Screening Cardiac Surgery Patients for MRSA: An Economic Computer Model

Abstract

Objective

To estimate the economic value of preoperative methicillin-resistant Staphylococcus aureus (MRSA) screening and decolonization for cardiac surgery patients.

Study Design

Monte Carlo decision-analytic computer simulation model.

Methods

We developed a computer simulation model representing the decision of whether to perform preoperative MRSA screening and decolonizing those patients with a positive MRSA culture. Sensitivity analyses varied key input parameters including MRSA colonization prevalence, decolonization success rates, the number of surveillance sites, and screening/decolonization costs. Separate analyses estimated the incremental cost-effectiveness ratio (ICER) of the screening and decolonization strategy from the third-party payer and hospital perspectives.

Results

Even when MRSA colonization prevalence and decolonization success rate were as low as 1% and 25%, respectively, the ICER of implementing routine surveillance was well under $15,000 per quality-adjusted life-year from both the third-party payer and hospital perspectives. The surveillance strategy was economically dominant (less costly and more effective than no testing) for most scenarios explored.

Conclusions

Our results suggest that routine preoperative MRSA screening of cardiac surgery patients could provide substantial economic value to third-party payers and hospitals over a wide range of MRSA colonization prevalence levels, decolonization success rates, and surveillance costs. Healthcare administrators, infection control specialists, and surgeons can compare their local conditions with our study’s benchmarks to make decisions about whether to implement preoperative MRSA testing. Third-party payers may want to consider covering such a strategy.

Infection due to Staphylococcus aureus is a common complication of cardiac surgical procedures. Postoperative S aureus infections have been associated with significant morbidity, prolonged hospitalization, higher medical costs, an increased likelihood of readmission within 90 days postprocedure, and death.^1–4 S aureus is the most common cause of sternal wound infections and the leading cause of infective endocarditis in the developed world.^1,5 An increasing number of these infections are due to methicillin-resistant S aureus (MRSA), resulting in greater morbidity and mortality than infections from methicillin- sensitive S aureus (MSSA) strains.

Patients about to undergo cardiac surgery tend to have a number of factors predisposing them to postoperative MRSA infections, including older age, comorbid conditions (eg, congestive heart failure, chronic kidney disease, diabetes), and recent hospitalization.^6,7 An estimated 30% of Americans are chronic or intermittent carriers of S aureus and nasal MRSA colonization rates from 0.4% to 20.6% have been reported.^8–11 Cardiac surgery procedures often are invasive, involving prolonged use of mechanical support devices and indwelling intravenous lines, and introduce foreign materials and tissues (eg, a pacemaker, a prosthetic valve) into the body.^1,12

Preoperative screening and decolonization of cardiac surgery patients who are MRSA positive could help prevent postoperative MRSA infections and their sequelae.^13–15 Studies have suggested that nasal MRSA colonization is associated with increased rates of postsurgical MRSA infections and poor outcomes, and that decolonization prior to surgery could lower rates of MRSA surgical-site infections and bacteremia.^4,11,16,17 However, implementing routine screening and decolonization can be costly, and to date the economic value of such a strategy has not been fully ascertained. Moreover, the appropriateness of such a strategy may differ by rate of MRSA colonization prevalence, which varies geographically and temporally, as well as by individual patient risk factors.

To estimate the economic value of preoperative MRSA screening and decolonization for cardiac surgery patients, we developed a stochastic decision-analytic computer simulation model. Sensitivity analyses varied key model parameters and allowed us to delineate how the cost-effectiveness of this strategy may vary by MRSA colonization prevalence, decolonization success rate, and surveillance/decolonization cost. The results of our model may help clinicians, hospital administrators, third-party payers, and other decision makers determine when and where to use this strategy.

METHODS

Model Structure

Figure 1 depicts the general structure of our Monte Carlo decision-analytic computer simulation model. This model, constructed using TreeAge Pro 2009 (TreeAge Software, Williamstown, MA), represents the decision of whether to perform preoperative MRSA screening (by means of a single anterior nares culture) and decolonization for those patients with a positive MRSA culture. The time course of each simulation run is the perioperative period for that patient’s cardiac surgical procedure.

