the results. In the sensitivity analysis presented, the optimal sample size remained between 100 and 300 patients in most of the scenarios.
5.
Conclusions
Based on the best available evidence, NPWT appears costeffective compared with standard dressing in preventing SSI in obese women undergoing CS. But, there is high uncertainty surrounding a decision to implement this technology and further research to explore the relative effect of NPWT in this population would be worthwhile before implementation.
Uncited figure Figure 2.
Acknowledgments H.W.T. is supported by a National Health and Medical Q6 Research Council PhD scholarship through the Centre for Research Excellence in Nursing Interventions for Hospitalised Patients. Authors’ contributions: H.W.T., L.G.G., and P.A.S. performed the economic analysis. W.C. and B.M.G. provided the clinical data. All authors contributed substantially to the preparation of the article.
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975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039
j o u r n a l o f s u r g i c a l r e s e a r c h x x x ( 2 0 1 5 ) 1 e1 1
1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
Disclosure [22]
The authors declare no conflict of interest.
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3. Expected value of sample information
Appendix
EVSI is the difference between the expected value of a decision made after collecting data (D) on the parameter of interest and the expected NB with current information [47].
Appendix 1.
The NB for an intervention i informed by the set of input parameters q was calculated as follows: NB ði; qÞ ¼ l Effect ði; qÞ Cost ði; qÞ
(1)
1. Expected value of perfect information The EVPI is the difference between the expected NB of a decision with perfect information and the decision made based on current information. [49]. EVPI ¼ Eq Maxi NBði; qÞ Maxi Eq NBði; qÞ
(2)
1. Assigned probability distributions to the input parameters in the mode as summarized in Table 2 in the main text. 2. Sampled random values k times (e.g., k ¼ 10,000) from the distributions described previously for each intervention. 3. Calculated the mean NB for each intervention across all simulations and identified the preferred baseline decision that is, the intervention with the maximum expected mean NB (maxi Eq NBði; qÞ). 4. Calculated the NB for each intervention and identified the optimal intervention at each simulation. 5. Averaged the NBs from the identified optimal interventions in step 4 (Eq maxi NBði; qÞ). 6. EVPI per patient is the difference between the average NBs in steps 5 and 3.
2. Expected value of perfect parameter information Because the model used was linear and assuming no correlation between input parameters, the same one-level Monte Carlo simulation technique described previously was used to calculate the EVPPI; the sampling would be only from the distribution of the parameter(s) of interest qI , whereas the other parameters qC were fixed at their prior means [51]. EVPPIqI ¼ EqI maxi NBði; qI ; EðqC ÞÞ maxi Eq NBði; qÞ
(3)
Steps 1e3 are as described in EVPI algorithm detailed previously. Steps 4e9 are as follows: 4. Sample qI once from its prior distribution (one-level simulation). 5. Fix qI at their sampled values, and fix the remaining uncertain parameters qC at their prior mean value. 6. Calculate the average NB of each intervention given these parameter values. 7. Identify the intervention that has the highest estimated expected NB given the sampled value for the parameters of interest (qIK ). 8. Repeat steps 4e7 k times (e.g., k ¼ 10,000), and calculate the average NB of the preferred interventions identified in step 7. 9. EVPPI is the difference between the average NBs in steps 8 and 3.
EVSIn ¼ ED maxi NBði; EðqI jDÞ; EðqC ÞÞ maxi Eq NBði; qI ; qC Þ
(4)
Given the linearity of the model, calculating the EVSI required the same one-level Monte Carlo sampling; however, the sampling would be from the posterior distribution of the parameter of interest obtained using Bayesian updating [47]. To estimate the EVSI for the RR of NPWT compared with that of standard dressing, we assumed that parameters qNP and qSD represent the probability of SSI with NPWT and standard dressing, respectively. We followed the algorithm adapted from the algorithm reported in Ades et al. [47]. Steps 0e3 are as described in EVPI algorithm mentioned previously. Steps 4e9 are given as follows: 4. Simulate the variety of possible results of proposed data collection by the following steps. 4.1. Draw a sample from the prior distribution of the RR. The logRR w normal (m0 , s0 ) wherem0 is logRR in the metaanalysis and s0 is its variance. 4.2. Draw a sample baseline parameter qSD from its prior distribution: qSD w beta (a,b), where a is the number of patients who developed SSI and b is the number of patients who did not develop SSI from the combined data of the pilot trial and Masden et al. 4.3. Transform back to obtain an implied prior for qNP : qNP ¼ qSD expðlog RRÞ 5. 5.1 Draw a sample sufficient statistic D, in this case a binominal numerator, for each arm in the future trial with size n, assuming equal size arms: rSD wbinomial ðqSD ; nÞandrNP wbinomial ðqNP ; nÞ 5.2 Convert the sufficient statistics to a mean and variance using the normal approximation: mD ¼ log rNP n rSD n ; 1 sD ¼ ðn rSD Þ rSD n þ ðn rNP Þ rNP n 6. Update the prior with the new simulated data to obtain parameters of the posterior distribution: logRRjDwnormal m0 s0 þ mD ; sD
ðs0 þ sD Þ; s0 þ sD
7. Because the model is linear, we sampled from the expected value of the updated distribution in step 6 and the mean values for qC and identified the intervention with the highest expected NB. 8. Repeat steps 4e7 for 10,000 times, and calculate the average NB of the preferred interventions identified in step 7. 9. The EVSI is the difference between the average NBs in steps 8 and 3.
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