Figure 1.

General Decision Model Structure.

Each patient entering the model had a probability of being MRSA colonized based on the local prevalence of MRSA, but only those who were screened and tested positive underwent decolonization. All positive test results led to decolonization, regardless of true colonization status. Decolonization had a probability of success based on the chosen regimen’s efficacy. Successful decolonization eliminated a patient’s risk of MRSA infection throughout the perioperative period; unsuccessfully decolonized patients retained a probability of developing MRSA infection. Patients who developed a MRSA infection proceeded through the MRSA infection outcomes subtree (Figure 2). Patients entering this subtree had an independent probability of developing each of the following sequelae alone or in combination: mediastinitis, prosthetic valve endocarditis, septic shock, surgical-site infection, catheter- related bloodstream infection, and pneumonia. Returning to Figure 1, each individual with an invasive infection has a probability of death.

Figure 2.

MRSA Infection Outcomes Subtree

In our model the probability of an outcome was not a point estimate, but rather the multiplicative product of the probabilities of all relevant events. For example, the probability of a postoperative MRSA infection was the product of a value drawn from the MRSA colonization probability distribution (range: 0.01 to 0.20) and a value drawn from the postoperative MRSA infection probability distribution (range: 0.0 to 0.4024). Therefore, the probability of a postoperative MRSA infection ranged from the product of the minimum values that can be drawn from each probability distribution (0%) to the product of the maximum possible values drawn from each probability distribution (8.048%).

Each complication necessitates a distinct set of treatment procedures, outcomes, and costs. Surgical-site infections alone do not require additional procedures. Pneumonia requires a chest x-ray. Patients who develop mediastinitis have a probability of undergoing mediastinal exploration. Patients who develop prosthetic valve endocarditis may need a transesophageal or transthoracic echocardiogram, or require a valve replacement procedure. Septic shock necessitates central and arterial line insertion, as well as transesophageal and transthoracic echocardiograms.

MRSA Outcomes

To estimate the probability of each clinical outcome, we conducted a PubMed search using the following key terms: [cardiac], [cardiac surgery], [methicillin-resistant Staphylococcus aureus], and [MRSA]. We limited the search to English-language articles published since 1999 and reviewed all abstracts returned by the search to determine their appropriateness. Case reports, case series, and studies that did not clearly report their population denominators were excluded. We included only studies that clearly characterized study populations and reported on the full set of clinical outcomes from that population (ie, the numerators were properly defined). Parameter distributions in the model reflect the range of values identified by the literature search.

Data Inputs

The median age of simulated patients was 65 years, consistent with the population undergoing cardiac procedures.¹⁸ All cost, probability, and utility values and their respective distribution parameters are shown in Table 1. Gamma distributions were used for all cost variables. Hospitalization cost and length of stay (LOS) gamma distributions for each condition were approximated using mean and standard deviation values from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample.¹⁹ All probability values drew from beta distributions, with the exceptions of diagnostic test sensitivity and specificity for MRSA, and the probability of developing pneumonia given a MRSA infection, which drew from a triangular distribution. Using a triangular distribution is appropriate when a parameter’s distribution is asymmetric and only the lower limit, mode, and upper limit are known. Such a distribution is shaped like a triangle with the lower limit at the far left of the base, the upper limit at the far right, and the mode at the point of the triangle. Model assumptions were made based on the convention for economic analyses of MRSA and previously published literature. A 3% discount rate was used to standardize all cost and utility measures into 2009 values.

Table 1.

Data Inputs.

Variable	Type of Distribution	Mean or Median	Standard Deviation	Rangea	Source
Costs, $
Surveillance	γ	9.52	1.62	—	Expert consultation
Decolonization	γ	103.95	35.34	—	Expert consultation
Vancomycin (per day)	γ	9.01	5.00	—	39
Hospitalization
Catheter-related bloodstream infection	γ	9152.40	453.00	—	19
Mediastinitis	γ	13,993.30	6886.30	—	19
Pneumonia	γ	6033.34	184.60	—	19
Prosthetic valve endocarditis	γ	8635.70	2098.50	—	19
Septic shock	γ	11,417.41	363.89	—	19
Surgical-site infection	γ	7526.00	264.20	—	19
Procedure
Line insertion	γ	281.66	95.77	—	19
Chest x-ray	γ	25.75	8.75	—	19
Echocardiogram (transesophageal)	γ	163.72	55.66	—	19
Echocardiogram (transthoracic)	γ	164.71	56.00	—	19
Mediastinotomy exploration	γ	762.97	259.41	—	40
Valve replacement	γ	2437.18	828.64	—	40
Utilities (QALYs)
Catheter-related bloodstream infection	—	0.530	—	—	41
Mediastinitis	—	0.530	—	—	41
Pneumonia	—	0.580	—	—	20
Prosthetic valve endocarditis	—	0.530	—	—	41
Septic shock	—	0.530	—	—	41
Surgical-site infection	—	0.642	—	—	22
Probability, %
Surveillance sensitivity	Δ	92.6	—	63.0–97.0	42
Surveillance specificity	Δ	97.1	—	92.2–99.5	42
Given MRSA colonization
MRSA infection	β	16.67	23.57	—	10, 15, 43–51
Given MRSA infection
Catheter-related bloodstream infection	β	22.8	21.6	—	48, 52
Mediastinitis	β	23.36	14.10	—	12, 14
Prosthetic valve endocarditis	β	12.50	1.29	—	7, 53–54
Pneumonia	Δ	33.33	—	22.22–44.66	14
Septic shock	β	33.29	18.79	—	18, 55
Surgical-site infection	β	28.94	15.13	—	12, 14, 17, 56
Mortality rate given MRSA infection
Catheter-related bloodstream infection	β	31.50	12.80	—	52, 57–58
Pneumonia	β	31.37	15.20	—	59–67
Prosthetic valve endocarditis	β	17.91	8.86	—	68–69
Septic shock	β	48.44	2.21	—	12, 14
Length of Stay, Days
Catheter-related bloodstream infection	γ	7.5	0.2	—	19
Mediastinitis	γ	9.3	1.4	—	19
Pneumonia	γ	5.4	0.1	—	19
Prosthetic valve endocarditis	γ	14.2	0.3	—	19
Septic shock	γ	8.0	1.4	—	19
Surgical-site infection	γ	4.9	0.1	—	19

MRSA indicates methicillin-resistant Staphylococcus aureus; QALYs, quality-adjusted life-years.

^aUpper and lower limits.

Our model measured effectiveness in quality-adjusted life-years (QALYs). An otherwise healthy 65-year-old had a baseline of 0.84 QALY per year of life.^20–22 Patients who did not survive lost QALYs based on their projected life expectancy estimates from the Human Mortality Database.²³

Simulation Runs

Each simulation run consisted of 1000 hypothetical patients (each with a distinct set of characteristics) about to undergo cardiac surgery. These patients traveled through the model 1000 times each, accruing a distinct set of costs, utilities, and outcomes each time because of the probabilistic nature of the input parameters. This process generated a total of 1 million patient outcomes for each simulation run.

Economic Outcomes

For each simulation run, we estimated the incremental cost-effectiveness ratio (ICER), expressed in US dollars per QALY, of implementing the screening and decolonization strategy using the following formula:

• ICER = (Cost_{MRSA Screening ± Decolonization} – Cost_{No MRSA Screening or Decolonization}) ÷ (Effectiveness_{MRSA Screening ± Decolonization} – Effectiveness_{No MRSA Screening or Decolonization})

Although some debate exists about the ICER threshold below which an intervention is considered cost-effective, $50,000 per QALY is a commonly used cut point, and ICER values less than $20,000 per QALY offer strong economic support for the adoption of an intervention.²⁴ Strategies that are both less costly and more effective are deemed economically dominant.

Separate analyses evaluated the economic value of screening and decolonization from the following perspectives:

• Third-party payer. This perspective accounted for the direct medical costs associated with each outcome.

• Hospital. This perspective accounted for the additional hospital LOS associated with each outcome. Added LOS resulted in lost hospital bed days for the hospital.^12,25,26 A method described by Graves et al converted lost hospital bed days into costs.^27,28

Sensitivity Analyses

Sensitivity analyses varied the values of key input parameters and established the model’s robustness. Specific analyses systematically varied MRSA colonization prevalence from 1% to 40%, and decolonization success rates from 25% to 100%. To simulate the effect of testing sites in addition to the anterior nares (eg, throat, axilla, perianal swabs), the cost of testing was varied up to $200. In addition, for each simulation run, probabilistic sensitivity analyses simultaneously varied all input parameters over the ranges listed in Table 1.

RESULTS

Table 2 shows how the ICER for MRSA surveillance varied with MRSA colonization prevalence and decolonization success rate at single-location ($100) and 2-location ($200) surveillance price points from both the third-party payer and hospital perspectives. Cells labeled “surveillance” indicate scenarios in which testing dominates (ie, is less costly and more effective than) no testing. Intermediate decolonization success rate simulations (data not shown) yielded results consistent with those presented.

Table 2.

Incremental Cost-Effectiveness Ratios of Performing Surveillance and Decolonization for Different Cost, MRSA Prevalence, and Decolonization Success Rate Scenarios^a

Decolonization Success Rate	MRSA Colonization Prevalence
	1%	2.5%	5%	10%	15%
Third-Party Payer Perspective: Single-Location Surveillance ($100)
25%	5851	2677	35	Surveillance	Surveillance
50%	2007	Surveillance	Surveillance	Surveillance	Surveillance
75%	1210	Surveillance	Surveillance	Surveillance	Surveillance
100%	122	Surveillance	Surveillance	Surveillance	Surveillance
Third-Party Payer Perspective: 2-Location Surveillance ($200)
25%	12,887	3161	879	Surveillance	Surveillance
50%	4551	585	Surveillance	Surveillance	Surveillance
75%	1751	Surveillance	Surveillance	Surveillance	Surveillance
100%	1037	Surveillance	Surveillance	Surveillance	Surveillance
Hospital Perspective: Single-Location Surveillance ($100)
25%	2224	Surveillance	Surveillance	Surveillance	Surveillance
50%	Surveillance	Surveillance	Surveillance	Surveillance	Surveillance
75%	Surveillance	Surveillance	Surveillance	Surveillance	Surveillance
100%	Surveillance	Surveillance	Surveillance	Surveillance	Surveillance
Hospital Perspective: 2-Location Surveillance ($200)
25%	4411	Surveillance	Surveillance	Surveillance	Surveillance
50%	Surveillance	Surveillance	Surveillance	Surveillance	Surveillance
75%	Surveillance	Surveillance	Surveillance	Surveillance	Surveillance
100%	Surveillance	Surveillance	Surveillance	Surveillance	Surveillance

MRSA indicates methicillin-resistant Staphylococcus aureus.

^aThe incremental cost-effectiveness ratio is expressed in US dollars per quality-adjusted life-year. Cells labeled “surveillance” indicate scenarios in which testing dominated (ie, was less costly and more effective than) no testing.

Third-Party Payer Perspective

Universal MRSA surveillance was either a strongly cost-effective (ICER <$13,000 per QALY) or dominant strategy for a wide variety of decolonization success and MRSA colonization prevalence rates. For scenarios that used a single anterior nares swab ($100 cost of surveillance and decolonization), MRSA testing was the dominant strategy when MRSA colonization prevalence was ≥2.5% and decolonization was ≥50% successful, MRSA prevalence was >5% and decolonization was ≥25% successful, and for all decolonization success rates when MRSA colonization prevalence was ≥10%. Universal MRSA surveillance remained cost-effective (ie, ICER ≤$50,000 per QALY) for all other scenarios when the MRSA colonization prevalence was ≥1%. In scenarios where 2-location surveillance ($200 price point) was utilized, MRSA testing was the dominant strategy when MRSA colonization prevalence was ≥2.5% and decolonization was ≥75% successful, MRSA prevalence was ≥5% and decolonization was ≥50% successful, and when decolonization success rates were ≥25% and MRSA colonization prevalence was ≥10%. Universal MRSA surveillance was cost-effective for all other scenarios where MRSA colonization prevalence was ≥1%.

Figure 3A and Figure 3B show the acceptability curves for different MRSA colonization prevalence levels when the cost of decolonization was $200 and the probability of decolonization success was 25% (Figure 3A) and 50% (Figure 3B). These show the proportion of simulated patients for whom preoperative MRSA testing was a cost-effective intervention (compared with no testing) at different willingness-to-pay levels. The curves in Figure 3A indicate, for example, that when MRSA prevalence was 10% and the maximum willingness- to-pay was $50,000, universal surveillance was cost-effective approximately 50% of the time. As the prevalence of MRSA increased, surveillance became the optimal choice the majority of the time at lower willingness-to-pay thresh olds (~$10,000 at 20% prevalence, and ~$2500 for both 30% and 40% MRSA colonization prevalence). In Figure 3B, the rate of decolonization success increases to 50% and all of the curves shift upward.

Figure 3.

Acceptability Curves for Different MRSA Prevalence Rates When (A) Cost of Screening and Decolonization Is $200 and the Probability of Decolonization Success Is 25% and (B) Cost of Screening and Decolonization Is $200 and the Probability of Decolonization Success Is 50%

Hospital Perspective

Simulations using the opportunity cost of lost bed days showed surveillance to be economically dominant at even lower MRSA colonization prevalence and decolonization success rates. For surveillance at a single site (cost $100), when MRSA prevalence was ≥1% and decolonization success probability was ≥50%, or MRSA prevalence was ≥2.5% and decolonization success was ≥25%, surveillance dominated no surveillance. Even when colonization prevalence was <2.5% and decolonization was <50% successful, universal surveillance was still a strongly cost-effective intervention (ICER of $2224 per QALY). In scenarios using a 2-location surveillance strategy ($200 price point), testing was strongly cost-effective (ICER of $4411 per QALY) when MRSA colonization prevalence was 1% and the probability of decolonization success was 25%. Preoperative screening and decolonization was the dominant strategy when MRSA colonization prevalence was ≥2.5% and decolonization success was ≥25%, or MRSA prevalence was ≥1% and the decolonization success rate was ≥50%.

DISCUSSION

Our results suggest that routine preoperative screening of cardiac surgery patients may be a cost-effective strategy for a wide range of MRSA colonization prevalence levels, decolonization success rates, and screening/decolonization costs. In fact, this strategy could even save money for both hospitals and third-party payers. The value of MRSA testing strategy lies in its ability to prevent the substantial morbidity and mortality associated with MRSA infections in cardiac surgery patients.¹⁴ Patients undergoing major heart surgery, particularly MRSA carriers, are at risk of experiencing severe postoperative infections, including surgical-site infection, mediastinitis, catheter-related bloodstream infection, septic shock, and infective endocarditis, which in turn increase the length of hospital stays, the cost of care, and the likelihood of mortality.^17,29–31

Our study explored a wide range of decolonization success rates because the precise effectiveness of different decolonization regimens for cardiac surgery patients remains unclear. Additionally, antimicrobial resistance, as well as patient compliance, influence decolonization success. Similar sensitivity analyses were used to estimate the impact of doubling the price of screening and decolonization. This was an important consideration because culturing more than 1 body site increases cost, and because local pricing varies for medical and laboratory services as well as pharmaceuticals.

Studies have identified nasal carriage of S aureus as a major risk factor for MRSA infection after cardiac surgery and have shown that intranasal mupirocin treatment reduces sternal wound infection after cardiac surgery.^29,32 Segers et al found that a regimen consisting of perioperative naso- and oropharynx application of 0.12% chlorhexidine gluconate decreased cultures positive for S aureus by 57.5% and reduced the rate of lower respiratory tract infections and deep surgical-site infections after cardiac surgery.³³ Kluytmans et al reported that a preoperative regimen of nasal mupirocin significantly reduced the rate of surgical-site infections in patients undergoing cardiac surgery, warranting a more structured clinical trial to assess the efficacy of such a decolonization strategy in the cardiac surgical population.²⁹ van Rijen et al also found that the number of surgical-site infections attributable to S aureus can be decreased by screening and decolonizing S aureus carriers on admission to the hospital.³⁴

Although long-term decolonization is hard to achieve as a chronic or transient carrier state is common and many patients become recolonized when they return to home or to other healthcare environments, decolonization only needs to achieve short-term success to prevent perioperative infections.³⁵ Future studies may aim to clarify the short-term success rates of different decolonization regimens. However, based on our model, decolonization needs to be successful in only 25% of attempts to remain a cost-effective strategy for the prevention of perioperative MRSA infection in cardiac surgery patients.

Computer simulation models can assist—but should not replace—human decision making; models can identify and clarify key relationships and the factors that individual surgeons, surgical unit administrators, hospital infection control personnel, and policy makers should consider when determining policies and courses of action. Because our simulations demonstrated cost savings (surveillance dominating no surveillance) from the third-party payer perspective, they provide economic support for insurer-covered preoperative screening and decolonization among patients preparing to undergo cardiac surgery. Models also can raise key questions, guide the design of future epidemiologic and clinical studies, and complement retrospective and prospective clinical and epidemiologic studies. Although clinical and epidemiologic studies can provide important insights, their results may not be applicable to different settings. Computer simulation models can extend and extrapolate findings from retrospective and prospective studies, and they also can help delineate the relationships among various parameters and outcomes.

In many ways, our model may actually underestimate the economic value of MRSA screening. First, while constructing the model, we endeavored to remain conservative about the benefits of MRSA surveillance, deliberately choosing high surveillance and decolonization costs ($100 and $200), including only the most common potential MRSA complications, and using the least expensive procedures to diagnose and treat each MRSA clinical condition. Second, our model did not account for the possible transmission of MRSA from a carrier to other patients; screening and decolonization could limit the spread of MRSA in healthcare settings. Third, our model only accounted for MRSA infections and not MSSA infections. Routine screening also can be used to detect MSSA colonization. Incorporating MSSA screening and decolonization (for those who test positive) into our model would provide additional economic value beyond that for MRSA screening alone, suggesting that preoperative screening and decolonization is an even more cost-effective intervention than estimated herein. Fourth, decreasing the incidence of S aureus infections could spare patients antibiotic courses and in turn reduce the selection pressure for the development of antibiotic resistance.^36–38 Finally, additional comorbidities could make patients even more susceptible to poor MRSA infection outcomes.

Limitations

All computer simulation models are simplifications of real life and cannot fully represent the myriad complexities of real life; no model can fully represent every possible MRSA outcome. Model inputs drew from a variety of sources, including studies of varying (and often small) sample size and differing quality of design. The findings of this model may not be applicable to patients undergoing emergency cardiac surgery, as the urgency of surgery may not allow enough time for preoperative screening and decolonization.

CONCLUSIONS

Our results suggest that universal preoperative MRSA screening of cardiac surgery patients is a strongly cost-effective or cost-saving intervention from both third-party payer and hospital perspectives over a wide range of MRSA colonization prevalence, decolonization success rates, and screening/ decolonization costs. Clinical practitioners and hospital administrators stand to benefit from a decrease in healthcare-associated MRSA infections, improved patient outcomes, and the release of valuable monetary, personnel, and physical resources for alternative use. These stakeholders can compare their local conditions with our study’s benchmarks and make decisions about whether to implement preoperative MRSA testing. Third-party payers may want to consider covering such a strategy, as structuring reimbursements to provide coverage for preoperative MRSA screening and decolonization could generate cost savings.

Take-Away Points.

Routine preoperative methicillin-resistant Staphylococcus aureus (MRSA) screening of cardiac surgery patients could provide substantial economic value to third-party payers and hospitals over a wide range of MRSA colonization prevalence levels (≥1%), decolonization success rates (≥25%), and decolonization costs (up to $200 per patient)

• ▪Healthcare administrators, infection control specialists, and surgeons can compare their local conditions with our study’s benchmarks to make decisions about whether to implement preoperative MRSA testing.
• ▪Third-party payers may want to consider covering such a strategy.